@misc{Weber2008, type = {Master Thesis}, author = {Weber, Britta}, title = {Merkmalskurven auf triangulierten Oberfl{\"a}chen}, year = {2008}, language = {en} } @misc{Wittmers2011, type = {Master Thesis}, author = {Wittmers, Antonia}, title = {Ein Werkzeug zur Erzeugung konsistenter Netze auf triangulierten Oberfl{\"a}chen}, year = {2011}, language = {en} } @misc{RammMorilloVictoriaTodtetal.2013, author = {Ramm, Heiko and Morillo Victoria, Oscar Salvador and Todt, Ingo and Schirmacher, Hartmut and Ernst, Arneborg and Zachow, Stefan and Lamecker, Hans}, title = {Visual Support for Positioning Hearing Implants}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-42495}, year = {2013}, abstract = {We present a software planning tool that provides intuitive visual feedback for finding suitable positions of hearing implants in the human temporal bone. After an automatic reconstruction of the temporal bone anatomy the tool pre-positions the implant and allows the user to adjust its position interactively with simple 2D dragging and rotation operations on the bone's surface. During this procedure, visual elements like warning labels on the implant or color encoded bone density information on the bone geometry provide guidance for the determination of a suitable fit.}, language = {en} } @misc{AmbellanLameckervonTycowiczetal.2019, author = {Ambellan, Felix and Lamecker, Hans and von Tycowicz, Christoph and Zachow, Stefan}, title = {Statistical Shape Models - Understanding and Mastering Variation in Anatomy}, issn = {1438-0064}, doi = {10.1007/978-3-030-19385-0_5}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-72699}, year = {2019}, abstract = {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.}, language = {en} } @misc{AmbellanTackEhlkeetal.2019, author = {Ambellan, Felix and Tack, Alexander and Ehlke, Moritz and Zachow, Stefan}, title = {Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-72704}, year = {2019}, abstract = {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.}, language = {en} } @incollection{AmbellanLameckervonTycowiczetal.2019, author = {Ambellan, Felix and Lamecker, Hans and von Tycowicz, Christoph and Zachow, Stefan}, title = {Statistical Shape Models - Understanding and Mastering Variation in Anatomy}, volume = {3}, booktitle = {Biomedical Visualisation}, number = {1156}, editor = {Rea, Paul M.}, edition = {1}, publisher = {Springer Nature Switzerland AG}, isbn = {978-3-030-19384-3}, doi = {10.1007/978-3-030-19385-0_5}, pages = {67 -- 84}, year = {2019}, abstract = {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.}, language = {en} } @article{HildebrandtBrueningSchmidtetal.2019, author = {Hildebrandt, Thomas and Bruening, Jan Joris and Schmidt, Nora Laura and Lamecker, Hans and Heppt, Werner and Zachow, Stefan and Goubergrits, Leonid}, title = {The Healthy Nasal Cavity - Characteristics of Morphology and Related Airflow Based on a Statistical Shape Model Viewed from a Surgeon's Perspective}, volume = {35}, journal = {Facial Plastic Surgery}, number = {1}, doi = {10.1055/s-0039-1677721}, pages = {9 -- 13}, year = {2019}, abstract = {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.}, language = {en} } @article{HildebrandtBrueningLameckeretal.2019, author = {Hildebrandt, Thomas and Bruening, Jan Joris and Lamecker, Hans and Zachow, Stefan and Heppt, Werner and Schmidt, Nora and Goubergrits, Leonid}, title = {Digital Analysis of Nasal Airflow Facilitating Decision Support in Rhinosurgery}, volume = {35}, journal = {Facial Plastic Surgery}, number = {1}, doi = {10.1055/s-0039-1677720}, pages = {1 -- 8}, year = {2019}, abstract = {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.