Abstract #1309

Transfer Learning in Hip MRI Segmentation: Geometry Is More Important Than Contrast

Claudia Iriondo^1,2, Michael Girard³, Valentina Pedoia¹, and Sharmila Majumdar¹

¹Radiology and Biomedical Imaging, University of California, San Francsico, San Francisco, CA, United States, ²Bioengineering, University of California, Berkeley, Berkeley, CA, United States, ³Center for Digital Health Innovation, University of California, San Francsico, San Francisco, CA, United States

Transfer learning for medical image segmentation tasks is a promising technique that has the potential to overcome the challenges posed by limited training data. In this study we investigate the contribution of geometrically-similar and contrast-similar features for transfer learning to a hip MR segmentation task. We show pretraining with a geometrically similar task leads to more rapid convergence, can stabilize segmentation accuracy as datasets become reduced in size, and leads to more reliable biomarker extraction.

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