TY - GEN A1 - Ambellan, Felix A1 - Lamecker, Hans A1 - von Tycowicz, Christoph A1 - Zachow, Stefan T1 - Statistical Shape Models - Understanding and Mastering Variation in Anatomy N2 - In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression. Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated. We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) statistical shape models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods ormachine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring. T3 - ZIB-Report - 19-13 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-72699 SN - 1438-0064 ER - TY - GEN A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Ehlke, Moritz A1 - Zachow, Stefan T1 - Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative N2 - We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs).The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures.The shape models and neural networks employed are trained using data from the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets from the SKI10 challenge.For the first time, an accuracy equivalent to the inter-observer variability of human readers is achieved in this challenge.Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We make the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation.In conclusion, combining localized classification via CNNs with statistical anatomical knowledge via SSMs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data. T3 - ZIB-Report - 19-06 Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-72704 SN - 1438-0064 N1 - Innovation Excellence Award 2020 ER - TY - CHAP A1 - Ambellan, Felix A1 - Lamecker, Hans A1 - von Tycowicz, Christoph A1 - Zachow, Stefan ED - Rea, Paul M. T1 - Statistical Shape Models - Understanding and Mastering Variation in Anatomy T2 - Biomedical Visualisation N2 - In our chapter we are describing how to reconstruct three-dimensional anatomy from medical image data and how to build Statistical 3D Shape Models out of many such reconstructions yielding a new kind of anatomy that not only allows quantitative analysis of anatomical variation but also a visual exploration and educational visualization. Future digital anatomy atlases will not only show a static (average) anatomy but also its normal or pathological variation in three or even four dimensions, hence, illustrating growth and/or disease progression. Statistical Shape Models (SSMs) are geometric models that describe a collection of semantically similar objects in a very compact way. SSMs represent an average shape of many three-dimensional objects as well as their variation in shape. The creation of SSMs requires a correspondence mapping, which can be achieved e.g. by parameterization with a respective sampling. If a corresponding parameterization over all shapes can be established, variation between individual shape characteristics can be mathematically investigated. We will explain what Statistical Shape Models are and how they are constructed. Extensions of Statistical Shape Models will be motivated for articulated coupled structures. In addition to shape also the appearance of objects will be integrated into the concept. Appearance is a visual feature independent of shape that depends on observers or imaging techniques. Typical appearances are for instance the color and intensity of a visual surface of an object under particular lighting conditions, or measurements of material properties with computed tomography (CT) or magnetic resonance imaging (MRI). A combination of (articulated) statistical shape models with statistical models of appearance lead to articulated Statistical Shape and Appearance Models (a-SSAMs).After giving various examples of SSMs for human organs, skeletal structures, faces, and bodies, we will shortly describe clinical applications where such models have been successfully employed. Statistical Shape Models are the foundation for the analysis of anatomical cohort data, where characteristic shapes are correlated to demographic or epidemiologic data. SSMs consisting of several thousands of objects offer, in combination with statistical methods ormachine learning techniques, the possibility to identify characteristic clusters, thus being the foundation for advanced diagnostic disease scoring. Y1 - 2019 SN - 978-3-030-19384-3 SN - 978-3-030-19385-0 U6 - https://doi.org/10.1007/978-3-030-19385-0_5 VL - 3 IS - 1156 SP - 67 EP - 84 PB - Springer Nature Switzerland AG ET - 1 ER - TY - JOUR A1 - Hildebrandt, Thomas A1 - Bruening, Jan Joris A1 - Schmidt, Nora Laura A1 - Lamecker, Hans A1 - Heppt, Werner A1 - Zachow, Stefan A1 - Goubergrits, Leonid T1 - The Healthy Nasal Cavity - Characteristics of Morphology and Related Airflow Based on a Statistical Shape Model Viewed from a Surgeon’s Perspective JF - Facial Plastic Surgery N2 - Functional surgery on the nasal framework requires referential criteria to objectively assess nasal breathing for indication and follow-up. Thismotivated us to generate amean geometry of the nasal cavity based on a statistical shape model. In this study, the authors could demonstrate that the introduced nasal cavity’s mean geometry features characteristics of the inner shape and airflow, which are commonly observed in symptom-free subjects. Therefore, the mean geometry might serve as a reference-like model when one considers qualitative aspects. However, to facilitate quantitative considerations and statistical inference, further research is necessary. Additionally, the authorswere able to obtain details about the importance of the isthmus nasi and the inferior turbinate for the intranasal airstream. KW - statistical shape model KW - nasal cavity KW - nasal breathing KW - nasal airflow KW - isthmus nasi KW - inferior turbinate Y1 - 2019 U6 - https://doi.org/10.1055/s-0039-1677721 VL - 35 IS - 1 SP - 9 EP - 13 ER - TY - JOUR A1 - Hildebrandt, Thomas A1 - Bruening, Jan Joris A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Heppt, Werner A1 - Schmidt, Nora A1 - Goubergrits, Leonid T1 - Digital Analysis of Nasal Airflow Facilitating Decision Support in Rhinosurgery JF - Facial Plastic Surgery N2 - Successful functional surgery on the nasal framework requires reliable and comprehensive diagnosis. In this regard, the authors introduce a new methodology: Digital Analysis of Nasal Airflow (diANA). It is based on computational fluid dynamics, a statistical shape model of the healthy nasal cavity and rhinologic expertise. diANA necessitates an anonymized tomographic dataset of the paranasal sinuses including the complete nasal cavity and, when available, clinical information. The principle of diANA is to compare the morphology and the respective airflow of an individual nose with those of a reference. This enablesmorphometric aberrations and consecutive flow field anomalies to localize and quantify within a patient’s nasal cavity. Finally, an elaborated expert opinion with instructive visualizations is provided. Using diANA might support surgeons in decision-making, avoiding unnecessary surgery, gaining more precision, and target-orientation for indicated operations. KW - nasal airflow simulation KW - nasal breathing KW - statistical shape model KW - diANA KW - nasal obstruction KW - rhinorespiratory homeostasis Y1 - 2019 U6 - https://doi.org/10.1055/s-0039-1677720 VL - 35 IS - 1 SP - 1 EP - 8 ER - TY - THES A1 - Prendke, Mona T1 - Comparison of 2D and 3D CNNs for Classification of Knee MRI Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-72439 ER - TY - THES A1 - Dill, Sabrina Patricia T1 - Joint Feature Learning and Classification - Deep Learning for Surgical Phase Detection Y1 - 2018 ER - TY - CHAP A1 - Tack, Alexander A1 - Zachow, Stefan T1 - Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative T2 - IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) N2 - Volumetry of cartilage of the knee is