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arXiv · 2502.03229

A Unified Framework for Semi-Supervised Image Segmentation and Registration

Ruizhe Li·Grazziela Figueredo·Dorothee Auer·Rob Dineen·Paul Morgan·Xin Chen
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Abstract

Semi-supervised learning, which leverages both annotated and unannotated data, is an efficient approach for medical image segmentation, where obtaining annotations for the whole dataset is time-consuming and costly. Traditional semi-supervised methods primarily focus on extracting features and learning data distributions from unannotated data to enhance model training. In this paper, we introduce a novel approach incorporating an image registration model to generate pseudo-labels for the unannotated data, producing more geometrically correct pseudo-labels to improve the model training. Our method was evaluated on a 2D brain data set, showing excellent performance even using only 1\% of the annotated data. The results show that our approach outperforms conventional semi-supervised segmentation methods (e.g. teacher-student model), particularly in a low percentage of annotation scenario. GitHub: https://github.com/ruizhe-l/UniSegReg.

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Ruizhe Li, Grazziela Figueredo, Dorothee Auer, Rob Dineen, Paul Morgan, Xin Chen. 2025-02-05. A Unified Framework for Semi-Supervised Image Segmentation and Registration. https://arxiv.org/abs/2502.03229

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