@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} } @inproceedings{SahuStroemsdoerferMukhopadhyayetal.2020, author = {Sahu, Manish and Str{\"o}msd{\"o}rfer, Ronja and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation}, volume = {12263}, booktitle = {Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part III}, publisher = {Springer Nature}, doi = {https://doi.org/10.1007/978-3-030-59716-0_75}, year = {2020}, abstract = {Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big labeled datasets. However, the annotation of a big dataset of real videos can be prohibitively expensive and time consuming. Computer simulations could alleviate the manual labeling problem, however, models trained on simulated data do not generalize to real data. This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue. Empirical results on two data sets (15 videos of the Cholec80 and EndoVis'15 dataset) highlight the effectiveness of the proposed Endo-Sim2Real method for instrument segmentation. We compare the segmentation of the proposed approach with state-of-the-art solutions and show that our method improves segmentation both in terms of quality and quantity.}, language = {en} } @article{SahuSzengelMukhopadhyayetal.2020, author = {Sahu, Manish and Szengel, Angelika and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {Surgical phase recognition by learning phase transitions}, volume = {6}, journal = {Current Directions in Biomedical Engineering (CDBME)}, number = {1}, publisher = {De Gruyter}, doi = {https://doi.org/10.1515/cdbme-2020-0037}, pages = {20200037}, year = {2020}, abstract = {Automatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference between intra- and inter-phase tool usage patterns has not been investigated for automatic phase recognition. We developed a Recurrent Neural Network (RNN), in particular a state-preserving Long Short Term Memory (LSTM) architecture to utilize the long-term evolution of tool usage within complete surgical procedures. For fully automatic tool presence detection from surgical video frames, a Convolutional Neural Network (CNN) based architecture namely ZIBNet is employed. Our proposed approach outperformed EndoNet by 8.1\% on overall precision for phase detection tasks and 12.5\% on meanAP for tool recognition tasks.}, language = {en} } @misc{SahuSzengelMukhopadhyayetal.2020, author = {Sahu, Manish and Szengel, Angelika and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {Analyzing laparoscopic cholecystectomy with deep learning: automatic detection of surgical tools and phases}, journal = {28th International Congress of the European Association for Endoscopic Surgery (EAES)}, year = {2020}, abstract = {Motivation: The ever-rising volume of patients, high maintenance cost of operating rooms and time consuming analysis of surgical skills are fundamental problems that hamper the practical training of the next generation of surgeons. The hospitals prefer to keep the surgeons busy in real operations over training young surgeons for obvious economic reasons. One fundamental need in surgical training is the reduction of the time needed by the senior surgeon to review the endoscopic procedures performed by the young surgeon while minimizing the subjective bias in evaluation. The unprecedented performance of deep learning ushers the new age of data-driven automatic analysis of surgical skills. Method: Deep learning is capable of efficiently analyzing thousands of hours of laparoscopic video footage to provide an objective assessment of surgical skills. However, the traditional end-to-end setting of deep learning (video in, skill assessment out) is not explainable. Our strategy is to utilize the surgical process modeling framework to divide the surgical process into understandable components. This provides the opportunity to employ deep learning for superior yet automatic detection and evaluation of several aspects of laparoscopic cholecystectomy such as surgical tool and phase detection. We employ ZIBNet for the detection of surgical tool presence. ZIBNet employs pre-processing based on tool usage imbalance, a transfer learned 50-layer residual network (ResNet-50) and temporal smoothing. To encode the temporal evolution of tool usage (over the entire video sequence) that relates to the surgical phases, Long Short Term Memory (LSTM) units are employed with long-term dependency. Dataset: We used CHOLEC 80 dataset that consists of 80 videos of laparoscopic cholecystectomy performed by 13 surgeons, divided equally for training and testing. In these videos, up to three different tools (among 7 types of tools) can be present in a frame. Results: The mean average precision of the detection of all tools is 93.5 ranging between 86.8 and 99.3, a significant improvement (p <0.01) over the previous state-of-the-art. We observed that less frequent tools like Scissors, Irrigator, Specimen Bag etc. are more related to phase transitions. The overall precision (recall) of the detection of all surgical phases is 79.6 (81.3). Conclusion: While this is not the end goal for surgical skill analysis, the development of such a technological platform is essential toward a data-driven objective understanding of surgical skills. In future, we plan to investigate surgeon-in-the-loop analysis and feedback for surgical skill analysis.