needed for knee osteoarthritis (KOA) assessment. It is typically performed manually in a tedious and subjective process. We developed a method for an automated, segmentation-based quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data and cartilage volumetry readings performed by clinical experts for 1378 subjects provided by the Osteoarthritis Initiative. It was shown that 3D CNNs are able to achieve volume measures comparable to the magnitude of variation between expert readings and the real in vivo situation. In the future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as longitudinal analysis of KOA progression. Y1 - 2019 U6 - https://doi.org/10.1109/ISBI.2019.8759201 SP - 40 EP - 43 ER - TY - THES A1 - Gidey, Henok Hagos T1 - Automated Hip Knee Ankle Angle Determination using Convolutional Neural Networks N2 - Advanced osteoarthritis is a leading cause of knee replacement and loss of functionality. Early detection of risk factors plays an important role in the application of preventive measures. One of the risk factors is the leg alignment which influences the speed of knee cartilage degradation. The ’gold standard’ measurement of leg alignment is done by determining the Hip Knee Ankle (HKA) angle from full lower limb radiographs. Convolutional Neural Networks (CNNs) have gained popularity recently in computer vision. In this thesis we developed methods using CNNs to determine HKA angles from full lower limb radiographs. We trained the CNNs using data from the Osteoarthritis Initiative (OAI). We evaluated our method’s performance by evaluating its agreement to experts measurement and its reliability. Our best performing method shows excellent agreement and reliability levels. Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-71263 ER - TY - GEN A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Ehlke, Moritz A1 - Zachow, Stefan T1 - Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material) T2 - Medical Image Analysis N2 - We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data of the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets of the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We made the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining statistical anatomical knowledge via SSMs with the localized classification via CNNs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data. Y1 - 2019 U6 - https://doi.org/10.12752/4.ATEZ.1.0 N1 - OAI-ZIB dataset VL - 52 IS - 2 SP - 109 EP - 118 ER - TY - JOUR A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Ehlke, Moritz A1 - Zachow, Stefan T1 - Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative JF - Medical Image Analysis N2 - We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data of the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. We evaluate our method on 40 validation and 50 submission datasets of the SKI10 challenge. For the first time, an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for data from the OAI, i.e. 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy for both OAI datasets. We made the 507 manual segmentations as well as our experimental setup publicly available to further aid research in the field of medical image segmentation. In conclusion, combining statistical anatomical knowledge via SSMs with the localized classification via CNNs results in a state-of-the-art segmentation method for knee bones and cartilage from MRI data. Y1 - 2019 U6 - https://doi.org/10.1016/j.media.2018.11.009 VL - 52 IS - 2 SP - 109 EP - 118 ER - TY - JOUR A1 - Al Hajj, Hassan A1 - Sahu, Manish A1 - Lamard, Mathieu A1 - Conze, Pierre-Henri A1 - Roychowdhury, Soumali A1 - Hu, Xiaowei A1 - Marsalkaite, Gabija A1 - Zisimopoulos, Odysseas A1 - Dedmari, Muneer Ahmad A1 - Zhao, Fenqiang A1 - Prellberg, Jonas A1 - Galdran, Adrian A1 - Araujo, Teresa A1 - Vo, Duc My A1 - Panda, Chandan A1 - Dahiya, Navdeep A1 - Kondo, Satoshi A1 - Bian, Zhengbing A1 - Bialopetravicius, Jonas A1 - Qiu, Chenghui A1 - Dill, Sabrina A1 - Mukhopadyay, Anirban A1 - Costa, Pedro A1 - Aresta, Guilherme A1 - Ramamurthy, Senthil A1 - Lee, Sang-Woong A1 - Campilho, Aurelio A1 - Zachow, Stefan A1 - Xia, Shunren A1 - Conjeti, Sailesh A1 - Armaitis, Jogundas A1 - Heng, Pheng-Ann A1 - Vahdat, Arash A1 - Cochener, Beatrice A1 - Quellec, Gwenole T1 - CATARACTS: Challenge on Automatic Tool Annotation for cataRACT Surgery JF - Medical Image Analysis N2 - Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future. Y1 - 2019 U6 - https://doi.org/10.1016/j.media.2018.11.008 N1 - Best paper award - Computer Graphics Night 2020 (TU Darmstadt) VL - 52 IS - 2 SP - 24 EP - 41 PB - Elsevier ER - TY - GEN A1 - Tack, Alexander A1 - Zachow, Stefan T1 - Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative N2 - Volumetry of the cartilage of the knee, as needed for the assessment of knee osteoarthritis (KOA), is typically performed in a tedious and subjective process. We present an automated segmentation-based method for the quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data as well as cartilage volumetry readings given by clinical experts for 1378 subjects. It was shown that 3D CNNs can be employed for cartilage volumetry with an accuracy similar to expert volumetry readings. In future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as assessment of KOA progression via longitudinal analysis. T3 - ZIB-Report - 19-05 KW - Deep Learning KW - imaging biomarker KW - radiomics KW - cartilage morphometry KW - volume assessment Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-71439 SN - 1438-0064 ER - TY - JOUR A1 - Hoffmann, Rene A1 - Lemanis, Robert A1 - Wulff, Lena A1 - Zachow, Stefan A1 - Lukeneder, Alexander A1 - Klug, Christian A1 - Keupp, Helmut T1 - Traumatic events in the life of the deep-sea cephalopod mollusc, the coleoid Spirula spirula JF - ScienceDirect: Deep Sea Research Part I - Oceanographic Research N2 - Here, we report on different types of shell pathologies of the enigmatic deep-sea (mesopelagic) cephalopod Spirula spirula. For the first time, we apply non-invasive imaging methods to: document trauma-induced changes in shell shapes, reconstruct the different causes and effects of these pathologies, unravel the etiology, and attempt to quantify the efficiency of the buoyancy apparatus. We have analysed 2D and 3D shell parameters from eleven shells collected as beach findings from the Canary Islands (Gran Canaria and Fuerteventura), West-Australia, and the Maldives. All shells were scanned with a nanotom-m computer tomograph. Seven shells were likely injured by predator attacks: fishes, cephalopods or crustaceans, one specimen was infested by an endoparasite (potentially Digenea) and one shell shows signs of inflammation and one shell shows large fluctuations of chamber volumes without any signs of pathology. These fluctuations are potential indicators of a stressed environment. Pathological shells represent the most deviant morphologies of a single species and can therefore be regarded as morphological end-members. The changes in the shell volume / chamber volume ratio were assessed in order to evaluate the functional tolerance of the buoyancy apparatus showing that these had little effect. Y1 - 2018 U6 - https://doi.org/10.1016/j.dsr.2018.10.007 VL - 142 IS - 12 SP - 127 EP - 144 ER - TY - GEN A1 - Tycowicz, Christoph von A1 - Ambellan, Felix A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - A Riemannian Statistical Shape Model using Differential Coordinates N2 - We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidian structure. A key advantage of our framework is that statistics in a manifold shape space become numerically tractable improving performance by several orders of magnitude over state-of-the-art. We show that our Riemannian model is well suited for the identification of intra-population variability as well as inter-population