}, language = {en} } @masterthesis{Prendke2019, type = {Bachelor Thesis}, author = {Prendke, Mona}, title = {Comparison of 2D and 3D CNNs for Classification of Knee MRI}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-72439}, pages = {53}, year = {2019}, language = {en} } @misc{Dill2018, type = {Master Thesis}, author = {Dill, Sabrina Patricia}, title = {Joint Feature Learning and Classification - Deep Learning for Surgical Phase Detection}, pages = {88}, year = {2018}, language = {en} } @inproceedings{TackZachow2019, author = {Tack, Alexander and Zachow, Stefan}, title = {Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, booktitle = {IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, doi = {10.1109/ISBI.2019.8759201}, pages = {40 -- 43}, year = {2019}, abstract = {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.}, language = {en} } @misc{Gidey2019, type = {Master Thesis}, author = {Gidey, Henok Hagos}, title = {Automated Hip Knee Ankle Angle Determination using Convolutional Neural Networks}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-71263}, pages = {98}, year = {2019}, abstract = {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.}, language = {en} } @misc{AmbellanTackEhlkeetal.2019, author = {Ambellan, Felix and Tack, Alexander and Ehlke, Moritz and Zachow, Stefan}, title = {Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material)}, volume = {52}, journal = {Medical Image Analysis}, number = {2}, doi = {10.12752/4.ATEZ.1.0}, pages = {109 -- 118}, year = {2019}, abstract = {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.}, language = {en} } @article{AmbellanTackEhlkeetal.2019, author = {Ambellan, Felix and Tack, Alexander and Ehlke, Moritz and Zachow, Stefan}, title = {Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, volume = {52}, journal = {Medical Image Analysis}, number = {2}, doi = {10.1016/j.media.2018.11.009}, pages = {109 -- 118}, year = {2019}, abstract = {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.}, language = {en} } @article{AlHajjSahuLamardetal.2019, author = {Al Hajj, Hassan and Sahu, Manish and Lamard, Mathieu and Conze, Pierre-Henri and Roychowdhury, Soumali and Hu, Xiaowei and Marsalkaite, Gabija and Zisimopoulos, Odysseas and Dedmari, Muneer Ahmad and Zhao, Fenqiang and Prellberg, Jonas and Galdran, Adrian and Araujo, Teresa and Vo, Duc My and Panda, Chandan and Dahiya, Navdeep and Kondo, Satoshi and Bian, Zhengbing and Bialopetravicius, Jonas and Qiu, Chenghui and Dill, Sabrina and Mukhopadyay, Anirban and Costa, Pedro and Aresta, Guilherme and Ramamurthy, Senthil and Lee, Sang-Woong and Campilho, Aurelio and Zachow, Stefan and Xia, Shunren and Conjeti, Sailesh and Armaitis, Jogundas and Heng, Pheng-Ann and Vahdat, Arash and Cochener, Beatrice and Quellec, Gwenole}, title = {CATARACTS: Challenge on Automatic Tool Annotation for cataRACT Surgery}, volume = {52}, journal = {Medical Image Analysis}, number = {2}, publisher = {Elsevier}, doi = {10.1016/j.media.2018.11.008}, pages = {24 -- 41}, year = {2019}, abstract = {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.}, language = {en} } @misc{TackZachow2019, author = {Tack, Alexander and Zachow, Stefan}, title = {Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-71439}, year = {2019}, abstract = {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.}, language = {en} } @article{HoffmannLemanisWulffetal.2018, author = {Hoffmann, Rene and Lemanis, Robert and Wulff, Lena and Zachow, Stefan and Lukeneder, Alexander and Klug, Christian and Keupp, Helmut}, title = {Traumatic events in the life of the deep-sea cephalopod mollusc, the coleoid Spirula spirula}, volume = {142}, journal = {ScienceDirect: Deep Sea Research Part I - Oceanographic Research}, number = {12}, doi = {10.1016/j.dsr.2018.10.007}, pages = {127 -- 144}, year = {2018}, abstract = {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.}, language = {en} } @misc{TycowiczAmbellanMukhopadhyayetal.2016, author = {Tycowicz, Christoph von and Ambellan, Felix and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {A Riemannian Statistical Shape Model using Differential Coordinates}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-61175}, year = {2016}, abstract = {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.}, language = {en} } @article{LemanisZachowHoffmann2016, author = {Lemanis, Robert and Zachow, Stefan and Hoffmann, Ren{\´e}}, title = {Comparative cephalopod shell strength and the role of septum morphology on stress distribution}, volume = {4}, journal = {PeerJ}, doi = {10.7717/peerj.2434}, pages = {e2434}, year = {2016}, abstract = {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.