11 citations · 11 across the 2 of their papers we have counts for
10 papers
A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images
Christoph Leitner, Robert Jarolim, Bernhard Englmair +9
Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such a…
Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation
Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner +4
Supervised learning-based segmentation methods typically require a large number of annotated training data to generalize well at test time. In medical applications, curating such d…
PHiSeg: Capturing Uncertainty in Medical Image Segmentation
Christian F. Baumgartner, Kerem C. Tezcan, Krishna Chaitanya +6
Segmentation of anatomical structures and pathologies is inherently ambiguous. For instance, structure borders may not be clearly visible or different experts may have different st…
A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation
Robin Brügger, Christian F. Baumgartner, Ender Konukoglu
One of the key drawbacks of 3D convolutional neural networks for segmentation is their memory footprint, which necessitates compromises in the network architecture in order to fit…
Combining Heterogeneously Labeled Datasets For Training Segmentation Networks
Jana Kemnitz, Christian F. Baumgartner, Wolfgang Wirth +3
Accurate segmentation of medical images is an important step towards analyzing and tracking disease related morphological alterations in the anatomy. Convolutional neural networks…
Learning to Segment Medical Images with Scribble-Supervision Alone
Yigit B. Can, Krishna Chaitanya, Basil Mustafa +3
Semantic segmentation of medical images is a crucial step for the quantification of healthy anatomy and diseases alike. The majority of the current state-of-the-art segmentation al…