GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data

TL;DR

GuidedNet is a semi-supervised multi-organ segmentation method that uses a novel knowledge-guided approach to improve pseudo-label quality and segmentation accuracy, especially for small and complex organs, achieving state-of-the-art results.

Contribution

The paper introduces GuidedNet, which integrates a 3D-CGMM and KT-CPS strategies to better utilize labeled data for guiding unlabeled data training in multi-organ segmentation.

Findings

Achieves state-of-the-art performance on FLARE22 and AMOS datasets.

Improves segmentation accuracy for small and complex organs.

Effectively leverages labeled data to guide unlabeled data training.

Abstract

Semi-supervised multi-organ medical image segmentation aids physicians in improving disease diagnosis and treatment planning and reduces the time and effort required for organ annotation.Existing state-of-the-art methods train the labeled data with ground truths and train the unlabeled data with pseudo-labels. However, the two training flows are separate, which does not reflect the interrelationship between labeled and unlabeled data.To address this issue, we propose a semi-supervised multi-organ segmentation method called GuidedNet, which leverages the knowledge from labeled data to guide the training of unlabeled data. The primary goals of this study are to improve the quality of pseudo-labels for unlabeled data and to enhance the network's learning capability for both small and complex organs.A key concept is that voxel features from labeled and unlabeled data that are close to each other in the feature space are more likely to belong to the same class.On this basis, a 3D Consistent Gaussian Mixture Model (3D-CGMM) is designed to leverage the feature distributions from labeled data to rectify the generated pseudo-labels.Furthermore, we introduce a Knowledge Transfer Cross Pseudo Supervision (KT-CPS) strategy, which leverages the prior knowledge obtained from the labeled data to guide the training of the unlabeled data, thereby improving the segmentation accuracy for both small and complex organs. Extensive experiments on two public datasets, FLARE22 and AMOS, demonstrated that GuidedNet is capable of achieving state-of-the-art performance. The source code with our proposed model are available at https://github.com/kimjisoo12/GuidedNet.