}, language = {en} } @article{BernardSalamancaThunbergetal.2017, author = {Bernard, Florian and Salamanca, Luis and Thunberg, Johan and Tack, Alexander and Jentsch, Dennis and Lamecker, Hans and Zachow, Stefan and Hertel, Frank and Goncalves, Jorge and Gemmar, Peter}, title = {Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds}, volume = {38}, journal = {Medical Image Analysis}, doi = {10.1016/j.media.2017.02.005}, pages = {77 -- 89}, year = {2017}, abstract = {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.}, language = {en} } @incollection{ZachowWeiserDeuflhard2008, author = {Zachow, Stefan and Weiser, Martin and Deuflhard, Peter}, title = {Modellgest{\"u}tzte Operationsplanung in der Kopfchirurgie}, booktitle = {Modellgest{\"u}tzte Therapie}, editor = {Niederlag, Wolfgang and Lemke, Heinz and Meixensberger, J{\"u}rgen and Baumann, Michael}, publisher = {Health Academy}, pages = {140 -- 156}, year = {2008}, language = {en} } @article{ZeilhoferZachowFairleyetal.2000, author = {Zeilhofer, Hans-Florian and Zachow, Stefan and Fairley, Jeffrey and Sader, Robert and Deuflhard, Peter}, title = {Treatment Planning and Simulation in Craniofacial Surgery with Virtual Reality Techiques}, volume = {28 (Suppl. 1)}, journal = {Journal of Cranio-Maxillofacial Surgery}, pages = {82}, year = {2000}, language = {en} } @article{LamasRodriguezHerasArgueelloetal.2013, author = {Lamas-Rodr{\´i}guez, Juli{\´a}n and Heras, Dora Blanco and Arg{\"u}ello, Francisco and Kainm{\"u}ller, Dagmar and Zachow, Stefan and B{\´o}o, Montserrat}, title = {GPU-accelerated level-set segmentation}, journal = {Journal of Real-Time Image Processing}, publisher = {Springer Berlin Heidelberg}, issn = {1861-8200}, doi = {10.1007/s11554-013-0378-6}, pages = {1 -- 15}, year = {2013}, language = {en} } @article{HoffmannSchultzSchellhornetal.2014, author = {Hoffmann, Ren{\´e} and Schultz, Julia A. and Schellhorn, Rico and Rybacki, Erik and Keupp, Helmut and Gerden, S. R. and Lemanis, Robert and Zachow, Stefan}, title = {Non-invasive imaging methods applied to neo- and paleontological cephalopod research}, volume = {11}, journal = {Biogeosciences}, number = {10}, doi = {10.5194/bg-11-2721-2014}, pages = {2721 -- 2739}, year = {2014}, abstract = {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.}, language = {en} } @phdthesis{Kainmueller2013, author = {Kainm{\"u}ller, Dagmar}, title = {Deformable Meshes for Accurate Automatic Segmentation of Medical Image Data}, year = {2013}, language = {en} } @misc{Bindernagel2013, type = {Master Thesis}, author = {Bindernagel, Matthias}, title = {Articulated Statistical Shape Models}, year = {2013}, language = {en} } @misc{Renard2011, type = {Master Thesis}, author = {Renard, Maximilien}, title = {Improvement of Image Segmentation Based on Statistical Shape and Intensity Models}, year = {2011}, language = {en} } @misc{Jalda2009, type = {Master Thesis}, author = {Jalda, Dworzak}, title = {Reconstruction of the Human Rib Cage from 2D Projection Images using a Statistical Shape Model}, year = {2009}, language = {en} } @inproceedings{RammVictoriaMorilloTodtetal.2013, author = {Ramm, Heiko and Victoria Morillo, Oscar Salvador and Todt, Ingo and Schirmacher, Hartmut and Ernst, Arneborg and Zachow, Stefan and Lamecker, Hans}, title = {Visual Support for Positioning Hearing Implants}, booktitle = {Proceedings of the 12th annual meeting of the CURAC society}, editor = {Freysinger, Wolfgang}, pages = {116 -- 120}, year = {2013}, language = {en} } @article{KainmuellerLameckerHelleretal.2013, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Heller, Markus O. and Weber, Britta and Hege, Hans-Christian and Zachow, Stefan}, title = {Omnidirectional Displacements for Deformable Surfaces}, volume = {17}, journal = {Medical Image Analysis}, number = {4}, publisher = {Elsevier}, doi = {10.1016/j.media.2012.11.006}, pages = {429 -- 441}, year = {2013}, language = {en} } @article{DunlopApanaskevichLehmannetal.2016, author = {Dunlop, Jason and Apanaskevich, Dmitry and Lehmann, Jens and Hoffmann, Rene and Fusseis, Florian and Ehlke, Moritz and Zachow, Stefan and Xiao, Xianghui}, title = {Microtomography of the Baltic amber tick Ixodes succineus reveals affinities with the modern Asian disease vector Ixodes ovatus}, volume = {16}, journal = {BMC Evolutionary Biology}, number = {1}, doi = {10.1186/s12862-016-0777-y}, year = {2016}, abstract = {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.