}, 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{GreweZachow2017, author = {Grewe, Carl Martin and Zachow, Stefan}, title = {Face to Face-Interface}, journal = {+ultra. Knowledge \& Gestaltung}, editor = {Doll, Nikola and Bredekamp, Horst and Sch{\"a}ffner, Wolfgang}, publisher = {Seemann Henschel}, pages = {320 -- 321}, year = {2017}, 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{MoldenhauerWeiserZachow2017, author = {Moldenhauer, Marian and Weiser, Martin and Zachow, Stefan}, title = {Adaptive Algorithms for Optimal Hip Implant Positioning}, volume = {17}, journal = {PAMM}, number = {1}, doi = {10.1002/pamm.201710071}, pages = {203 -- 204}, year = {2017}, abstract = {In an aging society where the number of joint replacements rises, it is important to also increase the longevity of implants. In particular hip implants have a lifetime of at most 15 years. This derives primarily from pain due to implant migration, wear, inflammation, and dislocation, which is affected by the positioning of the implant during the surgery. Current joint replacement practice uses 2D software tools and relies on the experience of surgeons. Especially the 2D tools fail to take the patients' natural range of motion as well as stress distribution in the 3D joint induced by different daily motions into account. Optimizing the hip joint implant position for all possible parametrized motions under the constraint of a contact problem is prohibitively expensive as there are too many motions and every position change demands a recalculation of the contact problem. For the reduction of the computational effort, we use adaptive refinement on the parameter domain coupled with the interpolation method of Kriging. A coarse initial grid is to be locally refined using goal-oriented error estimation, reducing locally high variances. This approach will be combined with multi-grid optimization such that numerical errors are reduced.}, 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} } @inproceedings{NeumannHellwichZachow2019, author = {Neumann, Mario and Hellwich, Olaf and Zachow, Stefan}, title = {Localization and Classification of Teeth in Cone Beam CT using Convolutional Neural Networks}, booktitle = {Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC)}, isbn = {978-3-00-063717-9}, pages = {182 -- 188}, year = {2019}, abstract = {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.}, language = {en} } @inproceedings{JoachimskyMaIckingetal.2019, author = {Joachimsky, Robert and Ma, Lihong and Icking, Christian and Zachow, Stefan}, title = {A Collision-Aware Articulated Statistical Shape Model of the Human Spine}, booktitle = {Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC)}, pages = {58 -- 64}, year = {2019}, abstract = {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).}, language = {en} } @article{KraemerMaggioniBrissonetal.2019, author = {Kr{\"a}mer, Martin and Maggioni, Marta and Brisson, Nicholas and Zachow, Stefan and Teichgr{\"a}ber, Ulf and Duda, Georg and Reichenbach, J{\"u}rgen}, title = {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}, volume = {63}, journal = {Magnetic Resonance Imaging}, number = {11}, doi = {10.1016/j.mri.2019.07.015}, pages = {29 -- 36}, year = {2019}, abstract = {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.}, language = {en} } @misc{AmbellanZachowvonTycowicz2019, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {An as-invariant-as-possible GL+(3)-based Statistical Shape Model}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-74566}, year = {2019}, abstract = {We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling.}, language = {en} } @inproceedings{AmbellanZachowvonTycowicz2019, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {An as-invariant-as-possible GL+(3)-based Statistical Shape Model}, volume = {11846}, booktitle = {Proc. 7th MICCAI workshop on Mathematical Foundations of Computational Anatomy (MFCA)}, publisher = {Springer}, doi = {10.1007/978-3-030-33226-6_23}, pages = {219 -- 228}, year = {2019}, abstract = {We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling.}, language = {en} } @misc{AmbellanZachowvonTycowicz2019, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {A Surface-Theoretic Approach for Statistical Shape Modeling}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-74497}, year = {2019}, abstract = {We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability.}, language = {en} } @inproceedings{AmbellanZachowvonTycowicz2019, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {A Surface-Theoretic Approach for Statistical Shape Modeling}, volume = {11767}, booktitle = {Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part IV}, publisher = {Springer}, doi = {10.1007/978-3-030-32251-9_3}, pages = {21 -- 29}, year = {2019}, abstract = {We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability.}, language = {en} } @inproceedings{EstacioEhlkeTacketal.2021, author = {Estacio, Laura and Ehlke, Moritz and Tack, Alexander and Castro-Gutierrez, Eveling and Lamecker, Hans and Mora, Rensso and Zachow, Stefan}, title = {Unsupervised Detection of Disturbances in 2D Radiographs}, booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, doi = {10.1109/ISBI48211.2021.9434091}, pages = {367 -- 370}, year = {2021}, abstract = {We present a method based on a generative model for detection of disturbances such as prosthesis, screws, zippers, and metals in 2D radiographs. The generative model is trained in an unsupervised fashion using clinical radiographs as well as simulated data, none of which contain disturbances. Our approach employs a latent space consistency loss which has the benefit of identifying similarities, and is enforced to reconstruct X-rays without disturbances. In order to detect images with disturbances, an anomaly score is computed also employing the Frechet distance between the input X-ray and the reconstructed one using our generative model. Validation was performed using clinical pelvis radiographs. We achieved an AUC of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of our method for detecting outliers as well as the advantage of utilizing synthetic data.}, language = {en} } @article{SahuMukhopadhyayZachow2021, author = {Sahu, Manish and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation}, volume = {16}, journal = {International Journal of Computer Assisted Radiology and Surgery}, publisher = {Springer Nature}, arxiv = {http://arxiv.org/abs/arXiv:2103.01593}, doi = {10.1007/s11548-021-02383-4}, pages = {849 -- 859}, year = {2021}, abstract = {Purpose Segmentation of surgical instruments in endoscopic video streams is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts. Methods We introduce a teacher-student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the challenges in simulation-to-real unsupervised domain adaptation for endoscopic image segmentation. Results Empirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach. Conclusions We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical instrument in the annotation scarce setting.