Towards TotalSegmentator for MRI data leveraging GIN data augmentation

Kai Geißler; Daniel Mensing; Markus Wenzel; Jochen G. Hirsch; Stefan Heldmann

doi:10.1117/12.3006351

2 April 2024 Towards TotalSegmentator for MRI data leveraging GIN data augmentation

Kai Geißler, Daniel Mensing, Markus Wenzel, Jochen G. Hirsch, Stefan Heldmann

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292604 (2024) https://doi.org/10.1117/12.3006351
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Previous work on methods for cross domain generalization in medical imaging found a simple but very effective method called ”global intensity non-linear” (GIN) augmentation. Our goal in this study is to use the GIN approach to train a model as powerful as TotalSegmentator for MRI data, despite having neither sufficient amounts of MRI data nor ground truth organ contours. Instead, we employ the GIN augmentation approach to show qualitatively and quantitatively that this is indeed feasible for a diverse set of anatomical structures including abdominal and thoracic organs as well as bones. The models are trained on the TotalSegmentator and AMOS22 datasets. For evaluation we apply them to whole body MRI scans from the German National Cohort (NAKO) study with a set of in-house reference masks. With GIN augmentation the mean Dice score of the model increases from 0.18 to 0.52 on Dixon water images, when using TotalSegmentator data for training. The improvements can be further split into 0.47 to 0.66 for abdominal organs, 0.55 to 0.79 for thoracic organs and 0.00 to 0.40 for bones.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kai Geißler, Daniel Mensing, Markus Wenzel, Jochen G. Hirsch, and Stefan Heldmann "Towards TotalSegmentator for MRI data leveraging GIN data augmentation", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292604 (2 April 2024); https://doi.org/10.1117/12.3006351

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