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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.