}, language = {en} } @article{SchenklMuggenthalerHubigetal.2017, author = {Schenkl, Sebastian and Muggenthaler, Holger and Hubig, Michael and Erdmann, Bodo and Weiser, Martin and Zachow, Stefan and Heinrich, Andreas and G{\"u}ttler, Felix Victor and Teichgr{\"a}ber, Ulf and Mall, Gita}, title = {Automatic CT-based finite element model generation for temperature-based death time estimation: feasibility study and sensitivity analysis}, volume = {131}, journal = {International Journal of Legal Medicine}, number = {3}, doi = {doi:10.1007/s00414-016-1523-0}, pages = {699 -- 712}, year = {2017}, abstract = {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.}, language = {en} } @misc{Tack2015, type = {Master Thesis}, author = {Tack, Alexander}, title = {Gruppenweise Registrierung zur robusten Bewegungsfeldsch{\"a}tzung in artefaktbehafteten 4D-CT-Bilddaten}, year = {2015}, abstract = {Das Ziel der Strahlentherapie ist, eine m{\"o}glichst 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 w{\"a}hrend der Bestrahlung große, komplexe und patientenspezifisch unterschiedliche Bewegungen der Gewebe auf. Um den Einfluss dieser Bewegung auf die i.d.R. statisch geplante Dosisverteilung abzusch{\"a}tzen, k{\"o}nnen unter Verwendung der nicht-linearen Bildregistrierung anhand von 3D-CT-Aufnahmen eines Atmungszyklus - also 4D-CT-Daten - zun{\"a}chst die Bewegungsfelder f{\"u}r die strahlentherapeutisch relevanten Strukturen, beispielsweise f{\"u}r die Lunge, berechnet werden. Diese Informationen bilden die Grundlage f{\"u}r sogenannte 4D-Dosisberechnungs- oder Dosisakkumulationsverfahren. Deren Genauigkeit h{\"a}ngt aber wesentlich von der Genauigkeit der Bewegungsfeldsch{\"a}tzung ab. Klassisch erfolgt die Berechnung der Bewegungsfelder mittels paarweiser Bildregistrierung, womit f{\"u}r die Berechnung des Bewegungsfeldes zwischen zwei Bildern im Allgemeinen eine sehr hohe Genauigkeit erreicht wird. Auch f{\"u}r CT-Bilder, die Bewegungsartefakte, wie beispielsweise doppelte oder unvollst{\"a}ndige 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 m{\"u}ssen weiterhin f{\"u}r einen Atemzyklus die Voxel-Trajektorien aus Bewegungsfeldern zwischen mehreren dreidimensionalen Bildern zusammengesetzt werden. Durch Bewegungsartefakte entsprechen diese Trajektorien dann teilweise keiner nat{\"u}rlichen 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 f{\"u}r frei zug{\"a}ngliche klinische Datens{\"a}tze 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 Gr{\"o}ßenordnung von aktuellen paarweisen Registrierungen verdeutlicht die Qualit{\"a}t 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 st{\"a}rker entsprechen. F{\"u}r die Patientendaten wird außerdem anhand von automatisch detektierten Landmarken der TRE ausgewertet. Der TRE verschlechterte sich f{\"u}r die paarweise Bildregistrierung unter Vorliegen von Bewegungsartefakten von durchschnittlich 1,30 mm auf 3,94 mm. Auch hier zeigte sich f{\"u}r die gruppenweise Registrierung die Robustheit gegen{\"u}ber Bewegungsartefakten und der TRE verschlechterte sich nur geringf{\"u}gig von 1,45 mm auf 1,71 mm.}, language = {de} } @misc{NavaYazdaniHegevonTycowiczetal.2018, author = {Nava-Yazdani, Esfandiar and Hege, Hans-Christian and von Tycowicz, Christoph and Sullivan, T. J.