}, language = {en} } @article{TackPreimZachow2021, author = {Tack, Alexander and Preim, Bernhard and Zachow, Stefan}, title = {Fully automated Assessment of Knee Alignment from Full-Leg X-Rays employing a "YOLOv4 And Resnet Landmark regression Algorithm" (YARLA): Data from the Osteoarthritis Initiative}, volume = {205}, journal = {Computer Methods and Programs in Biomedicine}, number = {106080}, doi = {https://doi.org/10.1016/j.cmpb.2021.106080}, year = {2021}, abstract = {We present a method for the quantification of knee alignment from full-leg X-Rays. A state-of-the-art object detector, YOLOv4, was trained to locate regions of interests (ROIs) in full-leg X-Ray images for the hip joint, the knee, and the ankle. Residual neural networks (ResNets) were trained to regress landmark coordinates for each ROI.Based on the detected landmarks the knee alignment, i.e., the hip-knee-ankle (HKA) angle, was computed. The accuracy of landmark detection was evaluated by a comparison to manually placed landmarks for 360 legs in 180 X-Rays. The accuracy of HKA angle computations was assessed on the basis of 2,943 X-Rays. Results of YARLA were compared to the results of two independent image reading studies(Cooke; Duryea) both publicly accessible via the Osteoarthritis Initiative. The agreement was evaluated using Spearman's Rho, and weighted kappa as well as regarding the correspondence of the class assignment (varus/neutral/valgus). The average difference between YARLA and manually placed landmarks was less than 2.0+- 1.5 mm for all structures (hip, knee, ankle). The average mismatch between HKA angle determinations of Cooke and Duryea was 0.09 +- 0.63°; YARLA resulted in a mismatch of 0.10 +- 0.74° compared to Cooke and of 0.18 +- 0.64° compared to Duryea. Cooke and Duryea agreed almost perfectly with respect to a weighted kappa value of 0.86, and showed an excellent reliability as measured by a Spearman's Rho value of 0.99. Similar values were achieved by YARLA, i.e., a weighted kappa value of0.83 and 0.87 and a Spearman's Rho value of 0.98 and 0.99 to Cooke and Duryea,respectively. Cooke and Duryea agreed in 92\% of all class assignments and YARLA did so in 90\% against Cooke and 92\% against Duryea. In conclusion, YARLA achieved results comparable to those of human experts and thus provides a basis for an automated assessment of knee alignment in full-leg X-Rays.}, language = {de} } @article{SekuboyinaBayatHusseinietal.2020, author = {Sekuboyina, Anjany and Bayat, Amirhossein and Husseini, Malek E. and L{\"o}ffler, Maximilian and Li, Hongwei and Tetteh, Giles and Kukačka, Jan and Payer, Christian and Štern, Darko and Urschler, Martin and Chen, Maodong and Cheng, Dalong and Lessmann, Nikolas and Hu, Yujin and Wang, Tianfu and Yang, Dong and Xu, Daguang and Ambellan, Felix and Amiranashvili, Tamaz and Ehlke, Moritz and Lamecker, Hans and Lehnert, Sebastian and Lirio, Marilia and de Olaguer, Nicol{\´a}s P{\´e}rez and Ramm, Heiko and Sahu, Manish and Tack, Alexander and Zachow, Stefan and Jiang, Tao and Ma, Xinjun and Angerman, Christoph and Wang, Xin and Wei, Qingyue and Brown, Kevin and Wolf, Matthias and Kirszenberg, Alexandre and Puybareau, {\´E}lodie and Valentinitsch, Alexander and Rempfler, Markus and Menze, Bj{\"o}rn H. and Kirschke, Jan S.}, title = {VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images}, journal = {arXiv}, arxiv = {http://arxiv.org/abs/2001.09193}, year = {2020}, language = {en} } @article{AmbellanZachowvonTycowicz2021, author = {Ambellan, Felix and Zachow, Stefan and von Tycowicz, Christoph}, title = {Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms}, volume = {73}, journal = {Medical Image Analysis}, arxiv = {http://arxiv.org/abs/2111.06850}, doi = {10.1016/j.media.2021.102178}, year = {2021}, abstract = {We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we achieve state-of-the-art accuracies outperforming the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability.}, language = {en} } @article{TrepczynskiKneifelHeylandetal.2025, author = {Trepczynski, Adam and Kneifel, Paul and Heyland, Mark and Leskovar, Marko and Moewis, Philippe and Damm, Philipp and Taylor, William R. and Zachow, Stefan and Duda, Georg N.}, title = {Impact of the external knee flexion moment on patello-femoral loading derived from in vivo loads and kinematics}, volume = {12/2024}, journal = {Frontiers in Bioengineering and Biotechnology}, publisher = {Frontiers Media SA}, organization = {Charit{\´e} - Universit{\"a}tsmedizin Berlin}, issn = {2296-4185}, doi = {10.3389/fbioe.2024.1473951}, year = {2025}, abstract = {Introduction: Anterior knee pain and other patello-femoral (PF) complications frequently limit the success of total knee arthroplasty as the final treatment of end stage osteoarthritis. However, knowledge about the in-vivo loading conditions at the PF joint remains limited, as no direct measurements are available. We hypothesised that the external knee flexion moment (EFM) is highly predictive of the PF contact forces during activities with substantial flexion of the loaded knee. Materials and methods: Six patients (65-80 years, 67-101 kg) with total knee arthroplasty (TKA) performed two activities of daily living: sit-stand-sit and squat. Tibio-femoral (TF) contact forces were measured in vivo using instrumented tibial components, while synchronously internal TF and PF kinematics were captured with mobile fluoroscopy. The measurements were used to compute PF contact forces using patient specific musculoskeletal models. The relationship between the EFM and the PF contact force was quantified using linear regression. Results: Mean peak TF contact forces of 1.97-3.24 times body weight (BW) were found while peak PF forces reached 1.75 to 3.29 times body weight (BW). The peak EFM ranged from 3.2 to 5.9 \%BW times body height, and was a good predictor of the PF contact force (R2 = 0.95 and 0.88 for sit-stand-sit and squat, respectively). Discussion: The novel combination of in vivo TF contact forces and internal patellar kinematics enabled a reliable assessment of PF contact forces. The results of the regression analysis suggest that PF forces can be estimated based solely on the EFM from quantitative gait analysis. Our study also demonstrates the relevance of PF contact forces, which reach magnitudes similar to TF forces during activities of daily living.