Leveraging Labelled Data Knowledge: A Cooperative Rectification Learning Network for Semi-supervised 3D Medical Image Segmentation

TL;DR

This paper introduces a novel semi-supervised 3D medical image segmentation method that leverages a cooperative learning network, dynamic interaction modules, and positive supervision to produce high-quality pseudo-labels and improve segmentation accuracy.

Contribution

The paper proposes the Cooperative Rectification Learning Network, Dynamic Interaction Module, and Cooperative Positive Supervision to enhance pseudo-label quality in semi-supervised 3D medical image segmentation.

Findings

Outperforms existing semi-supervised methods on three datasets.

Produces more accurate pseudo-labels for unlabelled data.

Improves segmentation accuracy with limited labelled data.

Abstract

Semi-supervised 3D medical image segmentation aims to achieve accurate segmentation using few labelled data and numerous unlabelled data. The main challenge in the design of semi-supervised learning methods consists in the effective use of the unlabelled data for training. A promising solution consists of ensuring consistent predictions across different views of the data, where the efficacy of this strategy depends on the accuracy of the pseudo-labels generated by the model for this consistency learning strategy. In this paper, we introduce a new methodology to produce high-quality pseudo-labels for a consistency learning strategy to address semi-supervised 3D medical image segmentation. The methodology has three important contributions. The first contribution is the Cooperative Rectification Learning Network (CRLN) that learns multiple prototypes per class to be used as external knowledge priors to adaptively rectify pseudo-labels at the voxel level. The second contribution consists of the Dynamic Interaction Module (DIM) to facilitate pairwise and cross-class interactions between prototypes and multi-resolution image features, enabling the production of accurate voxel-level clues for pseudo-label rectification. The third contribution is the Cooperative Positive Supervision (CPS), which optimises uncertain representations to align with unassertive representations of their class distributions, improving the model's accuracy in classifying uncertain regions. Extensive experiments on three public 3D medical segmentation datasets demonstrate the effectiveness and superiority of our semi-supervised learning method.