Healthy-to-patients domain-adaptive deep learning for time-resolved segmentation of left atrium in short-axis cine MRI images

Abstract

Motivation: Addressing challenges with current deep learning (DL) techniques that struggle with domain shifts. Goal(s): To introduce a domain-adaptive technique that is able to segment the Left Atrium from MRI of patients employing model trained exclusively on healthy data. Approach: Our approach involves training exclusively on healthy data and incorporating stochastic encoding of temporal composite variations as augmentations to encode the underlying space of plausible anatomical changes and dynamics. We tested on three challenging unseen patient daatsets. Results: Our domain-adaptive approach showed significant improvement over the state-of-the-art LA segmentation model. Enabling LA segmentation of all time frames of the cardiac cycle. Impact: The proposed domain-adaptive deep learning approach addresses a fundamental challenge of training deep learning models only on healthy control datasets while maintaining high performance on unseen patients' populations. This could potentially lead to solve performance issues for limited patients cohorts.