}, language = {en} } @article{KomnikFunkenZachowetal.2024, author = {Komnik, Igor and Funken, Johannes and Zachow, Stefan and Schmidt-Wiethoff, R{\"u}diger and Ellermann, Andree and Potthast, Wolfgang}, title = {Surgical planning in HTO - Alternative approaches to the Fujisawa gold-standard}, journal = {Technology and Health Care}, doi = {10.1177/09287329241299568}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-98227}, year = {2024}, abstract = {BACKGROUND: Presurgical planning of the correction angle plays a decisive role in a high tibial osteotomy, affecting the loading situation in the knee affected by osteoarthritis. The planning approach by Fujisawa et al. aims to adjust the weight-bearing line to achieve an optimal knee joint load distribution. While this method is accessible, it may not fully consider the complexity of individual dynamic knee-loading profiles. This review aims to disclose existing alternative HTO planning methods that do not follow Fujisawa's standard. METHODS: PubMed, Web of Science and CENTRAL databases were screened, focusing on HTO research in combination with alternative planning approaches. RESULTS: Eight out of 828 studies were included, with seven simulation studies based on finite element analysis and multi-body dynamics. The planning approaches incorporated gradual degrees of realignment parameters (weight-bearing line shift, medial proximal tibial angle, hip- knee-ankle, knee joint line orientation), simulating their effect on knee kinematics, contact force/stress, Von Mises and shear stress. Two studies proposed implementing individual correction magnitudes derived from preoperatively predicted knee adduction moments. CONCLUSION: Most planning methods depend on static alignment assessments, neglecting an adequate loading-depending profile. They are confined to their conceptual phases, making the associated planning methods unviable for current clinical use.}, language = {en} } @misc{StallingSeebassZachow1998, author = {Stalling, Detlev and Seebass, Martin and Zachow, Stefan}, title = {Mehrschichtige Oberfl{\"a}chenmodelle zur computergest{\"u}tzten Planung in der Chirurgie}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-5661}, number = {TR-98-05}, year = {1998}, abstract = {Polygonale Sch{\"a}delmodelle bilden ein wichtiges Hilfsmittel f{\"u}r computergest{\"u}tzte Planungen im Bereich der plastischen Chirurgie. Wir beschreiben, wie derartige Modelle automatisch aus hochaufgel{\"o}sten CT-Datens{\"a}tzen erzeugt werden k{\"o}nnen. Durch einen lokal steuerbaren Simplifizierungsalgorithmus werden die Modelle so weit vereinfacht, daß auch auf kleineren Graphikcomputern interaktives Arbeiten m{\"o}glich wird. Die Verwendung eines speziellen Transparenzmodells erm{\"o}glicht den ungehinderten Blick auf die bei der Planung relevanten Knochenstrukturen und l{\"a}ßt den Benutzer zugleich die Kopfumrisse des Patienten erkennen.}, language = {de} } @article{DeuflhardWeiserZachow2006, author = {Deuflhard, Peter and Weiser, Martin and Zachow, Stefan}, title = {Mathematics in Facial Surgery}, volume = {53}, journal = {AMS Notices}, number = {9}, pages = {1012 -- 1016}, year = {2006}, language = {en} } @article{ZinserZachowSailer2013, author = {Zinser, Max and Zachow, Stefan and Sailer, Hermann}, title = {Bimaxillary "rotation advancement" procedures in patients with obstructive sleep apnea: A 3-dimensional airway analysis of morphological changes}, volume = {42}, journal = {International Journal of Oral \& Maxillofacial Surgery}, number = {5}, doi = {10.1016/j.ijom.2012.08.002}, pages = {569 -- 578}, year = {2013}, language = {en} } @misc{DeuflhardZachow2012, author = {Deuflhard, Peter and Zachow, Stefan}, title = {Mathematische Therapie- und Operationsplanung}, publisher = {Berliner Wirtschaftsgespr{\"a}che e.V.}, address = {Berlin}, pages = {89 -- 90}, year = {2012}, language = {en} } @article{GallowayKahntRammetal.2013, author = {Galloway, Francis and Kahnt, Max and Ramm, Heiko and Worsley, Peter and Zachow, Stefan and Nair, Prasanth and Taylor, Mark}, title = {A large scale finite element study of a cementless osseointegrated tibial tray}, volume = {46}, journal = {Journal of Biomechanics}, number = {11}, doi = {/10.1016/j.jbiomech.2013.04.021}, pages = {1900 -- 1906}, year = {2013}, language = {en} } @misc{GreweLameckerZachow2013, author = {Grewe, Carl Martin and Lamecker, Hans and Zachow, Stefan}, title = {Landmark-based Statistical Shape Analysis}, journal = {Auxology - Studying Human Growth and Development url}, editor = {Hermanussen, Michael}, publisher = {Schweizerbart Verlag, Stuttgart}, pages = {199 -- 201}, year = {2013}, language = {en} } @article{EhlkeRammLameckeretal.2013, author = {Ehlke, Moritz and Ramm, Heiko and Lamecker, Hans and Hege, Hans-Christian and Zachow, Stefan}, title = {Fast Generation of Virtual X-ray Images for Reconstruction of 3D Anatomy}, volume = {19}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {12}, doi = {10.1109/TVCG.2013.159}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-35928}, pages = {2673 -- 2682}, year = {2013}, language = {en} } @inproceedings{HoffmannZachow2011, author = {Hoffmann, Ren{\´e} and Zachow, Stefan}, title = {Non-invasive approach to shed new light on the buoyancy business of chambered cephalopods (Mollusca)}, booktitle = {Proc. of the Intl. Assoc. for Mathematical Geosciences, Salzburg}, doi = {10.5242/iamg.2011.0163}, pages = {1 -- 11}, year = {2011}, language = {en} } @misc{GreweLameckerZachow2011, author = {Grewe, Carl Martin and Lamecker, Hans and Zachow, Stefan}, title = {Digital morphometry: The Potential of Statistical Shape Models}, journal = {Anthropologischer Anzeiger. Journal of Biological and Clinical Anthropology}, pages = {506 -- 506}, year = {2011}, language = {en} } @inproceedings{LameckerKainmuellerSeimetal.2010, author = {Lamecker, Hans and Kainm{\"u}ller, Dagmar and Seim, Heiko and Zachow, Stefan}, title = {Automatische 3D Rekonstruktion des Unterkiefers und der Mandibul{\"a}rnerven auf Basis dentaler Bildgebung}, volume = {55 (Suppl. 