MedAI Session 25: Training medical image segmentation models with less labeled data | Sarah Hooper

Title: Training medical image segmentation models with less labeled data

Speaker: Sarah Hooper

Abstract:
Segmentation is a powerful tool for quantitative analysis of medical images. Because manual segmentation can be tedious, be time consuming, and have high inter-observer variability, neural networks (NNs) are an appealing solution for automating the segmentation process. However, most approaches to training segmentation NNs rely on large, labeled training datasets that are costly to curate. In this work, we present a general semi-supervised method for training segmentation networks that reduces the required amount of labeled data. Instead, we rely on a small set of labeled data and a large set of unlabeled data for training. We evaluate our method on four cardiac magnetic resonance (CMR) segmentation targets and show that by using only 100 labeled training image slices---up to a 99.4% reduction of labeled data---the proposed model achieves within 1.10% of the Dice coefficient achieved by a network trained with over 16,000 labeled image slices. We use the segmentations predicted by our method to derive cardiac functional biomarkers and find strong agreement to expert measurements of predicted ejection fraction, end diastolic volume, end systolic volume, stroke volume, or left ventricular mass compared an expert annotator.

Speaker Bio:
Sarah Hooper is a PhD candidate at Stanford University, where she works with Christopher Ré and Curtis Langlotz. She is broadly interested in applying machine learning to meet needs in healthcare, with a particular interest in applications that make quality healthcare more accessible. Sarah received her B.S. in Electrical Engineering at Rice University in 2017 and her M.S. in Electrical Engineering at Stanford University in 2020.

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