@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}, series = {IEEE Transactions on Visualization and Computer Graphics}, 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} } @misc{Ehlke2012, type = {Master Thesis}, author = {Ehlke, Moritz}, title = {Simulating X-ray images from deformable shape and intensity models on the GPU}, year = {2012}, 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}, series = {BMC Evolutionary Biology}, 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} } @misc{LamasRodriguezEhlkeHoffmannetal., author = {Lamas-Rodr{\´i}guez, Juli{\´a}n and Ehlke, Moritz and Hoffmann, Ren{\´e} and Zachow, Stefan}, title = {GPU-accelerated denoising of large tomographic data sets with low SNR}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-56339}, abstract = {Enhancements in tomographic imaging techniques facilitate non-destructive methods for visualizing fossil structures. However, to penetrate dense materials such as sediments or pyrites, image acquisition is typically performed with high beam energy and very sensitive image intensifiers, leading to artifacts and noise in the acquired data. The analysis of delicate fossil structures requires the images to be captured in maximum resolution, resulting in large data sets of several giga bytes (GB) in size. Since the structural information of interest is often almost in the same spatial range as artifacts and noise, image processing and segmentation algorithms have to cope with a very low signal-to-noise ratio (SNR). Within this report we present a study on the performance of a collection of denoising algorithms applied to a very noisy fossil dataset. The study shows that a non-local means (NLM) filter, in case it is properly configured, is able to remove a considerable amount of noise while preserving most of the structural information of interest. Based on the results of this study, we developed a software tool within ZIBAmira that denoises large tomographic datasets using an adaptive, GPU-accelerated NLM filter. With the help of our implementation a user can interactively configure the filter's parameters and thus its effectiveness with respect to the data of interest, while the filtering response is instantly visualized for a preselected region of interest (ROI). Our implementation efficiently denoises even large fossil datasets in a reasonable amount of time.}, 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}, series = {Eurographics Workshop on Visual Computing for Biomedicine (NVIDIA best poster award)}, journal = {Eurographics Workshop on Visual Computing for Biomedicine (NVIDIA best poster award)}, year = {2012}, language = {en} } @inproceedings{EhlkeFrenzelRammetal., author = {Ehlke, Moritz and Frenzel, Thomas and Ramm, Heiko and Shandiz, Mohsen Akbari and Anglin, Carolyn and Zachow, Stefan}, title = {Towards Robust Measurement Of Pelvic Parameters From AP Radiographs Using Articulated 3D Models}, series = {Computer Assisted Radiology and Surgery (CARS)}, booktitle = {Computer Assisted Radiology and Surgery (CARS)}, abstract = {Patient-specific parameters such as the orientation of the acetabulum or pelvic tilt are useful for custom planning for total hip arthroplasty (THA) and for evaluating the outcome of surgical interventions. The gold standard in obtaining pelvic parameters is from three-dimensional (3D) computed tomography (CT) imaging. However, this adds time and cost, exposes the patient to a substantial radiation dose, and does not allow for imaging under load (e.g. while the patient is standing). If pelvic parameters could be reliably derived from the standard anteroposterior (AP) radiograph, preoperative planning would be more widespread, and research analyses could be applied to retrospective data, after a postoperative issue is discovered. The goal of this work is to enable robust measurement of two surgical parameters of interest: the tilt of the anterior pelvic plane (APP) and the orientation of the natural acetabulum. We present a computer-aided reconstruction method to determine the APP and natural acetabular orientation from a single, preoperative X-ray. It can easily be extended to obtain other important preoperative and postoperative parameters solely based on a single AP radiograph.