1)}, booktitle = {Proc. BMT, Biomed Tech}, publisher = {Walter de Gruyter-Verlag}, pages = {35 -- 36}, year = {2010}, language = {en} } @article{DworzakLameckervonBergetal.2009, author = {Dworzak, Jalda and Lamecker, Hans and von Berg, Jens and Klinder, Tobias and Lorenz, Cristian and Kainm{\"u}ller, Dagmar and Seim, Heiko and Hege, Hans-Christian and Zachow, Stefan}, title = {3D Reconstruction of the Human Rib Cage from 2D Projection Images using a Statistical Shape Model}, volume = {5}, journal = {Int. J. Comput. Assist. Radiol. Surg.}, number = {2}, publisher = {Springer}, issn = {1861-6410}, doi = {10.1007/s11548-009-0390-2}, pages = {111 -- 124}, year = {2009}, language = {en} } @article{RybakKussLameckeretal.2010, author = {Rybak, J{\"u}rgen and Kuß, Anja and Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian and Lienhard, Matthias and Singer, Jochen and Neubert, Kerstin and Menzel, Randolf}, title = {The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System}, volume = {4}, journal = {Front. Syst. Neurosci.}, number = {30}, doi = {10.3389/fnsys.2010.00030}, year = {2010}, language = {en} } @inproceedings{vonBergDworzakKlinderetal.2011, author = {von Berg, Jens and Dworzak, Jalda and Klinder, Tobias and Manke, Dirk and Lamecker, Hans and Zachow, Stefan and Lorenz, Cristian}, title = {Temporal Subtraction of Chest Radiographs Compensating Pose Differences}, booktitle = {SPIE Medical Imaging}, year = {2011}, language = {en} } @inproceedings{BindernagelKainmuellerSeimetal.2011, author = {Bindernagel, Matthias and Kainm{\"u}ller, Dagmar and Seim, Heiko and Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian}, title = {An Articulated Statistical Shape Model of the Human Knee}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2011}, publisher = {Springer}, doi = {10.1007/978-3-642-19335-4_14}, pages = {59 -- 63}, year = {2011}, language = {en} } @inproceedings{KahntGallowaySeimetal.2011, author = {Kahnt, Max and Galloway, Francis and Seim, Heiko and Lamecker, Hans and Taylor, Mark and Zachow, Stefan}, title = {Robust and Intuitive Meshing of Bone-Implant Compounds}, booktitle = {CURAC}, address = {Magdeburg}, pages = {71 -- 74}, year = {2011}, language = {en} } @misc{SKGBSetal.2011, author = {SK, Saevarsson and GB, Sharma and S, Montgomery and KCT, Ho and Ramm, Heiko and Lieck, Robert and Zachow, Stefan and C, Anglin}, title = {Kinematic Comparison Between Gender Specific and Traditional Femoral Implants}, journal = {Proceedings of the 11th Alberta Biomedical Engineering (BME) Conference (Poster)}, pages = {80}, year = {2011}, language = {en} } @incollection{DeuflhardDoesselLouisetal.2010, author = {Deuflhard, Peter and D{\"o}ssel, Olaf and Louis, Alfred and Zachow, Stefan}, title = {More Mathematics into Medicine!}, booktitle = {Production Factor Mathematics}, publisher = {Springer}, pages = {357 -- 378}, year = {2010}, language = {en} } @inproceedings{SeimKainmuellerLameckeretal.2010, author = {Seim, Heiko and Kainm{\"u}ller, Dagmar and Lamecker, Hans and Bindernagel, Matthias and Malinowski, Jana and Zachow, Stefan}, title = {Model-based Auto-Segmentation of Knee Bones and Cartilage in MRI Data}, booktitle = {Proc. MICCAI Workshop Medical Image Analysis for the Clinic}, editor = {v. Ginneken, B.}, pages = {215 -- 223}, year = {2010}, language = {en} } @article{WeiserZachowDeuflhard2010, author = {Weiser, Martin and Zachow, Stefan and Deuflhard, Peter}, title = {Craniofacial Surgery Planning Based on Virtual Patient Models}, volume = {52}, journal = {it - Information Technology}, number = {5}, publisher = {Oldenbourg Verlagsgruppe}, doi = {10.1524/itit.2010.0600}, pages = {258 -- 263}, year = {2010}, language = {en} } @incollection{ZachowHahnLange2010, author = {Zachow, Stefan and Hahn, Horst and Lange, Thomas}, title = {Computerassistierte Chirugieplanung}, booktitle = {Computerassistierte Chirurgie}, editor = {Schlag, Peter and Eulenstein, Sebastian and Lange, Thomas}, publisher = {Elsevier}, pages = {119 -- 149}, year = {2010}, language = {en} } @inproceedings{KainmuellerLameckerSeimetal.2010, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Seim, Heiko and Zachow, Stefan and Hege, Hans-Christian}, title = {Improving Deformable Surface Meshes through Omni-directional Displacements and MRFs}, volume = {6361}, booktitle = {Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI)}, editor = {Navab, Tianzi and P. W. Pluim, Josien and Viergever, Max}, publisher = {Springer}, doi = {10.1007/978-3-642-15705-9_28}, pages = {227 -- 234}, year = {2010}, language = {en} } @inproceedings{ZachowKubiackMalinowskietal.2010, author = {Zachow, Stefan and Kubiack, Kim and Malinowski, Jana and Lamecker, Hans and Essig, Harald and Gellrich, Nils-Claudius}, title = {Modellgest{\"u}tzte chirurgische Rekonstruktion komplexer Mittelgesichtsfrakturen}, volume = {55 (Suppl 1)}, booktitle = {Proc. BMT, Biomed Tech 2010}, publisher = {Walter de Gruyter-Verlag}, pages = {107 -- 108}, year = {2010}, language = {de} } @misc{BindernagelKainmuellerRammetal.2012, author = {Bindernagel, Matthias and Kainm{\"u}ller, Dagmar and Ramm, Heiko and Lamecker, Hans and Zachow, Stefan}, title = {Analysis of inter-individual anatomical shape variations of joint structures}, journal = {Proc. Int. Society of Computer Assisted Orthopaedic Surgery (CAOS)}, number = {210}, year = {2012}, language = {en} } @inproceedings{NguyenLameckerKainmuelleretal.2012, author = {Nguyen, The Duy and Lamecker, Hans and Kainm{\"u}ller, Dagmar and Zachow, Stefan}, title = {Automatic Detection and Classification of Teeth in CT Data}, volume = {7510}, booktitle = {Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI)}, editor = {Ayache, Nicholas and Delingette, Herv{\´e} and Golland, Polina and Mori, Kensaku}, pages = {609 -- 616}, year = {2012}, language = {en} } @article{NguyenKainmuellerLameckeretal.2012, author = {Nguyen, The Duy and Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan}, title = {Automatic bone and tooth detection for CT-based dental implant planning}, volume = {7, Supplement 1}, journal = {Int. J. Computer Assisted Radiology and Surgery}, number = {1}, publisher = {Springer}, pages = {293 -- 294}, year = {2012}, language = {en} } @misc{EhlkeRammLameckeretal.2012, author = {Ehlke, Moritz and Ramm, Heiko and Lamecker, Hans and Zachow, Stefan}, title = {Efficient projection and deformation of volumetric shape and intensity models for accurate simulation of X-ray images}, journal = {Eurographics Workshop on Visual Computing for Biomedicine (NVIDIA best poster award)}, year = {2012}, language = {en} } @article{RammKahntZachow2012, author = {Ramm, Heiko and Kahnt, Max and Zachow, Stefan}, title = {Patientenspezifische Simulationsmodelle f{\"u}r die funktionelle Analyse von k{\"u}nstlichem Gelenkersatz}, volume = {3}, journal = {Computer Aided Medical Engineering (CaMe)}, number = {2}, pages = {30 -- 36}, year = {2012}, language = {de} } @inproceedings{KahntRammLameckeretal.2012, author = {Kahnt, Max and Ramm, Heiko and Lamecker, Hans and Zachow, Stefan}, title = {Feature-Preserving, Multi-Material Mesh Generation using Hierarchical Oracles}, volume = {7599}, booktitle = {Proc. MICCAI Workshop on Mesh Processing in Medical Image Analysis (MeshMed)}, editor = {Levine, Joshua A. and Paulsen, Rasmus R. and Zhang, Yongjie}, pages = {101 -- 111}, year = {2012}, language = {en} } @incollection{RammZachow2012, author = {Ramm, Heiko and Zachow, Stefan}, title = {Computergest{\"u}tzte Planung f{\"u}r die individuelle Implantatversorgung}, volume = {16}, booktitle = {Health Academy}, editor = {Niederlag, Wolfgang and Lemke, Heinz and Peitgen, Heinz-Otto and Lehrach, Hans}, pages = {145 -- 158}, year = {2012}, language = {de} } @inproceedings{NeugebauerJanigaZachowetal.2008, author = {Neugebauer, Mathias and Janiga, Gabor and Zachow, Stefan and Krischek, {\"O}zlem and Preim, Bernhard}, title = {Generierung qualitativ hochwertiger Modelle f{\"u}r die Simulation von Blutfluss in zerebralen Aneurysmen}, booktitle = {Proc. of Simulation and Visualization 2008}, editor = {Hauser, Helwig}, pages = {221 -- 235}, year = {2008}, language = {en} } @inproceedings{DornheimBornZachowetal.2008, author = {Dornheim, Jana and Born, Silvia and Zachow, Stefan and Gessat, Michael and Wellein, Daniela and Strauß, Gero and Preim, Bernhard and Bartz, Dirk}, title = {Bildanalyse, Visualisierung und Modellerstellung f{\"u}r die Implantatplanung im Mittelohr}, booktitle = {Proc. of Simulation and Visualization 2008}, editor = {Hauser, Helwig}, pages = {139 -- 154}, year = {2008}, language = {en} } @article{SteinmannBartschZachowetal.2008, author = {Steinmann, Alexander and Bartsch, Peter and Zachow, Stefan and Hildebrandt, Thomas}, title = {Breathing Easily: Simulation of airflow in human noses can become a useful rhinosurgery planning tool}, volume = {Vol. II, No. 1}, journal = {ANSYS Advantage}, pages = {30 -- 31}, year = {2008}, language = {en} } @inproceedings{KainmuellerLameckerSeimetal.2009, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Seim, Heiko and Zinser, Max and Zachow, Stefan}, title = {Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data}, booktitle = {Proceedings of Medical Image Computing and Computer Assisted Intervention (MICCAI)}, editor = {Yang, Guang-Zhong and J. Hawkes, David and Rueckert, Daniel and Noble, J. Alison and J. Taylor, Chris}, address = {London, UK}, pages = {76 -- 83}, year = {2009}, language = {en} } @inproceedings{KainmuellerLameckerZachowetal.2009, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian}, title = {An Articulated Statistical Shape Model for Accurate Hip Joint Segmentation}, booktitle = {EBMC 2009. Int. Conf. of the IEEE Eng. in Med. and Biol. Society (EMBC)}, address = {Minneapolis, USA}, pages = {6345 -- 6351}, year = {2009}, language = {en} } @inproceedings{SeimKainmuellerHelleretal.2009, author = {Seim, Heiko and Kainm{\"u}ller, Dagmar and Heller, Markus O. and Zachow, Stefan and Hege, Hans-Christian}, title = {Automatic Extraction of Anatomical Landmarks from Medical Image Data: An Evaluation of Different Methods}, booktitle = {Proc. of IEEE Int. Symposium on Biomedical Imaging (ISBI)}, address = {Boston, MA, USA}, pages = {538 -- 541}, year = {2009}, language = {en} } @article{ZachowLameckerZoeckleretal.2009, author = {Zachow, Stefan and Lamecker, Hans and Z{\"o}ckler, Maja and Haberl, Ernst}, title = {Computergest{\"u}tzte Planung zur chirurgischen Korrektur von fr{\"u}hkindlichen Sch{\"a}delfehlbildungen (Craniosynostosen)}, journal = {Face 02/09, Int. Mag. of Orofacial Esthetics, Oemus Journale Leipzig}, pages = {48 -- 53}, year = {2009}, language = {en} } @article{KainmuellerLameckerZachow2009, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan}, title = {Multi-object Segmentation with Coupled Deformable Models}, volume = {5}, journal = {Annals of the British Machine Vision Association (BMVA)}, pages = {1 -- 10}, year = {2009}, language = {en} } @inproceedings{KainmuellerLameckerZachowetal.2008, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian}, title = {Coupling Deformable Models for Multi-object Segmentation}, booktitle = {Proc. Int. Symp. on Computational Models for Biomedical Simulation (ISBMS)}, doi = {10.1007/978-3-540-70521-5_8}, pages = {69 -- 78}, year = {2008}, language = {en} } @inproceedings{KainmuellerLameckerZachowetal.2008, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan and Heller, Markus O. and Hege, Hans-Christian}, title = {Multi-Object Segmentation with Coupled Deformable Models}, booktitle = {Proc. Medical Image Understanding and Analysis}, pages = {34 -- 38}, year = {2008}, language = {en} } @inproceedings{SeimKainmuellerKussetal.2008, author = {Seim, Heiko and Kainm{\"u}ller, Dagmar and Kuss, Anja and Lamecker, Hans and Zachow, Stefan and Menzel, Randolf and Rybak, Juergen}, title = {Model-based autosegmentation of the central brain of the honeybee, Apis mellifera, using active statistical shape models}, booktitle = {Proc. 1st INCF Congress of Neuroinformatics: Databasing and Modeling the Brain}, doi = {10.3389/conf.neuro.11.2008.01.064}, year = {2008}, language = {en} } @inproceedings{DworzakLameckervonBergetal.2008, author = {Dworzak, Jalda and Lamecker, Hans and von Berg, Jens and Klinder, Tobias and Lorenz, Cristian and Kainm{\"u}ller, Dagmar and Seim, Heiko and Hege, Hans-Christian and Zachow, Stefan}, title = {Towards model-based 3-D reconstruction of the human rib cage from radiographs}, booktitle = {Proc. 7. