Leveraging CORAL-Correlation Consistency Network for Semi-Supervised Left Atrium MRI Segmentation

TL;DR

This paper introduces CORAL-Correlation Consistency Network (CORN), a semi-supervised learning method that effectively captures the global structure and local details of the left atrium in MRI images, improving segmentation accuracy.

Contribution

The paper proposes the CORAL-Correlation Consistency Module and Dynamic Feature Pool to better utilize second-order statistics and address sample selection bias in semi-supervised left atrium segmentation.

Findings

CORN outperforms previous semi-supervised methods on the Left Atrium dataset.

The Dynamic Feature Pool improves feature selection accuracy.

Global structural features enhance segmentation performance.

Abstract

Semi-supervised learning (SSL) has been widely used to learn from both a few labeled images and many unlabeled images to overcome the scarcity of labeled samples in medical image segmentation. Most current SSL-based segmentation methods use pixel values directly to identify similar features in labeled and unlabeled data. They usually fail to accurately capture the intricate attachment structures in the left atrium, such as the areas of inconsistent density or exhibit outward curvatures, adding to the complexity of the task. In this paper, we delve into this issue and introduce an effective solution, CORAL(Correlation-Aligned)-Correlation Consistency Network (CORN), to capture the global structure shape and local details of Left Atrium. Diverging from previous methods focused on each local pixel value, the CORAL-Correlation Consistency Module (CCM) in the CORN leverages second-order statistical information to capture global structural features by minimizing the distribution discrepancy between labeled and unlabeled samples in feature space. Yet, direct construction of features from unlabeled data frequently results in ``Sample Selection Bias'', leading to flawed supervision. We thus further propose the Dynamic Feature Pool (DFP) for the CCM, which utilizes a confidence-based filtering strategy to remove incorrectly selected features and regularize both teacher and student models by constraining the similarity matrix to be consistent. Extensive experiments on the Left Atrium dataset have shown that the proposed CORN outperforms previous state-of-the-art semi-supervised learning methods.