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Abstract #1298

APGC Net: Unsupervised Cross-Modality Adaptation for Multi-organ Segmentation in TAO via adaptive pseudo-label-guided contrastive learning

Yiyou Sun1, Xiaogen Zhou2, Min Deng1, Cheng Chen2, Qi Dou2, Karen Kar Wun Chan3, Kelvin Kam Lung Chong3, and Winnie Chiu Wing Chu1
1Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong, 2Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong, 3Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong

Synopsis

Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Unsupervised domain adaptation, medical image segmentation,Thyroid-associated orbitopathy

Motivation: Clinical assessment of thyroid-associated orbitopathy (TAO) demands precise orbital structure segmentation. The central challenge is that neither pre-contrast (T1) nor post-contrast (T1c) MRI alone provides comprehensive segmentation that covers all TAO-affected organs.

Goal(s): The primary objective is to develop an automated segmentation framework that can segment multi-modal MRIs even when complete manual labels are not available for both modalities.

Approach: An unsupervised domain adaptation approach is proposed to tackle the challenge via adaptative pseudo-label guided cross-modality contrastive learning.

Results: This work achieved significant performance improvements in terms of multiple evaluation metrics.

Impact: The impact lies in the technical innovation to ensure consistently accurate segmentation of each organ involved in TAO on multi-modal MRI, which is beneficial to alleviating the burden of manual labeling and reducing observer variability.

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Keywords