differences. In particular, we demonstrate the superiority of the proposed model in experiments on specificity and generalization ability. We further derive a statistical shape descriptor that outperforms the standard Euclidian approach in terms of shape-based classification of morphological disorders. T3 - ZIB-Report - 16-69 Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-61175 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/6485 SN - 1438-0064 ER - TY - JOUR A1 - Lemanis, Robert A1 - Zachow, Stefan A1 - Hoffmann, René T1 - Comparative cephalopod shell strength and the role of septum morphology on stress distribution JF - PeerJ N2 - The evolution of complexly folded septa in ammonoids has long been a controversial topic. Explanations of the function of these folded septa can be divided into physiological and mechanical hypotheses with the mechanical functions tending to find widespread support. The complexity of the cephalopod shell has made it difficult to directly test the mechanical properties of these structures without oversimplification of the septal morphology or extraction of a small sub-domain. However, the power of modern finite element analysis now permits direct testing of mechanical hypothesis on complete, empirical models of the shells taken from computed tomographic data. Here we compare, for the first time using empirical models, the capability of the shells of extant Nautilus pompilius, Spirula spirula, and the extinct ammonite Cadoceras sp. to withstand hydrostatic pressure and point loads. Results show hydrostatic pressure imparts highest stress on the final septum with the rest of the shell showing minimal compression. S. spirula shows the lowest stress under hydrostatic pressure while N. pompilius shows the highest stress. Cadoceras sp. shows the development of high stress along the attachment of the septal saddles with the shell wall. Stress due to point loads decreases when the point force is directed along the suture as opposed to the unsupported chamber wall. Cadoceras sp. shows the greatest decrease in stress between the point loads compared to all other models. Greater amplitude of septal flutes corresponds with greater stress due to hydrostatic pressure; however, greater amplitude decreases the stress magnitude of point loads directed along the suture. In our models, sutural complexity does not predict greater resistance to hydrostatic pressure but it does seem to increase resistance to point loads, such as would be from predators. This result permits discussion of palaeoecological reconstructions on the basis of septal morphology. We further suggest that the ratio used to characterize septal morphology in the septal strength index and in calculations of tensile strength of nacre are likely insufficient. A better understanding of the material properties of cephalopod nacre may allow the estimation of maximum depth limits of shelled cephalopods through finite element analysis. Y1 - 2016 U6 - https://doi.org/10.7717/peerj.2434 VL - 4 SP - e2434 ER - TY - JOUR A1 - Bernard, Florian A1 - Salamanca, Luis A1 - Thunberg, Johan A1 - Tack, Alexander A1 - Jentsch, Dennis A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hertel, Frank A1 - Goncalves, Jorge A1 - Gemmar, Peter T1 - Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds JF - Medical Image Analysis N2 - The reconstruction of an object’s shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed. To this end, we propose the use of a statistical shape model (SSM) as a prior for surface reconstruction. The SSM is represented by a point distribution model (PDM), which is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we formulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given points are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are “oriented” according to the surface normals at the PDM points, a surface-based fitting is accomplished. Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points. We compare our method to the extensively used Iterative Closest Points method on several different anatomical datasets/SSMs (brain, femur, tibia, hip, liver) and demonstrate superior accuracy and robustness on sparse data. Y1 - 2017 UR - http://www.sciencedirect.com/science/article/pii/S1361841517300233 U6 - https://doi.org/10.1016/j.media.2017.02.005 VL - 38 SP - 77 EP - 89 ER - TY - CHAP A1 - Zachow, Stefan A1 - Weiser, Martin A1 - Deuflhard, Peter ED - Niederlag, Wolfgang ED - Lemke, Heinz ED - Meixensberger, Jürgen ED - Baumann, Michael T1 - Modellgestützte Operationsplanung in der Kopfchirurgie T2 - Modellgestützte Therapie Y1 - 2008 SP - 140 EP - 156 PB - Health Academy ER - TY - JOUR A1 - Zeilhofer, Hans-Florian A1 - Zachow, Stefan A1 - Fairley, Jeffrey A1 - Sader, Robert A1 - Deuflhard, Peter T1 - Treatment Planning and Simulation in Craniofacial Surgery with Virtual Reality Techiques JF - Journal of Cranio-Maxillofacial Surgery Y1 - 2000 VL - 28 (Suppl. 1) SP - 82 ER - TY - JOUR A1 - Lamas-Rodríguez, Julián A1 - Heras, Dora Blanco A1 - Argüello, Francisco A1 - Kainmüller, Dagmar A1 - Zachow, Stefan A1 - Bóo, Montserrat T1 - GPU-accelerated level-set segmentation JF - Journal of Real-Time Image Processing Y1 - 2013 UR - http://dx.doi.org/10.1007/s11554-013-0378-6 U6 - https://doi.org/10.1007/s11554-013-0378-6 SN - 1861-8200 SP - 1 EP - 15 PB - Springer Berlin Heidelberg ER - TY - JOUR A1 - Hoffmann, René A1 - Schultz, Julia A. A1 - Schellhorn, Rico A1 - Rybacki, Erik A1 - Keupp, Helmut A1 - Gerden, S. R. A1 - Lemanis, Robert A1 - Zachow, Stefan T1 - Non-invasive imaging methods applied to neo- and paleontological cephalopod research JF - Biogeosciences N2 - Several non-invasive methods are common practice in natural sciences today. Here we present how they can be applied and contribute to current topics in cephalopod (paleo-) biology. Different methods will be compared in terms of time necessary to acquire the data, amount of data, accuracy/resolution, minimum/maximum size of objects that can be studied, the degree of post-processing needed and availability. The main application of the methods is seen in morphometry and volumetry of cephalopod shells. In particular we present a method for precise buoyancy calculation. Therefore, cephalopod shells were scanned together with different reference bodies, an approach developed in medical sciences. It is necessary to know the volume of the reference bodies, which should have similar absorption properties like the object of interest. Exact volumes can be obtained from surface scanning. Depending on the dimensions of the study object different computed tomography techniques were applied. Y1 - 2014 U6 - https://doi.org/10.5194/bg-11-2721-2014 N1 - To access the corresponding discussion paper go to www.biogeosciences-discuss.net/10/18803/2013/ - Biogeosciences Discuss., 10, 18803-18851, 2013 VL - 11 IS - 10 SP - 2721 EP - 2739 ER - TY - THES A1 - Kainmüller, Dagmar T1 - Deformable Meshes for Accurate Automatic Segmentation of Medical Image Data Y1 - 2013 ER - TY - THES A1 - Bindernagel, Matthias T1 - Articulated Statistical Shape Models Y1 - 2013 ER - TY - THES A1 - Renard, Maximilien T1 - Improvement of Image Segmentation Based on Statistical Shape and Intensity Models Y1 - 2011 ER - TY - THES A1 - Jalda, Dworzak T1 - Reconstruction of the Human Rib Cage from 2D Projection Images using a Statistical Shape Model Y1 - 2009 ER - TY - CHAP A1 - Ramm, Heiko A1 - Victoria Morillo, Oscar Salvador A1 - Todt, Ingo A1 - Schirmacher, Hartmut A1 - Ernst, Arneborg A1 - Zachow, Stefan A1 - Lamecker, Hans ED - Freysinger, Wolfgang T1 - Visual Support for Positioning Hearing Implants T2 - Proceedings of the 12th annual meeting of the CURAC society Y1 - 2013 SP - 116 EP - 120 ER - TY - JOUR A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Heller, Markus O. A1 - Weber, Britta A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - Omnidirectional