}, language = {en} } @misc{EhlkeFrenzelRammetal., author = {Ehlke, Moritz and Frenzel, Thomas and Ramm, Heiko and Shandiz, Mohsen Akbari and Anglin, Carolyn and Zachow, Stefan}, title = {Towards Robust Measurement of Pelvic Parameters from AP Radiographs using Articulated 3D Models}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-53707}, abstract = {Patient-specific parameters such as the orientation of the acetabulum or pelvic tilt are useful for custom planning for total hip arthroplasty (THA) and for evaluating the outcome of surgical interventions. The gold standard in obtaining pelvic parameters is from three-dimensional (3D) computed tomography (CT) imaging. However, this adds time and cost, exposes the patient to a substantial radiation dose, and does not allow for imaging under load (e.g. while the patient is standing). If pelvic parameters could be reliably derived from the standard anteroposterior (AP) radiograph, preoperative planning would be more widespread, and research analyses could be applied to retrospective data, after a postoperative issue is discovered. The goal of this work is to enable robust measurement of two surgical parameters of interest: the tilt of the anterior pelvic plane (APP) and the orientation of the natural acetabulum. We present a computer-aided reconstruction method to determine the APP and natural acetabular orientation from a single, preoperative X-ray. It can easily be extended to obtain other important preoperative and postoperative parameters solely based on a single AP radiograph.}, language = {en} } @phdthesis{Ehlke2020, author = {Ehlke, Moritz}, title = {3D Reconstruction of Anatomical Structures from 2D X-ray Images}, doi = {https://doi.org/10.14279/depositonce-11553}, year = {2020}, language = {en} } @inproceedings{EstacioEhlkeTacketal., 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}, series = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, booktitle = {2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)}, doi = {10.1109/ISBI48211.2021.9434091}, pages = {367 -- 370}, 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{PimentelSzengelEhlkeetal., 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}, series = {Towards the Automatization of Cranial Implant Design in Cranioplasty}, 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}, 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} } @inproceedings{AmbellanTackEhlkeetal., 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}, series = {Medical Imaging with Deep Learning}, booktitle = {Medical Imaging with Deep Learning}, 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} } @misc{AmbellanTackEhlkeetal., 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}, 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} } @misc{AmbellanTackEhlkeetal., 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)}, series = {Medical Image Analysis}, volume = {52}, journal = {Medical Image Analysis}, number = {2}, doi = {10.12752/4.ATEZ.1.0}, pages = {109 -- 118}, 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., 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}, series = {Medical Image Analysis}, volume = {52}, journal = {Medical Image Analysis}, number = {2}, doi = {10.1016/j.media.2018.11.009}, pages = {109 -- 118}, 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{LiPimentelSzengeletal., author = {Li, Jianning and Pimentel, Pedro and Szengel, Angelika and Ehlke, Moritz and Lamecker, Hans and Zachow, Stefan and Estacio, Laura and Doenitz, Christian and Ramm, Heiko and Shi, Haochen and Chen, Xiaojun and Matzkin, Franco and Newcombe, Virginia and Ferrante, Enzo and Jin, Yuan and Ellis, David G. and Aizenberg, Michele R. and Kodym, Oldrich and Spanel, Michal and Herout, Adam and Mainprize, James G. and Fishman, Zachary and Hardisty, Michael R. and Bayat, Amirhossein and Shit, Suprosanna and Wang, Bomin and Liu, Zhi and Eder, Matthias and Pepe, Antonio and Gsaxner, Christina and Alves, Victor and Zefferer, Ulrike and von Campe, Cord and Pistracher, Karin and Sch{\"a}fer, Ute and Schmalstieg, Dieter and Menze, Bjoern H. and Glocker, Ben and Egger, Jan}, title = {AutoImplant 2020 - First MICCAI Challenge on Automatic Cranial Implant Design}, series = {IEEE Transactions on Medical Imaging}, volume = {40}, journal = {IEEE Transactions on Medical Imaging}, number = {9}, issn = {0278-0062}, doi = {10.1109/TMI.2021.3077047}, pages = {2329 -- 2342}, abstract = {The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use.}, language = {en} } @misc{EhlkeRammLameckeretal., author = {Ehlke, Moritz and Ramm, Heiko and Lamecker, Hans and Hege, Hans-Christian and Zachow, Stefan}, title = {Fast Generation of Virtual X-ray Images from Deformable Tetrahedral Meshes}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-41896}, abstract = {We propose a novel GPU-based approach to render virtual X-ray projections of deformable tetrahedral meshes. These meshes represent the shape and the internal density distribution of a particular anatomical structure and are derived from statistical shape and intensity models (SSIMs). We apply our method to improve the geometric reconstruction of 3D anatomy (e.g.