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Alexander Tack
Researcher at Zuse Institute Berlin
Publications - 14
Citations - 520
Alexander Tack is an academic researcher from Zuse Institute Berlin. The author has contributed to research in topics: Segmentation & Point distribution model. The author has an hindex of 6, co-authored 14 publications receiving 283 citations.
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Journal ArticleDOI
Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge and Convolutional Neural Networks: Data from the Osteoarthritis Initiative
TL;DR: 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.
Journal ArticleDOI
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Anjany Sekuboyina,Malek El Husseini,Amirhossein Bayat,Maximilian T. Löffler,Hans Liebl,Hongwei Li,Giles Tetteh,Jan Kukačka,Christian Payer,Darko Štern,Martin Urschler,Maodong Chen,Dalong Cheng,Nikolas Lessmann,Yujin Hu,Tianfu Wang,Dong Yang,Daguang Xu,Felix Ambellan,Tamaz Amiranashvili,Moritz Ehlke,Hans Lamecker,Sebastian Lehnert,Marilia Lirio,Nicolás Pérez de Olaguer,Heiko Ramm,Manish Sahu,Alexander Tack,Stefan Zachow,Tao Jiang,Xinjun Ma,Christoph Angerman,Xin Wang,Kevin W. Brown,Alexandre Kirszenberg,Elodie Puybareau,Di Chen,Yiwei Bai,Brandon H. Rapazzo,Timyoas Yeah,Amber Zhang,Shangliang Xu,Feng Hou,Zhiqiang He,Chan Zeng,Zheng Xiangshang,Xu Liming,Tucker Netherton,Raymond P. Mumme,Laurence E. Court,Zixun Huang,Chenhang He,Li-Wen Wang,Sai Ho Ling,Lê Duy Huỳnh,Nicolas Boutry,Roman Jakubicek,Jiri Chmelik,Supriti Mulay,Mohanasankar Sivaprakasam,Johannes C. Paetzold,Suprosanna Shit,Ivan Ezhov,Benedikt Wiestler,Ben Glocker,Alexander Valentinitsch,Markus Rempfler,Björn H. Menze,Jan S. Kirschke +68 more
TL;DR: 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.
Journal ArticleDOI
Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative.
TL;DR: In this paper, a segmentation method employing convolutional neural networks in combination with statistical shape models was developed for knee menisci segmentation from MRIs, which was evaluated on 88 manual segmentations.
Journal ArticleDOI
Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds
Florian Bernard,Florian Bernard,Luis Salamanca,Johan Thunberg,Alexander Tack,Dennis Jentsch,Hans Lamecker,Stefan Zachow,Frank Hertel,Jorge Goncalves,Peter Gemmar +10 more
TL;DR: This work proposes the use of a statistical shape model (SSM) as a prior for surface reconstruction and compares its method to the extensively used Iterative Closest Points method on several different anatomical datasets/SSMs and demonstrates superior accuracy and robustness on sparse data.
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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
TL;DR: In this article, a state-of-the-art object detector, YOLOv4, was trained to locate regions of interests in full-leg radiographs for the hip joint, knee, and ankle.