Displacements for Deformable Surfaces JF - Medical Image Analysis Y1 - 2013 U6 - https://doi.org/10.1016/j.media.2012.11.006 VL - 17 IS - 4 SP - 429 EP - 441 PB - Elsevier ER - TY - JOUR A1 - Dunlop, Jason A1 - Apanaskevich, Dmitry A1 - Lehmann, Jens A1 - Hoffmann, Rene A1 - Fusseis, Florian A1 - Ehlke, Moritz A1 - Zachow, Stefan A1 - Xiao, Xianghui T1 - Microtomography of the Baltic amber tick Ixodes succineus reveals affinities with the modern Asian disease vector Ixodes ovatus JF - BMC Evolutionary Biology N2 - Background: Fossil ticks are extremely rare, whereby Ixodes succineus Weidner, 1964 from Eocene (ca. 44-49 Ma) Baltic amber is one of the oldest examples of a living hard tick genus (Ixodida: Ixodidae). Previous work suggested it was most closely related to the modern and widespread European sheep tick Ixodes ricinus (Linneaus, 1758). Results: Restudy using phase contrast synchrotron x-ray tomography yielded images of exceptional quality. These confirm the fossil's referral to Ixodes Latreille, 1795, but the characters resolved here suggest instead affinities with the Asian subgenus Partipalpiger Hoogstraal et al., 1973 and its single living (and medically significant) species Ixodes ovatus Neumann, 1899. We redescribe the amber fossil here as Ixodes (Partipalpiger) succineus. Conclusions: Our data suggest that Ixodes ricinus is unlikely to be directly derived from Weidner's amber species, but instead reveals that the Partipalpiger lineage was originally more widely distributed across the northern hemisphere. The closeness of Ixodes (P.) succineus to a living vector of a wide range of pathogens offers the potential to correlate its spatial and temporal position (northern Europe, nearly 50 million years ago) with the estimated origination dates of various tick-borne diseases. Y1 - 2016 U6 - https://doi.org/10.1186/s12862-016-0777-y VL - 16 IS - 1 ER - TY - JOUR A1 - Schenkl, Sebastian A1 - Muggenthaler, Holger A1 - Hubig, Michael A1 - Erdmann, Bodo A1 - Weiser, Martin A1 - Zachow, Stefan A1 - Heinrich, Andreas A1 - Güttler, Felix Victor A1 - Teichgräber, Ulf A1 - Mall, Gita T1 - Automatic CT-based finite element model generation for temperature-based death time estimation: feasibility study and sensitivity analysis JF - International Journal of Legal Medicine N2 - Temperature based death time estimation is based either on simple phenomenological models of corpse cooling or on detailed physical heat transfer models. The latter are much more complex, but allow a higher accuracy of death time estimation as in principle all relevant cooling mechanisms can be taken into account. Here, a complete work flow for finite element based cooling simulation models is presented. The following steps are demonstrated on CT-phantoms: • CT-scan • Segmentation of the CT images for thermodynamically relevant features of individual geometries • Conversion of the segmentation result into a Finite Element (FE) simulation model • Computation of the model cooling curve • Calculation of the cooling time For the first time in FE-based cooling time estimation the steps from the CT image over segmentation to FE model generation are semi-automatically performed. The cooling time calculation results are compared to cooling measurements performed on the phantoms under controlled conditions. In this context, the method is validated using different CTphantoms. Some of the CT phantoms thermodynamic material parameters had to be experimentally determined via independent experiments. Moreover the impact of geometry and material parameter uncertainties on the estimated cooling time is investigated by a sensitivity analysis. KW - temperature based death time estimation KW - finite element method KW - CT segmentation KW - sensitivity analysis Y1 - 2017 U6 - https://doi.org/doi:10.1007/s00414-016-1523-0 VL - 131 IS - 3 SP - 699 EP - 712 ER - TY - THES A1 - Tack, Alexander T1 - Gruppenweise Registrierung zur robusten Bewegungsfeldschätzung in artefaktbehafteten 4D-CT-Bilddaten N2 - Das Ziel der Strahlentherapie ist, eine möglichst hohe Dosis in den Tumor zu applizieren und zeitgleich die Strahlenexposition des Normalgewebes zu minimieren. Insbesondere bei thorakalen und abdominalen Tumoren treten aufgrund der Atmung während der Bestrahlung große, komplexe und patientenspezifisch unterschiedliche Bewegungen der Gewebe auf. Um den Einfluss dieser Bewegung auf die i.d.R. statisch geplante Dosisverteilung abzuschätzen, können unter Verwendung der nicht-linearen Bildregistrierung anhand von 3D-CT-Aufnahmen eines Atmungszyklus - also 4D-CT-Daten - zunächst die Bewegungsfelder für die strahlentherapeutisch relevanten Strukturen, beispielsweise für die Lunge, berechnet werden. Diese Informationen bilden die Grundlage für sogenannte 4D-Dosisberechnungs- oder Dosisakkumulationsverfahren. Deren Genauigkeit hängt aber wesentlich von der Genauigkeit der Bewegungsfeldschätzung ab. Klassisch erfolgt die Berechnung der Bewegungsfelder mittels paarweiser Bildregistrierung, womit für die Berechnung des Bewegungsfeldes zwischen zwei Bildern im Allgemeinen eine sehr hohe Genauigkeit erreicht wird. Auch für CT-Bilder, die Bewegungsartefakte, wie beispielsweise doppelte oder unvollständige Strukturen, enthalten, wird unter Verwendung der paarweisen Bildregistrierung im Kontext der Registrierung eine exakte Abbildung der anatomischen Strukturen zwischen den beiden Bildern erreicht. Dabei erfolgt aber eine physiologisch unplausible Anpassung der Felder an die Artefakte. Bei Verwendung der paarweisen Bildregistrierung müssen weiterhin für einen Atemzyklus die Voxel-Trajektorien aus Bewegungsfeldern zwischen mehreren dreidimensionalen Bildern zusammengesetzt werden. Durch Bewegungsartefakte entsprechen diese Trajektorien dann teilweise keiner natürlichen Bewegung der anatomischen Strukturen. Diese Ungenauigkeit stellt in der klinischen Anwendung ein Problem dar; dies gilt umso mehr, wenn Bewegungsartefakte im Bereich eines Tumors vorliegen. Im Gegensatz zu der paarweisen Registrierung kann mit der gruppenweisen Registrierung das Problem der durch Bewegungsartefakte hervorgerufenen ungenauen Abbildung der physiologischen Gegebenheiten dadurch reduziert werden, dass im Registrierungsprozess Bildinformationen aller Bilder, also in diesem Kontext der CT-Daten zu unterschiedlichen Atemphasen, gleichzeitig genutzt werden. Es kann bereits im Registrierungsprozess eine zeitliche Glattheit der Voxel-Trajektorien gefordert werden. In dieser Arbeit wird eine Methode zur B-Spline-basierten zeitlich regularisierten gruppenweisen Registrierung entwickelt. Die Genauigkeit der entwickelten Methode wird für frei zugängliche klinische Datensätze landmarkenbasiert evaluiert. Dabei wird mit dem Target Registration Error (TRE) die durchschnittliche dreidimensionale euklidische Distanz zwischen den korrespondierenden Landmarken nach Transformation der Landmarken bezeichnet. Eine Genauigkeit in der Größenordnung von aktuellen paarweisen Registrierungen verdeutlicht die Qualität des vorgestellten Registrierungs-Algorithmus. Anschließend werden die Vorteile der gruppenweisen Registrierung durch Experimente an einem Lungenphantom und an manipulierten, artefaktbehafteten klinischen 4D-CT-Bilddaten demonstriert. Dabei werden unter Verwendung der gruppenweisen Registrierung im Vergleich zu der paarweisen Registrierung glattere Trajektorien berechnet, die der realen Bewegung der anatomischen Strukturen stärker entsprechen. Für die Patientendaten wird außerdem anhand von automatisch detektierten Landmarken der TRE ausgewertet. Der TRE verschlechterte sich für die paarweise Bildregistrierung unter Vorliegen von Bewegungsartefakten von durchschnittlich 1,30 mm auf 3,94 mm. Auch hier zeigte sich für die gruppenweise Registrierung die Robustheit gegenüber Bewegungsartefakten und der TRE verschlechterte sich nur