}, title = {A Shape Trajectories Approach to Longitudinal Statistical Analysis}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-69759}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{JoachimskyAmbellanZachow2017, author = {Joachimsky, Robert and Ambellan, Felix and Zachow, Stefan}, title = {Computerassistierte Auswahl und Platzierung von interpositionalen Spacern zur Behandlung fr{\"u}her Gonarthrose}, volume = {16}, booktitle = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65321}, pages = {106 -- 111}, year = {2017}, abstract = {Degenerative Gelenkerkrankungen, wie die Osteoarthrose, sind ein h{\"a}ufiges Krankheitsbild unter {\"a}lteren Erwachsenen. Hierbei verringert sich u.a. der Gelenkspalt aufgrund degenerierten Knorpels oder gesch{\"a}digter Menisci. Ein in den Gelenkspalt eingebrachter interpositionaler Spacer soll die mit der Osteoarthrose einhergehende verringerte Gelenkkontaktfl{\"a}che erh{\"o}hen und so der teilweise oder vollst{\"a}ndige Gelenkersatz hinausgez{\"o}gert oder vermieden werden. In dieser Arbeit pr{\"a}sentieren wir eine Planungssoftware f{\"u}r 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 ausgew{\"a}hlt und algorithmisch vorpositioniert. Die Positionierung des Spacers ist durch den Benutzer jederzeit interaktiv anpassbar. F{\"u}r jede Positionierung eines Spacers wird ein Fitness-Wert zur Knieanatomie des jeweiligen Patienten berechnet und den Nutzern R{\"u}ckmeldung hinsichtlich Passgenauigkeit gegeben. Die Software unterst{\"u}tzt somit als Entscheidungshilfe die behandelnden {\"A}rzte bei der patientenspezifischen Spacerauswahl.}, language = {de} } @inproceedings{AmbellanTackWilsonetal.2017, author = {Ambellan, Felix and Tack, Alexander and Wilson, Dave and Anglin, Carolyn and Lamecker, Hans and Zachow, Stefan}, title = {Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data}, volume = {16}, booktitle = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-65339}, pages = {24 -- 30}, year = {2017}, abstract = {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.}, language = {en} } @article{AkbariShandizBoulosSavarssonetal.2018, author = {Akbari Shandiz, Mohsen and Boulos, Paul and S{\ae}varsson, Stefan and Ramm, Heiko and Fu, Chun Kit and Miller, Stephen and Zachow, Stefan and Anglin, Carolyn}, title = {Changes in Knee Shape and Geometry Resulting from Total Knee Arthroplasty}, volume = {232}, journal = {Journal of Engineering in Medicine}, number = {1}, doi = {10.1177/0954411917743274}, pages = {67 -- 79}, year = {2018}, abstract = {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.}, language = {en} } @misc{Reddy2017, type = {Master Thesis}, author = {Reddy, Gutha Vaishnavi}, title = {Automatic Classification of 3D MRI data using Deep Convolutional Neural Networks}, pages = {60}, year = {2017}, abstract = {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.}, language = {en} } @misc{AmbellanTackWilsonetal.2017, author = {Ambellan, Felix and Tack, Alexander and Wilson, Dave and Anglin, Carolyn and Lamecker, Hans and Zachow, Stefan}, title = {Evaluating two methods for Geometry Reconstruction from Sparse Surgical Navigation Data}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-66052}, year = {2017}, abstract = {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.}, language = {en} } @misc{SahuDillMukhopadyayetal.2017, author = {Sahu, Manish and Dill, Sabrina and Mukhopadyay, Anirban and Zachow, Stefan}, title = {Surgical Tool Presence Detection for Cataract Procedures}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-69110}, year = {2017}, abstract = {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.}, language = {en} } @article{TackMukhopadhyayZachow2018, author = {Tack, Alexander and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, volume = {26}, journal = {Osteoarthritis and Cartilage}, number = {5}, doi = {10.1016/j.joca.2018.02.907}, pages = {680 -- 688}, year = {2018}, abstract = {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.