@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} } @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} } @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} } @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{SharmaSaevarssonAmirietal.2012, author = {Sharma, Gulshan and Saevarsson, Stefan and Amiri, Shahram and Montgomery, Sigrun and Ramm, Heiko and Lichti, Derek and Zachow, Stefan and Anglin, Carolyn}, title = {Sequential-Biplane Radiography for Measuring Pre and Post Total Knee Arthroplasty Kinematics}, series = {58th Annual Meeting of the Orthopaedic Research Society (ORS)}, journal = {58th Annual Meeting of the Orthopaedic Research Society (ORS)}, address = {San Francisco, CA}, year = {2012}, language = {en} } @article{SaevarssonSharmaAmirietal.2012, author = {Saevarsson, Stefan and Sharma, Gulshan and Amiri, Shahram and Montgomery, Sigrun and Ramm, Heiko and Lichti, Derek and Lieck, Robert and Zachow, Stefan and Anglin, Carolyn}, title = {Radiological method for measuring patellofemoral tracking and tibiofemoral kinematics before and after total knee replacement}, series = {Bone and Joint Research}, volume = {1}, journal = {Bone and Joint Research}, number = {10}, doi = {10.1302/2046-3758.110.2000117}, pages = {263 -- 271}, year = {2012}, 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} } @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} } @misc{SharmaHoSaevarssonetal.2012, author = {Sharma, Gulshan and Ho, Karen and Saevarsson, Stefan and Ramm, Heiko and Lieck, Robert and Zachow, Stefan and Anglin, Carolyn}, title = {Knee Pose and Geometry Pre- and Post-Total Knee Arthroplasty Using Computed Tomography}, series = {58th Annual Meeting of the Orthopaedic Research Society (ORS)}, journal = {58th Annual Meeting of the Orthopaedic Research Society (ORS)}, address = {San Francisco, CA}, year = {2012}, language = {en} }