geringfügig von 1,45 mm auf 1,71 mm. KW - Gruppenweise Registrierung KW - 4D-CT Y1 - 2015 ER - TY - GEN A1 - Nava-Yazdani, Esfandiar A1 - Hege, Hans-Christian A1 - von Tycowicz, Christoph A1 - Sullivan, T. J. T1 - A Shape Trajectories Approach to Longitudinal Statistical Analysis N2 - For Kendall’s shape space we determine analytically Jacobi fields and parallel transport, and compute geodesic regression. Using the derived expressions, we can fully leverage the geometry via Riemannian optimization and reduce the computational expense by several orders of magnitude. The methodology is demonstrated by performing a longitudinal statistical analysis of epidemiological shape data. As application example we have chosen 3D shapes of knee bones, reconstructed from image data of the Osteoarthritis Initiative. Comparing subject groups with incident and developing osteoarthritis versus normal controls, we find clear differences in the temporal development of femur shapes. This paves the way for early prediction of incident knee osteoarthritis, using geometry data only. T3 - ZIB-Report - 18-42 KW - shape space, shape trajectories, geodesic regression, longitudinal analysis, osteoarthritis Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-69759 SN - 1438-0064 ER - TY - CHAP A1 - Joachimsky, Robert A1 - Ambellan, Felix A1 - Zachow, Stefan T1 - Computerassistierte Auswahl und Platzierung von interpositionalen Spacern zur Behandlung früher Gonarthrose T2 - Proceedings of the Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC) N2 - Degenerative Gelenkerkrankungen, wie die Osteoarthrose, sind ein häufiges Krankheitsbild unter älteren Erwachsenen. Hierbei verringert sich u.a. der Gelenkspalt aufgrund degenerierten Knorpels oder geschädigter Menisci. Ein in den Gelenkspalt eingebrachter interpositionaler Spacer soll die mit der Osteoarthrose einhergehende verringerte Gelenkkontaktfläche erhöhen und so der teilweise oder vollständige Gelenkersatz hinausgezögert oder vermieden werden. In dieser Arbeit präsentieren wir eine Planungssoftware für die Auswahl und Positionierung eines interpositionalen Spacers am Patientenmodell. Auf einer MRT-basierten Bildsegmentierung aufbauend erfolgt eine geometrische Rekonstruktion der 3D-Anatomie des Kniegelenks. Anhand dieser wird der Gelenkspalt bestimmt, sowie ein Spacer ausgewählt und algorithmisch vorpositioniert. Die Positionierung des Spacers ist durch den Benutzer jederzeit interaktiv anpassbar. Für jede Positionierung eines Spacers wird ein Fitness-Wert zur Knieanatomie des jeweiligen Patienten berechnet und den Nutzern Rückmeldung hinsichtlich Passgenauigkeit gegeben. Die Software unterstützt somit als Entscheidungshilfe die behandelnden Ärzte bei der patientenspezifischen Spacerauswahl. KW - Osteoarthrose KW - Kniegelenk KW - Medizinische Planung KW - Visualisierung Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-65321 VL - 16 SP - 106 EP - 111 ER - TY - CHAP A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Wilson, Dave A1 - Anglin, Carolyn A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data T2 - Proceedings of the Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie (CURAC) N2 - In this study we investigate methods for fitting a Statistical Shape Model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system. We validate the fitted models against the pre-operatively acquired Magnetic Resonance Imaging (MRI) data from the same patients. We consider a cohort of 10 patients who underwent navigated total knee arthroplasty. As part of the surgical protocol the patients’ distal femurs were partially digitized. All patients had an MRI scan two months pre-operatively. The MRI data were manually segmented and the reconstructed bone surfaces used as ground truth against which the fit was compared. Two methods were used to fit the SSM to the data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM). For both approaches, the difference between model fit and ground truth surface averaged less than 1.7 mm and excellent correspondence with the distal femoral morphology can be demonstrated. KW - Total Knee Arthoplasty KW - Sparse Geometry Reconstruction KW - Statistical Shape Models Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-65339 VL - 16 SP - 24 EP - 30 ER - TY - JOUR A1 - Akbari Shandiz, Mohsen A1 - Boulos, Paul A1 - Sævarsson, Stefan A1 - Ramm, Heiko A1 - Fu, Chun Kit A1 - Miller, Stephen A1 - Zachow, Stefan A1 - Anglin, Carolyn T1 - Changes in Knee Shape and Geometry Resulting from Total Knee Arthroplasty JF - Journal of Engineering in Medicine N2 - Changes in knee shape and geometry resulting from total knee arthroplasty can affect patients in numerous important ways: pain, function, stability, range of motion, and kinematics. Quantitative data concerning these changes have not been previously available, to our knowledge, yet are essential to understand individual experiences of total knee arthroplasty and thereby improve outcomes for all patients. The limiting factor has been the challenge of accurately measuring these changes. Our study objective was to develop a conceptual framework and analysis method to investigate changes in knee shape and geometry, and prospectively apply it to a sample total knee arthroplasty population. Using clinically available computed tomography and radiography imaging systems, the three-dimensional knee shape and geometry of nine patients (eight varus and one valgus) were compared before and after total knee arthroplasty. All patients had largely good outcomes after their total knee arthroplasty. Knee shape changed both visually and numerically. On average, the distal condyles were slightly higher medially and lower laterally (range: +4.5 mm to −4.4 mm), the posterior condyles extended farther out medially but not laterally (range: +1.8 to −6.4 mm), patellofemoral distance increased throughout flexion by 1.8–3.5 mm, and patellar thickness alone increased by 2.9 mm (range: 0.7–5.2 mm). External femoral rotation differed preop and postop. Joint line distance, taking cartilage into account, changed by +0.7 to −1.5 mm on average throughout flexion. Important differences in shape and geometry were seen between pre-total knee arthroplasty and post-total knee arthroplasty knees. While this is qualitatively known, this is the first study to report it quantitatively, an important precursor to identifying the reasons for the poor outcome of some patients. Using the developed protocol and visualization techniques to compare patients with good versus poor clinical outcomes could lead to changes in implant design, implant selection, component positioning, and surgical technique. Recommendations based on this sample population are provided. Intraoperative and postoperative feedback could ultimately improve patient satisfaction. Y1 - 2018 UR - http://journals.sagepub.com/eprint/ZVgNrNESA9EjIcaFWSjb/full U6 - https://doi.org/10.1177/0954411917743274 VL - 232 IS - 1 SP - 67 EP - 79 ER - TY - THES A1 - Reddy, Gutha Vaishnavi T1 - Automatic Classification of 3D MRI data using Deep Convolutional Neural Networks N2 - The chronic disease of Osteoarthritis of the knee that causes pain and discomfort in the knee is associated with the degradation of the joint between the tibia and the femur. The degeneration of this joint is attributed partially to the damage of the meniscus of the knee which forms an important part of the knee joint. Magnetic Resonance Imaging (MRI) is used to diagnose such a kind of osteoarthritis by identifying the degeneration of the knee meniscus. A computer aided diagnostic system that aims to assist a doctor in decision making regarding such a diagnosis can expedite the very diagnosis. Diagnostic decision making for medical imaging falls into the category of classification for a computer vision task. Very Deep Convolutional Networks have been central to the largest advances