@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}, series = {Journal of Biomechanics}, volume = {46}, journal = {Journal of Biomechanics}, number = {11}, doi = {/10.1016/j.jbiomech.2013.04.021}, pages = {1900 -- 1906}, year = {2013}, language = {en} } @article{SaevarssonSharmaRammetal.2013, author = {Saevarsson, Stefan and Sharma, Gulshan and Ramm, Heiko and Lieck, Robert and Hutchison, Carol and Werle, Jason and Montgomery, Sigrun and Romeo, Carolina and Zachow, Stefan and Anglin, Carolyn}, title = {Kinematic Differences Between Gender Specific And Traditional Knee Implants}, series = {The Journal of Arthroplasty}, volume = {28}, journal = {The Journal of Arthroplasty}, number = {9}, doi = {10.1016/j.arth.2013.01.021}, pages = {1543 -- 1550}, year = {2013}, language = {en} } @article{ZahnGrotjohannRammetal., author = {Zahn, Robert and Grotjohann, Sarah and Ramm, Heiko and Zachow, Stefan and Pumberger, Matthias and Putzier, Michael and Perka, Carsten and Tohtz, Stephan}, title = {Influence of pelvic tilt on functional acetabular orientation}, series = {Technology and Health Care}, volume = {25}, journal = {Technology and Health Care}, number = {3}, publisher = {IOS Press}, doi = {10.3233/THC-161281}, pages = {557 -- 565}, language = {en} } @article{ZahnGrotjohannRammetal., author = {Zahn, Robert and Grotjohann, Sarah and Ramm, Heiko and Zachow, Stefan and Putzier, Michael and Perka, Carsten and Tohtz, Stephan}, title = {Pelvic tilt compensates for increased acetabular anteversion}, series = {International Orthopaedics}, volume = {40}, journal = {International Orthopaedics}, number = {8}, doi = {10.1007/s00264-015-2949-6}, pages = {1571 -- 1575}, abstract = {Pelvic tilt determines functional orientation of the acetabulum. In this study, we investigated the interaction of pelvic tilt and functional acetabular anteversion (AA) in supine position.}, language = {en} } @inproceedings{RammVictoriaMorilloTodtetal.2013, author = {Ramm, Heiko and Victoria Morillo, Oscar Salvador and Todt, Ingo and Schirmacher, Hartmut and Ernst, Arneborg and Zachow, Stefan and Lamecker, Hans}, title = {Visual Support for Positioning Hearing Implants}, series = {Proceedings of the 12th annual meeting of the CURAC society}, booktitle = {Proceedings of the 12th annual meeting of the CURAC society}, editor = {Freysinger, Wolfgang}, pages = {116 -- 120}, year = {2013}, 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}, series = {Proceedings of the 11th Alberta Biomedical Engineering (BME) Conference (Poster)}, journal = {Proceedings of the 11th Alberta Biomedical Engineering (BME) Conference (Poster)}, pages = {80}, year = {2011}, 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} } @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}, series = {Computer Aided Medical Engineering (CaMe)}, 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}, series = {Proc. MICCAI Workshop on Mesh Processing in Medical Image Analysis (MeshMed)}, 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}, series = {Health Academy}, 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{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} } @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} } @article{HettichSchierjottSchillingetal., author = {Hettich, G. and Schierjott, R. A. and Schilling, C. and Maas, A. and Ramm, Heiko and Bindernagel, Matthias and Lamecker, Hans and Grupp, T. M.}, title = {Validation of a Statistical Shape Model for Acetabular Bone Defect Analysis}, series = {ISTA 2018 London Abstract Book}, journal = {ISTA 2018 London Abstract Book}, abstract = {Acetabular bone defects are still challenging to quantify. Numerous classification schemes have been proposed to categorize the diverse kinds of defects. However, these classification schemes are mainly descriptive and hence it remains difficult to apply them in pre-clinical testing, implant development and pre-operative planning. By reconstructing the native situation of a defect pelvis using a Statistical Shape Model (SSM), a more quantitative analysis of the bone defects could be performed. The aim of this study is to develop such a SSM and to validate its accuracy using relevant clinical scenarios and parameters.}, language = {en} } @article{BrueningHildebrandtHepptetal., 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}, series = {Scientific Reports}, volume = {3755}, journal = {Scientific Reports}, number = {10}, doi = {10.1038/s41598-020-60755-3}, 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} } @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} } @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} }