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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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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.
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VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images

Anjany Sekuboyina, +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.
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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.
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Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds

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.