in computer vision, in recent years. This work entails application of such convolutional networks for the purpose of recognizing a meniscus tear in MRI images as attempting a step towards developing a computer aided diagnosis system for osteoarthritis. Consequently, state-of-the-art pre-trained image recognition networks namely Alexnet, Inceptionv3, VGG and Resnet and Xception were trained on MRI data of the knee meniscus to see if they work for the task of recognizing a tear. A comparison of their classification performance on MRI data was done. The best performing model was the fine-tuned InceptionV3 network which achieved an accuracy close to 60% for classifying 600 patients based on presence of a tear or not. Y1 - 2017 ER - TY - GEN A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Wilson, Dave A1 - Anglin, Carolyn A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data N2 - In this study we investigate methods for fitting a Statistical Shape Model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system. We validate the fitted models against the pre-operatively acquired Magnetic Resonance Imaging (MRI) data from the same patients. We consider a cohort of 10 patients who underwent navigated total knee arthroplasty. As part of the surgical protocol the patients’ distal femurs were partially digitized. All patients had an MRI scan two months pre-operatively. The MRI data were manually segmented and the reconstructed bone surfaces used as ground truth against which the fit was compared. Two methods were used to fit the SSM to the data, based on (1) Iterative Closest Points (ICP) and (2) Gaussian Mixture Models (GMM). For both approaches, the difference between model fit and ground truth surface averaged less than 1.7 mm and excellent correspondence with the distal femoral morphology can be demonstrated. T3 - ZIB-Report - 17-71 KW - Knee Arthroplasty KW - Sparse Geometry Reconstruction KW - Statistical Shape Models Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-66052 SN - 1438-0064 ER - TY - GEN A1 - Sahu, Manish A1 - Dill, Sabrina A1 - Mukhopadyay, Anirban A1 - Zachow, Stefan T1 - Surgical Tool Presence Detection for Cataract Procedures N2 - This article outlines the submission to the CATARACTS challenge for automatic tool presence detection [1]. Our approach for this multi-label classification problem comprises labelset-based sampling, a CNN architecture and temporal smothing as described in [3], which we call ZIB-Res-TS. T3 - ZIB-Report - 18-28 Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-69110 SN - 1438-0064 ER - TY - JOUR A1 - Tack, Alexander A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative JF - Osteoarthritis and Cartilage N2 - Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p<0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA. Y1 - 2018 U6 - https://doi.org/10.1016/j.joca.2018.02.907 VL - 26 IS - 5 SP - 680 EP - 688 ER - TY - GEN A1 - Tack, Alexander A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material) N2 - Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p<0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA. Y1 - 2018 U6 - https://doi.org/10.12752/4.TMZ.1.0 N1 - Supplementary data to reproduce results from the related publication, including convolutional neural networks' weights. ER - TY - CHAP A1 - Krämer, Martin A1 - Maggioni, Marta A1 - Tycowicz, Christoph von A1 - Brisson, Nick A1 - Zachow, Stefan A1 - Duda, Georg A1 - Reichenbach, Jürgen T1 - Ultra-short echo-time (UTE) imaging of the knee with curved surface reconstruction-based extraction of the patellar tendon T2 - ISMRM (International Society for Magnetic Resonance in Medicine), 26th Annual Meeting 2018, Paris, France N2 - Due to very short T2 relaxation times, imaging of tendons is typically performed using ultra-short echo-time (UTE) acquisition techniques. In this work, we combined an echo-train shifted multi-echo 3D UTE imaging sequence with a 3D curved surface reconstruction to virtually extract the patellar tendon from an acquired 3D UTE dataset. Based on the analysis of the acquired multi-echo data, a T2* relaxation time parameter map was calculated and interpolated to the curved surface of the patellar tendon. Y1 - 2018 ER - TY - CHAP A1 - Ambellan, Felix A1 - Tack, Alexander A1 - Ehlke, Moritz A1 - Zachow, Stefan T1 - Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative T2 - Medical Imaging with Deep Learning N2 - We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging, that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The method is evaluated on data of the MICCAI grand challenge "Segmentation of Knee Images 2010". For the first time an accuracy equivalent to the inter-observer variability of human readers has been achieved in this challenge. Moreover, the quality of the proposed method is thoroughly assessed using various measures for 507 manual segmentations of bone and cartilage, and 88 additional manual segmentations of cartilage. Our method yields sub-voxel accuracy. In conclusion, combining of anatomical knowledge using SSMs with localized classification via CNNs results in a state-of-the-art segmentation method. Y1 - 2018 ER - TY - JOUR A1 - Brüning, Jan A1 - Hildebrandt, Thomas A1 - Heppt, Werner A1 - Schmidt, Nora A1 - Lamecker, Hans A1 - Szengel, Angelika A1 - Amiridze, Natalja A1 - Ramm, Heiko A1 - Bindernagel, Matthias A1 - Zachow, Stefan A1 - Goubergrits, Leonid T1 - Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity JF - Scientific Reports N2 - This study’s objective was the generation of a standardized geometry of the healthy nasal cavity. An average geometry of the healthy nasal cavity was generated using a statistical shape model based on 25 symptom-free subjects. Airflow within the average geometry and these geometries was calculated using fluid simulations. Integral measures of the nasal resistance, wall shear stresses (WSS) and velocities were calculated as well as cross-sectional areas (CSA). Furthermore, individual WSS and static pressure distributions were mapped onto the average geometry. The average geometry featured an overall more regular shape that resulted in less resistance, reduced wall shear stresses and velocities compared to the median of the 25 geometries. Spatial distributions of WSS and pressure of average geometry agreed well compared to the average distributions of all individual geometries. The minimal CSA of the average geometry was larger than the median of all individual geometries (83.4 vs. 74.7 mm²). The airflow observed within the average geometry of the healthy nasal cavity did not equal the average airflow of the individual geometries. While differences observed for integral measures were notable, the calculated values for the average geometry lay within the distributions of the individual parameters. Spatially resolved parameters differed less prominently. Y1 - 2020 UR - https://rdcu.be/b2irD U6 - https://doi.org/10.1038/s41598-020-60755-3 VL - 3755 IS - 10 ER - TY - THES A1 - Amiridze, Natalja T1 - Morphological indicators for limitations in nasal breathing T1 - Morphologische Indikatoren für Einschränkungen der Nasenatmung N2 - In order to better understand the relationship between shape of the nasal cavity and to find objective classification for breathing obstruction, a population of 25 cases of healthy nasal cavity and 27 cases with diagnosed nasal airway obstruction (NAO) was examined for correlations between morphological, clinical and CFD parameters. For this purpose a workflow was implemented in Tcl to perform automatic measurements of morphological parameters of nasal cavity surfaces in Amira, which has as output a table with all estimated values. Furthermore, the statistical analysis was designed using Python to find the most probable subset of parameters that are predictors of nasal cavity pathology and consisted of correlation