}, language = {en} } @misc{TackMukhopadhyayZachow2018, author = {Tack, Alexander and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative (Supplementary Material)}, doi = {10.12752/4.TMZ.1.0}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{KraemerMaggioniTycowiczetal.2018, author = {Kr{\"a}mer, Martin and Maggioni, Marta and Tycowicz, Christoph von and Brisson, Nick and Zachow, Stefan and Duda, Georg and Reichenbach, J{\"u}rgen}, title = {Ultra-short echo-time (UTE) imaging of the knee with curved surface reconstruction-based extraction of the patellar tendon}, booktitle = {ISMRM (International Society for Magnetic Resonance in Medicine), 26th Annual Meeting 2018, Paris, France}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{AmbellanTackEhlkeetal.2018, author = {Ambellan, Felix and Tack, Alexander and Ehlke, Moritz and Zachow, Stefan}, title = {Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, booktitle = {Medical Imaging with Deep Learning}, year = {2018}, abstract = {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.}, language = {en} } @article{BrueningHildebrandtHepptetal.2020, author = {Br{\"u}ning, Jan and Hildebrandt, Thomas and Heppt, Werner and Schmidt, Nora and Lamecker, Hans and Szengel, Angelika and Amiridze, Natalja and Ramm, Heiko and Bindernagel, Matthias and Zachow, Stefan and Goubergrits, Leonid}, title = {Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity}, volume = {3755}, journal = {Scientific Reports}, number = {10}, doi = {10.1038/s41598-020-60755-3}, year = {2020}, abstract = {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.}, language = {en} } @masterthesis{Amiridze2020, type = {Bachelor Thesis}, author = {Amiridze, Natalja}, title = {Morphological indicators for limitations in nasal breathing}, pages = {54}, year = {2020}, abstract = {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.}, language = {en} } @article{PimentelSzengelEhlkeetal.2020, author = {Pimentel, Pedro and Szengel, Angelika and Ehlke, Moritz and Lamecker, Hans and Zachow, Stefan and Estacio, Laura and Doenitz, Christian and Ramm, Heiko}, title = {Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks}, volume = {12439}, journal = {Towards the Automatization of Cranial Implant Design in Cranioplasty}, editor = {Li, Jianning and Egger, Jan}, edition = {1}, publisher = {Springer International Publishing}, doi = {10.1007/978-3-030-64327-0_3}, pages = {16 -- 27}, year = {2020}, abstract = {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.}, language = {en} } @misc{GreweLeRouxPilzetal.2018, author = {Grewe, Carl Martin and Le Roux, Gabriel and Pilz, Sven-Kristofer and Zachow, Stefan}, title = {Spotting the Details: The Various Facets of Facial Expressions}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-67696}, year = {2018}, abstract = {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.}, language = {en} } @article{OeltzeJaffraMeuschkeNeugebaueretal.2019, author = {Oeltze-Jaffra, Steffen and Meuschke, Monique and Neugebauer, Mathias and Saalfeld, Sylvia and Lawonn, Kai and Janiga, Gabor and Hege, Hans-Christian and Zachow, Stefan and Preim, Bernhard}, title = {Generation and Visual Exploration of Medical Flow Data: Survey, Research Trends, and Future Challenges}, volume = {38}, journal = {Computer Graphics Forum}, number = {1}, publisher = {Wiley}, doi = {10.1111/cgf.13394}, pages = {87 -- 125}, year = {2019}, abstract = {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.}, language = {en} } @misc{TackMukhopadhyayZachow2018, author = {Tack, Alexander and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {Knee Menisci Segmentation using Convolutional Neural Networks: Data from the Osteoarthritis Initiative}, volume = {26}, number = {5}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-68038}, pages = {680 -- 688}, year = {2018}, abstract = {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.}, language = {en} } @article{WeiserErdmannSchenkletal.2018, author = {Weiser, Martin and Erdmann, Bodo and Schenkl, Sebastian and Muggenthaler, Holger and Hubig, Michael and Mall, Gita and Zachow, Stefan}, title = {Uncertainty in Temperature-Based Determination of Time of Death}, volume = {54}, journal = {Heat and Mass Transfer}, number = {9}, publisher = {Springer}, doi = {10.1007/s00231-018-2324-4}, pages = {2815 -- 2826}, year = {2018}, abstract = {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.}, language = {en} }