Get our free extension to see links to code for papers anywhere online!

Add to Chrome

Add to Firefox

Picture for Christian Baumgartner

Christian Baumgartner

A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images

Feb 10, 2022
Christoph Leitner, Robert Jarolim, Bernhard Englmair, Annika Kruse, Karen Andrea Lara Hernandez, Andreas Konrad, Eric Su, Jörg Schröttner, Luke A. Kelly, Glen A. Lichtwark, Markus Tilp, Christian Baumgartner

Figure 1 for A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images

Figure 2 for A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images

Figure 3 for A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images

Figure 4 for A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images

* in IEEE Transactions on Biomedical Engineering

Access Paper or Ask Questions

Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning

May 05, 2020
Christoph Leitner, Robert Jarolim, Andreas Konrad, Annika Kruse, Markus Tilp, Jörg Schröttner, Christian Baumgartner

Figure 1 for Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning

Figure 2 for Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning

Figure 3 for Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning

Figure 4 for Automatic Tracking of the Muscle Tendon Junction in Healthy and Impaired Subjects using Deep Learning

* Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canada

Access Paper or Ask Questions

Semi-Supervised and Task-Driven Data Augmentation

Feb 28, 2019
Krishna Chaitanya, Neerav Karani, Christian Baumgartner, Olivio Donati, Anton Becker, Ender Konukoglu

Figure 1 for Semi-Supervised and Task-Driven Data Augmentation

Figure 2 for Semi-Supervised and Task-Driven Data Augmentation

Figure 3 for Semi-Supervised and Task-Driven Data Augmentation

Figure 4 for Semi-Supervised and Task-Driven Data Augmentation

* 13 pages, 3 figures, 1 table, This article has been accepted at the 26th international conference on Information Processing in Medical Imaging (IPMI) 2019

Access Paper or Ask Questions

A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols

May 25, 2018
Neerav Karani, Krishna Chaitanya, Christian Baumgartner, Ender Konukoglu

Figure 1 for A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols

Figure 2 for A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols

Figure 3 for A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols

Figure 4 for A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols

* Accepted at the 21st International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI 2018) in Granada, Spain

Access Paper or Ask Questions