analysis, the selection of the best possible subset of parameters that could be used as predictors of clinically stated pathology of the nasal cavity by a logistic regression classifier. As a result, 10 most promising parameters were identified: mean distance between the two isthmuses, left isthmus contour, area ratio between the two isthmuses, left isthmus height, height ratio between the two isthmuses, left isthmus width, right isthmus width, right isthmus hydraulic diameter, mean distance of septal curvature between the septum enclosing walls of the nasal cavity, velocities volume average by expiration. As it turns out, most parameters refer to the isthmus region. This was to be expected since this region plays an important role in the airflow system of the nasal cavity. Y1 - 2020 ER - TY - JOUR A1 - Pimentel, Pedro A1 - Szengel, Angelika A1 - Ehlke, Moritz A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Estacio, Laura A1 - Doenitz, Christian A1 - Ramm, Heiko ED - Li, Jianning ED - Egger, Jan T1 - Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks BT - First Challenge, AutoImplant 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 8, 2020, Proceedings JF - Towards the Automatization of Cranial Implant Design in Cranioplasty N2 - We present an automated method for extrapolating missing regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-speci� c cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head. Our approach utilizes a 3D statistical shape model (SSM) of the skull and a 2D generative adversarial network (GAN) that is trained in an unsupervised fashion from samples of healthy patients alone. By � tting the SSM to given input labels containing the skull defect, a First approximation of the healthy state of the patient is obtained. The GAN is then applied to further correct and smooth the output of the SSM in an anatomically plausible manner. Finally, the defect region is extracted using morphological operations and subtraction between the extrapolated healthy state of the patient and the defective input labels. The method is trained and evaluated based on data from the MICCAI 2020 AutoImplant challenge. It produces state-of-the art results on regularly shaped cut-outs that were present in the training and testing data of the challenge. Furthermore, due to unsupervised nature of the approach, the method generalizes well to previously unseen defects of varying shapes that were only present in the hidden test dataset. Y1 - 2020 U6 - https://doi.org/10.1007/978-3-030-64327-0_3 N1 - Best Paper Award VL - 12439 SP - 16 EP - 27 PB - Springer International Publishing ET - 1 ER - TY - GEN A1 - Grewe, Carl Martin A1 - Le Roux, Gabriel A1 - Pilz, Sven-Kristofer A1 - Zachow, Stefan T1 - Spotting the Details: The Various Facets of Facial Expressions N2 - 3D Morphable Models (MM) are a popular tool for analysis and synthesis of facial expressions. They represent plausible variations in facial shape and appearance within a low-dimensional parameter space. Fitted to a face scan, the model's parameters compactly encode its expression patterns. This expression code can be used, for instance, as a feature in automatic facial expression recognition. For accurate classification, an MM that can adequately represent the various characteristic facets and variants of each expression is necessary. Currently available MMs are limited in the diversity of expression patterns. We present a novel high-quality Facial Expression Morphable Model built from a large-scale face database as a tool for expression analysis and synthesis. Establishment of accurate dense correspondence, up to finest skin features, enables a detailed statistical analysis of facial expressions. Various characteristic shape patterns are identified for each expression. The results of our analysis give rise to a new facial expression code. We demonstrate the advantages of such a code for the automatic recognition of expressions, and compare the accuracy of our classifier to state-of-the-art. T3 - ZIB-Report - 18-06 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-67696 SN - 1438-0064 ER - TY - JOUR A1 - Oeltze-Jaffra, Steffen A1 - Meuschke, Monique A1 - Neugebauer, Mathias A1 - Saalfeld, Sylvia A1 - Lawonn, Kai A1 - Janiga, Gabor A1 - Hege, Hans-Christian A1 - Zachow, Stefan A1 - Preim, Bernhard T1 - Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends, and Future Challenges JF - Computer Graphics Forum N2 - Simulations and measurements of blood and air flow inside the human circulatory and respiratory system play an increasingly important role in personalized medicine for prevention, diagnosis, and treatment of diseases. This survey focuses on three main application areas. (1) Computational Fluid Dynamics (CFD) simulations of blood flow in cerebral aneurysms assist in predicting the outcome of this pathologic process and of therapeutic interventions. (2) CFD simulations of nasal airflow allow for investigating the effects of obstructions and deformities and provide therapy decision support. (3) 4D Phase-Contrast (4D PC) Magnetic Resonance Imaging (MRI) of aortic hemodynamics supports the diagnosis of various vascular and valve pathologies as well as their treatment. An investigation of the complex and often dynamic simulation and measurement data requires the coupling of sophisticated visualization, interaction, and data analysis techniques. In this paper, we survey the large body of work that has been conducted within this realm. We extend previous surveys by incorporating nasal airflow, addressing the joint investigation of blood flow and vessel wall properties, and providing a more fine-granular taxonomy of the existing techniques. From the survey, we extract major research trends and identify open problems and future challenges. The survey is intended for researchers interested in medical flow but also more general, in the combined visualization of physiology and anatomy, the extraction of features from flow field data and feature-based visualization, the visual comparison of different simulation results, and the interactive visual analysis of the flow field and derived characteristics. Y1 - 2019 U6 - https://doi.org/10.1111/cgf.13394 VL - 38 IS - 1 SP - 87 EP - 125 PB - Wiley ER - TY - GEN A1 - Tack, Alexander A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative N2 - Abstract: Objective: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. Method: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MOAKS experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression. Results: Segmentation accuracy measured as Dice Similarity Coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ=0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16±0.93 mm vs. 0.83±0.92 mm; p<0.05). Conclusion: Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA. T3 - ZIB-Report - 18-15 Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-68038 SN - 1438-0064 VL - 26 IS - 5 SP - 680 EP - 688 ER - TY - JOUR A1 - Weiser, Martin A1 - Erdmann, Bodo A1 - Schenkl, Sebastian A1 - Muggenthaler, Holger A1 - Hubig, Michael A1 - Mall, Gita A1 - Zachow, Stefan T1 - Uncertainty in Temperature-Based Determination of Time of Death JF - Heat and Mass Transfer N2 - Temperature-based estimation of time of death (ToD) can be per- formed either with the help of simple phenomenological models of corpse cooling or with detailed mechanistic (thermodynamic) heat transfer mod- els. The latter are much more complex, but allow a higher accuracy of ToD estimation as in principle all relevant cooling mechanisms can be taken into account. The potentially higher accuracy depends on the accuracy of tissue and environmental parameters as well as on the geometric resolution. We in- vestigate the impact of parameter variations and geometry representation on the estimated ToD based on a highly detailed 3D corpse model, that has