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer-Roboterassistierte Chirurgie (CURAC)}, pages = {193 -- 196}, year = {2008}, language = {en} } @inproceedings{SeimKainmuellerHelleretal.2008, author = {Seim, Heiko and Kainm{\"u}ller, Dagmar and Heller, Markus O. and Lamecker, Hans and Zachow, Stefan and Hege, Hans-Christian}, title = {Automatic Segmentation of the Pelvic Bones from CT Data Based on a Statistical Shape Model}, booktitle = {Eurographics Workshop on Visual Computing for Biomedicine (VCBM)}, address = {Delft, Netherlands}, pages = {93 -- 100}, year = {2008}, language = {en} } @incollection{DeuflhardDoesselLouisetal.2008, author = {Deuflhard, Peter and D{\"o}ssel, Olaf and Louis, Alfred and Zachow, Stefan}, title = {Mehr Mathematik wagen in der Medizin}, booktitle = {acatech diskutiert, Produktionsfaktor Mathematik - Wie Mathematik Technik und Wirtschaft bewegt}, publisher = {Springer}, doi = {10.1007/978-3-540-89435-3}, pages = {435 -- 459}, year = {2008}, language = {en} } @article{ZachowDeuflhard2008, author = {Zachow, Stefan and Deuflhard, Peter}, title = {Computergest{\"u}tzte Planung in der kraniofazialen Chirurgie}, journal = {Face 01/08, Int. Mag. of Orofacial Esthetics}, publisher = {Oemus Journale Leipzig}, pages = {43 -- 49}, year = {2008}, language = {en} } @inproceedings{ZilskeLameckerZachow2008, author = {Zilske, Michael and Lamecker, Hans and Zachow, Stefan}, title = {Adaptive Remeshing of Non-Manifold Surfaces}, booktitle = {Eurographics 2008 Annex to the Conf. Proc.}, pages = {207 -- 211}, year = {2008}, language = {en} } @inproceedings{SeimLameckerZachow2008, author = {Seim, Heiko and Lamecker, Hans and Zachow, Stefan}, title = {Segmentation of Bony Structures with Ligament Attachment Sites}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2008}, publisher = {Springer}, doi = {10.1007/978-3-540-78640-5_42}, pages = {207 -- 211}, year = {2008}, language = {en} } @article{ZachowSteinmannHildebrandtetal.2007, author = {Zachow, Stefan and Steinmann, Alexander and Hildebrandt, Thomas and Heppt, Werner}, title = {Understanding nasal airflow via CFD simulation and visualization}, journal = {Proc. Computer Aided Surgery around the Head}, pages = {173 -- 176}, year = {2007}, language = {en} } @article{LameckerKamerWittmersetal.2007, author = {Lamecker, Hans and Kamer, Lukas and Wittmers, Antonia and Zachow, Stefan and Kaup, Thomas and Schramm, Alexander and Noser, Hansrudi and Hammer, Beat}, title = {A method for the three-dimensional statistical shape analysis of the bony orbit}, journal = {Proc. Computer Aided Surgery around the Head}, pages = {94 -- 97}, year = {2007}, language = {en} } @article{ZachowMuiggHildebrandtetal.2009, author = {Zachow, Stefan and Muigg, Philipp and Hildebrandt, Thomas and Doleisch, Helmut and Hege, Hans-Christian}, title = {Visual Exploration of Nasal Airflow}, volume = {15}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = {8}, doi = {10.1109/TVCG.2009.198}, pages = {1407 -- 1414}, year = {2009}, language = {en} } @article{KainmuellerLameckerSeimetal.2009, author = {Kainm{\"u}ller, Dagmar and Lamecker, Hans and Seim, Heiko and Zachow, Stefan}, title = {Multi-object segmentation of head bones}, journal = {MIDAS Journal}, year = {2009}, language = {en} } @inproceedings{SeimKainmuellerLameckeretal.2009, author = {Seim, Heiko and Kainm{\"u}ller, Dagmar and Lamecker, Hans and Zachow, Stefan}, title = {A System for Unsupervised Extraction of Orthopaedic Parameters from CT Data}, booktitle = {GI Workshop Softwareassistenten - Computerunterst{\"u}tzung f{\"u}r die medizinische Diagnose und Therapieplanung}, address = {L{\"u}beck, Germany}, pages = {1328 -- 1337}, year = {2009}, language = {en} } @article{HepptHildebrandtSteinmannetal.2007, author = {Heppt, Werner and Hildebrandt, Thomas and Steinmann, Alexander and Zachow, Stefan}, title = {Aesthetic and Function in Rhinoplasty}, volume = {264 (Suppl 1), RL 126}, journal = {Springer Journal}, pages = {307}, year = {2007}, language = {en} } @inproceedings{ZachowZilskeHege2007, author = {Zachow, Stefan and Zilske, Michael and Hege, Hans-Christian}, title = {3D Reconstruction of Individual Anatomy from Medical Image Data: Segmentation and Geometry Processing}, booktitle = {25. ANSYS Conference \& CADFEM Users' Meeting}, address = {Dresden}, year = {2007}, language = {en} } @article{GessatZachowBurgertetal.2007, author = {Gessat, Michael and Zachow, Stefan and Burgert, Oliver and Lemke, Heinz}, title = {Geometric Meshes in Medical Applications - Steps towards a specification of Geometric Models in DICOM}, journal = {Int. J. of Computer Assisted Radiology and Surgery (CARS)}, doi = {10.1007/s11548-007-0112-6}, pages = {440 -- 442}, year = {2007}, language = {en} } @article{HildebrandtZachowSteinmannetal.2007, author = {Hildebrandt, Thomas and Zachow, Stefan and Steinmann, Alexander and Heppt, Werner}, title = {Innovation in der Funktionell-{\"A}sthetischen Nasenchirurgie: Rhino-CFD}, journal = {Face, Int. Mag. of Orofacial Esthetics}, publisher = {Oemus Journale Leipzig}, pages = {20 -- 23}, year = {2007}, language = {en} } @misc{KamerNoserLameckeretal.2006, author = {Kamer, Lukas and Noser, Hansrudi and Lamecker, Hans and Zachow, Stefan and Wittmers, Antonia and Kaup, Thomas and Schramm, Alexander and Hammer, Beat}, title = {Three-dimensional statistical shape analysis - A useful tool for developing a new type of orbital implant?