\ pelvic bone) from 2D X-ray images. For that purpose, shape and density of a tetrahedral mesh are varied and virtual X-ray projections are generated within an optimization process until the similarity between the computed virtual X-ray and the respective anatomy depicted in a given clinical X-ray is maximized. The OpenGL implementation presented in this work deforms and projects tetrahedral meshes of high resolution (200.000+ tetrahedra) at interactive rates. It generates virtual X-rays that accurately depict the density distribution of an anatomy of interest. Compared to existing methods that accumulate X-ray attenuation in deformable meshes, our novel approach significantly boosts the deformation/projection performance. The proposed projection algorithm scales better with respect to mesh resolution and complexity of the density distribution, and the combined deformation and projection on the GPU scales better with respect to the number of deformation parameters. The gain in performance allows for a larger number of cycles in the optimization process. Consequently, it reduces the risk of being stuck in a local optimum. We believe that our approach contributes in orthopedic surgery, where 3D anatomy information needs to be extracted from 2D X-rays to support surgeons in better planning joint replacements.}, language = {en} } @misc{EhlkeFrenzelRammetal., author = {Ehlke, Moritz and Frenzel, Thomas and Ramm, Heiko and Lamecker, Hans and Akbari Shandiz, Mohsen and Anglin, Carolyn and Zachow, Stefan}, title = {Robust Measurement of Natural Acetabular Orientation from AP Radiographs using Articulated 3D Shape and Intensity Models}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-49824}, language = {en} } @misc{EhlkeRammLameckeretal., author = {Ehlke, Moritz and Ramm, Heiko and Lamecker, Hans and Zachow, Stefan}, title = {Efficient projection and deformation of volumetric intensity models for accurate simulation of X-ray images}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-16580}, abstract = {We present an efficient GPU-based method to generate virtual X-ray images from tetrahedral meshes which are associated with attenuation values. In addition, a novel approach is proposed that performs the model deformation on the GPU. The tetrahedral grids are derived from volumetric statistical shape and intensity models (SSIMs) and describe anatomical structures. Our research targets at reconstructing 3D anatomical shapes by comparing virtual X-ray images generated using our novel approach with clinical data while varying the shape and density of the SSIM in an optimization process. We assume that a deformed SSIM adequately represents an anatomy of interest when the similarity between the virtual and the clinical X-ray image is maximized. The OpenGL implementation presented here generates accurate (virtual) X-ray images at interactive rates, thus qualifying it for its use in the reconstruction process.}, language = {en} } @article{SekuboyinaHusseiniBayatetal., author = {Sekuboyina, Anjany and Husseini, Malek E. and Bayat, Amirhossein and L{\"o}ffler, Maximilian and Liebl, Hans 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 Brown, Kevin and Kirszenberg, Alexandre and Puybareau, {\´E}lodie and Chen, Di and Bai, Yiwei and Rapazzo, Brandon H. and Yeah, Timyoas and Zhang, Amber and Xu, Shangliang and Hou, Feng and He, Zhiqiang and Zeng, Chan and Xiangshang, Zheng and Liming, Xu and Netherton, Tucker J. and Mumme, Raymond P. and Court, Laurence E. and Huang, Zixun and He, Chenhang and Wang, Li-Wen and Ling, Sai Ho and Huynh, L{\^e} Duy and Boutry, Nicolas and Jakubicek, Roman and Chmelik, Jiri and Mulay, Supriti and Sivaprakasam, Mohanasankar and Paetzold, Johannes C. and Shit, Suprosanna and Ezhov, Ivan and Wiestler, Benedikt and Glocker, Ben 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}, series = {Medical Image Analysis}, volume = {73}, journal = {Medical Image Analysis}, doi = {10.1016/j.media.2021.102166}, abstract = {Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.}, language = {en} } @article{SekuboyinaBayatHusseinietal., 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}, series = {arXiv}, journal = {arXiv}, language = {en} } @misc{EhlkeHeylandMaerdianetal., author = {Ehlke, Moritz and Heyland, Mark and M{\"a}rdian, Sven and Duda, Georg and Zachow, Stefan}, title = {Assessing the Relative Positioning of an Osteosynthesis Plate to the Patient-Specific Femoral Shape from Plain 2D Radiographs}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-54268}, abstract = {We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient­specific surface of the distal femur based on 2D X­ray images. Our goal is to study from clinical data, how the plate­to­bone distance affects bone healing. The patient­specific 3D shape of the femur is, however, seldom recorded for cases of femoral osteosynthesis since this typically requires Computed Tomography (CT), which comes at high cost and radiation dose. Our method instead utilizes two postoperative X­ray images to derive the femoral shape and thus can be applied on radiographs that are taken in clinical routine for follow­up. First, the implant geometry is used as a calibration object to relate the implant and the individual X­ray images spatially in a virtual X­ray setup. In a second step, the patient­specific femoral shape and pose are reconstructed in the virtual setup by fitting a deformable statistical shape and intensity model (SSIM) to the images. The relative positioning between femur and implant is then assessed in terms of displacement between the reconstructed 3D shape of the femur and the plate. A preliminary evaluation based on 4 cadaver datasets shows that the method derives the plate­to­bone distance with a mean absolute error of less than 1mm and a maximum error of 4.7 mm compared to ground truth from CT. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing.}, language = {en} } @misc{EhlkeHeylandMaerdianetal., author = {Ehlke, Moritz and Heyland, Mark and M{\"a}rdian, Sven and Duda, Georg and Zachow, Stefan}, title = {3D Assessment of Osteosynthesis based on 2D Radiographs}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-56217}, abstract = {We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient-specific surface of the distal femur based on postoperative 2D radiographs. In a first step, the implant geometry is used as a calibration object to relate the implant and the individual X-ray images spatially in a virtual X-ray setup. Second, the patient-specific femoral shape and pose are reconstructed by fitting a deformable statistical shape and intensity model (SSIM) to the X-rays. The relative positioning between femur and implant is then assessed in terms of the displacement between the reconstructed 3D shape of the femur and the plate. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing and, ultimately, to derive load recommendations after surgery.}, language = {en} } @inproceedings{EhlkeHeylandMaerdianetal., author = {Ehlke, Moritz and Heyland, Mark and M{\"a}rdian, Sven and Duda, Georg and Zachow, Stefan}, title = {Assessing the relative positioning of an osteosynthesis plate to the patient-specific femoral shape from plain 2D radiographs}, series = {Proceedings of the 15th Annual Meeting of CAOS-International (CAOS)}, booktitle = {Proceedings of the 15th Annual Meeting of CAOS-International (CAOS)}, abstract = {We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient­specific surface of the distal femur based on 2D X­ray images. Our goal is to study from clinical data, how the plate­to­bone distance affects bone healing. The patient­specific 3D shape of the femur is, however, seldom recorded for cases of femoral osteosynthesis since this typically requires Computed Tomography (CT), which comes at high cost and radiation dose. Our method instead utilizes two postoperative X­ray images to derive the femoral shape and thus can be applied on radiographs that are taken in clinical routine for follow­up. First, the implant geometry is used as a calibration object to relate the implant and the individual X­ray images spatially in a virtual X­ray setup. In a second step, the patient­specific femoral shape and pose are reconstructed in the virtual setup by fitting a deformable statistical shape and intensity model (SSIM) to the images. The relative positioning between femur and implant is then assessed in terms of displacement between the reconstructed 3D shape of the femur and the plate. A preliminary evaluation based on 4 cadaver datasets shows that the method derives the plate­to­bone distance with a mean absolute error of less than 1mm and a maximum error of 4.7 mm compared to ground truth from CT. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing.}, language = {en} } @inproceedings{EhlkeHeylandMaerdianetal., author = {Ehlke, Moritz and Heyland, Mark and M{\"a}rdian, Sven and Duda, Georg and Zachow, Stefan}, title = {3D Assessment of Osteosynthesis based on 2D Radiographs}, series = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, booktitle = {Proceedings of the Jahrestagung der Deutschen Gesellschaft f{\"u}r Computer- und Roboterassistierte Chirurgie (CURAC)}, pages = {317 -- 321}, abstract = {We present a novel method to derive the surface distance of an osteosynthesis plate w.r.t. the patient-specific surface of the distal femur based on postoperative 2D radiographs. In a first step, the implant geometry is used as a calibration object to relate the implant and the individual X-ray images spatially in a virtual X-ray setup. Second, the patient- specific femoral shape and pose are reconstructed by fitting a deformable statistical shape and intensity model (SSIM) to the X-rays. The relative positioning between femur and implant is then assessed in terms of the displacement between the reconstructed 3D shape of the femur and the plate. We believe that the approach presented in this paper constitutes a meaningful tool to elucidate the effect of implant positioning on fracture healing and, ultimately, to derive load recommendations after surgery.}, language = {en} }