been segmented and geometrically reconstructed from a computed to- mography (CT) data set, differentiating various organs and tissue types. Y1 - 2018 U6 - https://doi.org/10.1007/s00231-018-2324-4 VL - 54 IS - 9 SP - 2815 EP - 2826 PB - Springer ER - TY - CHAP A1 - Neumann, Mario A1 - Hellwich, Olaf A1 - Zachow, Stefan T1 - Localization and Classification of Teeth in Cone Beam CT using Convolutional Neural Networks T2 - Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC) N2 - In dentistry, software-based medical image analysis and visualization provide efficient and accurate diagnostic and therapy planning capabilities. We present an approach for the automatic recognition of tooth types and positions in digital volume tomography (DVT). By using deep learning techniques in combination with dimensionality reduction through non-planar reformatting of the jaw anatomy, DVT data can be efficiently processed and teeth reliably recognized and classified, even in the presence of imaging artefacts, missing or dislocated teeth. We evaluated our approach, which is based on 2D Convolutional Neural Networks (CNNs), on 118 manually annotated cases of clinical DVT datasets. Our proposed method correctly classifies teeth with an accuracy of 94% within a limit of 2mm distance to ground truth labels. Y1 - 2019 SN - 978-3-00-063717-9 SP - 182 EP - 188 ER - TY - CHAP A1 - Joachimsky, Robert A1 - Ma, Lihong A1 - Icking, Christian A1 - Zachow, Stefan T1 - A Collision-Aware Articulated Statistical Shape Model of the Human Spine T2 - Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC) N2 - Statistical Shape Models (SSMs) are a proven means for model-based 3D anatomy reconstruction from medical image data. In orthopaedics and biomechanics, SSMs are increasingly employed to individualize measurement data or to create individualized anatomical models to which implants can be adapted to or functional tests can be performed on. For modeling and analysis of articulated structures, so called articulated SSMs (aSSMs) have been developed. However, a missing feature of aSSMs is the consideration of collisions in the course of individual fitting and articulation. The aim of our work was to develop aSSMs that handle collisions between components correctly. That way it becomes possible to adjust shape and articulation in view of a physically and geometrically plausible individualization. To be able to apply collision-aware aSSMs in simulation and optimisation, our approach is based on an e� cient collision detection method employing Graphics Processing Units (GPUs). Y1 - 2019 SP - 58 EP - 64 ER - TY - JOUR A1 - Krämer, Martin A1 - Maggioni, Marta A1 - Brisson, Nicholas A1 - Zachow, Stefan A1 - Teichgräber, Ulf A1 - Duda, Georg A1 - Reichenbach, Jürgen T1 - T1 and T2* mapping of the human quadriceps and patellar tendons using ultra-short echo-time (UTE) imaging and bivariate relaxation parameter-based volumetric visualization JF - Magnetic Resonance Imaging N2 - Quantification of magnetic resonance (MR)-based relaxation parameters of tendons and ligaments is challenging due to their very short transverse relaxation times, requiring application of ultra-short echo-time (UTE) imaging sequences. We quantify both T1 and T2⁎ in the quadriceps and patellar tendons of healthy volunteers at a field strength of 3 T and visualize the results based on 3D segmentation by using bivariate histogram analysis. We applied a 3D ultra-short echo-time imaging sequence with either variable repetition times (VTR) or variable flip angles (VFA) for T1 quantification in combination with multi-echo acquisition for extracting T2⁎. The values of both relaxation parameters were subsequently binned for bivariate histogram analysis and corresponding cluster identification, which were subsequently visualized. Based on manually-drawn regions of interest in the tendons on the relaxation parameter maps, T1 and T2⁎ boundaries were selected in the bivariate histogram to segment the quadriceps and patellar tendons and visualize the relaxation times by 3D volumetric rendering. Segmentation of bone marrow, fat, muscle and tendons was successfully performed based on the bivariate histogram analysis. Based on the segmentation results mean T2⁎ relaxation times, over the entire tendon volumes averaged over all subjects, were 1.8 ms ± 0.1 ms and 1.4 ms ± 0.2 ms for the patellar and quadriceps tendons, respectively. The mean T1 value of the patellar tendon, averaged over all subjects, was 527 ms ± 42 ms and 476 ms ± 40 ms for the VFA and VTR acquisitions, respectively. The quadriceps tendon had higher mean T1 values of 662 ms ± 97 ms (VFA method) and 637 ms ± 40 ms (VTR method) compared to the patellar tendon. 3D volumetric visualization of the relaxation times revealed that T1 values are not constant over the volume of both tendons, but vary locally. This work provided additional data to build upon the scarce literature available on relaxation times in the quadriceps and patellar tendons. We were able to segment both tendons and to visualize the relaxation parameter distributions over the entire tendon volumes. Y1 - 2019 U6 - https://doi.org/10.1016/j.mri.2019.07.015 VL - 63 IS - 11 SP - 29 EP - 36 ER - TY - THES A1 - Neumann, Mario T1 - Localization and Classification of Teeth in Cone Beam Computed Tomography using 2D CNNs N2 - In dentistry, software-based medical image analysis and visualization provide effcient and accurate diagnostic and therapy planning capabilities. We present an approach for the automatic recognition of tooth types and positions in digital volume tomography (DVT). By using deep learning techniques in combination with dimension reduction through non-planar reformatting of the jaw anatomy, DVT data can be effciently processed and teeth reliably recognized and classified, even in the presence of imaging artefacts, missing or dislocated teeth. We evaluated our approach, which is based on 2D Convolutional Neural Networks (CNNs), on 118 manually annotated cases of clinical DVT datasets. Our proposed method correctly classifies teeth with an accuracy of 94% within a limit of 2mm distancr to ground truth landmarks. KW - Dental Imaging KW - Tooth Classification KW - Dimension Reduction KW - Image Reformatting Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-74045 ER - TY - THES A1 - Joachimsky, Robert T1 - Approaching Spinal Kinematics using a Collision-Aware Articulated Deformable Model N2 - Statistical Shape Models (SSMs) allow for a compact representation of shape and shape variation and they are a proven means for model-based 3D anatomy reconstruction from medical image data. In orthopaedics and biomechanics, SSMs are increasingly employed to individualize measurement data or to create individualized anatomical models. The human spine is a versatile and complex articulated structure and thus is an interesting candidate to be modeled using an advanced type of SSMs. For modeling and analysis of articulated structures, so called articulated SSMs (aSSMs) have been developed. However, a missing feature of aSSMs is the consideration of collisions in the course of individual fitting and articulation. The aim of this thesis is to develop an aSSM of two adjacent vertebrae that handles collisions between components correctly. The model will incorporate the two major aspects of variability: Shape of a single vertebra and the relative positioning of neighboring vertebrae. That way it becomes possible to adjust shape and articulation in view of a physically and geometrically plausible individualization. To be able to apply collision-aware aSSMs in simulation and optimisation in future work, the approach is based on a parallelized collision detection method employing Graphics Processing Units (GPUs). KW - Spine Modeling KW - Spinal Kinematics KW - GPU-Accelerated Collision Detection KW - Spherical Shell Y1 - 2019 ER -