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Abstract #0157

Reducing annotation burden in MR segmentation: A novel contrastive learning loss with multi-contrast constraints on local representations

Lavanya Umapathy1,2, Taylor Brown2,3, Mark Greenhill2,3, J'rick Lu2,3, Diego Martin4, Maria Altbach2, and Ali Bilgin1,2,5,6
1Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, 2Department of Medical Imaging, University of Arizona, Tucson, AZ, United States, 3College of Medicine, University of Arizona, Tucson, AZ, United States, 4Department of Radiology, Houston Methodist Hospital, Houston, TX, United States, 5Program in Applied Mathematics, University of Arizona, Tucson, AZ, United States, 6Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Representational LearningThe availability of limited labeled data motivates the use of self-supervised pretraining techniques for deep learning (DL) models. Here, we propose a novel contrastive loss that pushes/pulls local representations within an image based on representational constraints from co-registered multi-contrast MR images that share similar underlying parameters. For multi-organ segmentation tasks in T2-weighted images, pretraining a DL model using the proposed loss function with constraints from co-registered echo images from a radial TSE acquisition, can help reduce annotation burden by 60%. On two independent datasets, proposed pretraining improved Dice scores compared to random initialization and pretraining with conventional contrastive loss.

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Keywords