Segmentation of the Fascia Lata in Magnetic Resonance Images of the Thigh: Comparison of an Unsupervised Technique with a U-Net in 2D and Patch-wise 3D
Louise P LJ, Engelke K, Chaudry O (2021)
Publication Type: Conference contribution, Conference Contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 98-103
Conference Proceedings Title: Informatik aktuell
Event location: Regensburg, DEU
ISBN: 9783658331979
DOI: 10.1007/978-3-658-33198-6_23
Abstract
To quantify muscle properties in the thigh, the segmentation of the fascia lata is crucial. For this purpose, the U-Net architecture was implemented and compared for 2D images and patched 3D image stacks in magnetic resonance images (MRI). The training data consisted of T1 MRI data sets from elderly men. To test the performance of the models, they were applied on other data sets of different age groups and gender. The U-Net approaches were superior to an unsupervised semiautomatic method and reduced post-processing time.
Authors with CRIS profile
Klaus Engelke
Medizinische Fakultät
Oliver Chaudry
Department of Medicine 3 – Rheumatology and Immunology
How to cite
APA:
Louise P, L.J., Engelke, K., & Chaudry, O. (2021). Segmentation of the Fascia Lata in Magnetic Resonance Images of the Thigh: Comparison of an Unsupervised Technique with a U-Net in 2D and Patch-wise 3D. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 98-103). Regensburg, DEU, DE: Springer Science and Business Media Deutschland GmbH.
MLA:
Louise P, Lis J., Klaus Engelke, and Oliver Chaudry. "Segmentation of the Fascia Lata in Magnetic Resonance Images of the Thigh: Comparison of an Unsupervised Technique with a U-Net in 2D and Patch-wise 3D." Proceedings of the German Workshop on Medical Image Computing, 2021, Regensburg, DEU Ed. Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2021. 98-103.
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