}, publisher = {AO Development Institute, New Products Brochure 2/06}, pages = {20 -- 21}, year = {2006}, language = {en} } @inproceedings{ZachowHegeDeuflhard2004, author = {Zachow, Stefan and Hege, Hans-Christian and Deuflhard, Peter}, title = {Computergest{\"u}tzte Operationsplanung in der Gesichtschirurgie}, booktitle = {Proc. VDE Kongress 2004 - Innovationen f{\"u}r Menschen, Band 2, Fachtagungsberichte DGBMT - GMM - GMA}, pages = {53 -- 58}, year = {2004}, language = {en} } @inproceedings{ZachowHierlErdmann2004, author = {Zachow, Stefan and Hierl, Thomas and Erdmann, Bodo}, title = {A quantitative evaluation of 3D soft tissue prediction in maxillofacial surgery planning}, booktitle = {Proc. 3. Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboter-assistierte Chirurgie e.V.}, address = {M{\"u}nchen}, year = {2004}, language = {en} } @article{HierlZachowWollnyetal.2004, author = {Hierl, Thomas and Zachow, Stefan and Wollny, Gert and Berti, Guntram and Schmidt, Jens-Georg and Fingberg, Jochen}, title = {Computer-based simulation in distraction osteogenesis}, journal = {Proc. of 3rd Int. Oxford Distraction Symposium of the Craniofacial Skeleton}, pages = {33 -- 34}, year = {2004}, language = {en} } @article{HierlZachowWollnyetal.2004, author = {Hierl, Thomas and Zachow, Stefan and Wollny, Gert and Berti, Guntram and Schmidt, Jens-Georg and Fingberg, Jochen}, title = {Concepts of computer-based simulation in orthognathic surgery}, journal = {Journal of Cranio-Maxillofacial Surgery 32 Suppl. 1}, pages = {81 -- 82}, year = {2004}, language = {en} } @article{HaberlHellZoeckleretal.2004, author = {Haberl, Hannes and Hell, Bertold and Z{\"o}ckler, Maja and Zachow, Stefan and Lamecker, Hans and Sarrafzadeh, Asita and Riecke, B. and Langsch, Wolfgang and Deuflhard, Peter and Bier, J{\"u}rgen and Brock, Mario}, title = {Technical aspects and results of surgery for craniosynostosis}, volume = {65}, journal = {Zentralblatt f{\"u}r Neurochirurgie}, number = {2}, pages = {65 -- 74}, year = {2004}, language = {en} } @article{NkenkeZachowBenzetal.2004, author = {Nkenke, Emeka and Zachow, Stefan and Benz, Michaela and Maier, Tobias and Veit, Klaus and Kramer, Manuel and Benz, St. and H{\"a}usler, Gerd and Neukam, Friedrich and Lell, Michael}, title = {Fusion of computed tomography data and optical 3D images of the dentition for streak artefact correction in the simulation of orthognathic surgery}, volume = {33}, journal = {Journal of Dento-Maxillofacial Radiology}, doi = {10.1259/dmfr/27071199}, pages = {226 -- 232}, year = {2004}, language = {en} } @inproceedings{ZachowHierlErdmann2004, author = {Zachow, Stefan and Hierl, Thomas and Erdmann, Bodo}, title = {On the Predictability of tissue changes after osteotomy planning in maxillofacial surgery}, booktitle = {Computer Assisted Radiology and Surgery (CARS)}, address = {Chicago, USA}, doi = {10.1016/j.ics.2004.03.043}, pages = {648 -- 653}, year = {2004}, language = {en} } @inproceedings{ZachowHierlErdmann2004, author = {Zachow, Stefan and Hierl, Thomas and Erdmann, Bodo}, title = {{\"U}ber die Genauigkeit einer 3D Weichgewebepr{\"a}diktion in der MKG-Cirurgie}, booktitle = {Workshop 'Bildverarbeitung f{\"u}r die Medizin' (BVM)}, address = {Berlin, Germany}, pages = {75 -- 79}, year = {2004}, language = {en} } @inproceedings{LameckerZoecklerHaberletal.2005, author = {Lamecker, Hans and Z{\"o}ckler, Maja and Haberl, Hannes and Zachow, Stefan and Hege, Hans-Christian}, title = {Statistical shape modeling for craniosynostosis planning}, booktitle = {2nd International Conference Advanced Digital Technology in Head and Neck Reconstruction 2005, Abstract Volume}, address = {Banff, Alberta}, pages = {64}, year = {2005}, language = {en} } @article{WestermarkZachowEppley2005, author = {Westermark, Anders and Zachow, Stefan and Eppley, Barry}, title = {3D osteotomy planning in maxillofacial surgery, including 3D soft tissue prediction}, volume = {16(1)}, journal = {Journal of Craniofacial Surgery}, pages = {100 -- 104}, year = {2005}, language = {en} } @article{ZachowHegeDeuflhard2005, author = {Zachow, Stefan and Hege, Hans-Christian and Deuflhard, Peter}, title = {Maxillofacial surgery planning with 3D soft tissue prediction - modeling, planning, simulation}, journal = {2. Int. Conf. on Advanced Digital Technology in Head and Neck Reconstruction, Abstract 33}, address = {Banff, Alberta, CA}, pages = {64}, year = {2005}, language = {en} } @article{LameckerZachowHaberletal.2005, author = {Lamecker, Hans and Zachow, Stefan and Haberl, Hannes and Stiller, Michael}, title = {Medical applications for statistical shape models}, volume = {17 (258)}, journal = {Computer Aided Surgery around the Head, Fortschritt-Berichte VDI - Biotechnik/Medizintechnik}, pages = {61}, year = {2005}, language = {en} } @inproceedings{ZachowLameckerElsholtzetal.2005, author = {Zachow, Stefan and Lamecker, Hans and Elsholtz, Barbara and Stiller, Michael}, title = {Reconstruction of mandibular dysplasia using a statistical 3D shape model}, booktitle = {Proc. Computer Assisted Radiology and Surgery (CARS)}, address = {Berlin, Germany}, doi = {10.1016/j.ics.2005.03.339}, pages = {1238 -- 1243}, year = {2005}, language = {en} } @inproceedings{NkenkeHaeuslerNeukametal.2005, author = {Nkenke, Emeka and H{\"a}usler, Gerd and Neukam, Friedrich and Zachow, Stefan}, title = {Streak artifact correction of CT data by optical 3D imaging in the simulation of orthognathic surgery}, booktitle = {Computer Assisted Radiology and Surgery (CARS)}, address = {Berlin Germany}, doi = {doi:10.1016/j.ics.2005.03.278}, year = {2005}, language = {en} } @inproceedings{NkenkeZachowHaeusler2005, author = {Nkenke, Emeka and Zachow, Stefan and H{\"a}usler, Gerd}, title = {Fusion von optischen 3D- und CT-Daten des Gebisses zur Metallartefaktkorrektur vor computerassistierter Planung MKG-chirurgischer Eingriffe}, booktitle = {Symposium der Arbeitsgemeinschaf f{\"u}r Kieferchirurgie}, address = {Bad Homburg v.d.H}, year = {2005}, language = {en} } @misc{WilsonBuecherGreweetal.2015, author = {Wilson, David and B{\"u}cher, Pia and Grewe, Carl Martin and Anglin, Carolyn and Zachow, Stefan and Michael, Dunbar}, title = {Validation of Three Dimensional Models of the Distal Femur Created from Surgical Navigation Point Cloud Data}, journal = {15th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery (CAOS)}, year = {2015}, language = {en} }