Translation Consistent Semi-supervised Segmentation for 3D Medical Images

Liu Y, Tian Y, Wang C, Chen Y, Liu F, Belagiannis V, Carneiro G (2024)

Publication Language: English

Publication Type: Journal article

Publication year: 2024

Journal

IEEE Transactions on Medical Imaging Institute of Electrical and Electronics Engineers (IEEE)

DOI: 10.1109/TMI.2024.3468896

Abstract

3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given the high cost to obtain such annotation. Semi-supervised learning (SSL) solves this issue by training models with a large unlabelled and a small labelled dataset. The most successful SSL approaches are based on consistency learning that minimises the distance between model responses obtained from perturbed views of the unlabelled data. These perturbations usually keep the spatial input context between views fairly consistent, which may cause the model to learn segmentation patterns from the spatial input contexts instead of the foreground objects. In this paper, we introduce the Translation Consistent Co-training (TraCoCo) which is a consistency learning SSL method that perturbs the input data views by varying their spatial input context, allowing the model to learn segmentation patterns from foreground objects. Furthermore, we propose a new Confident Regional Cross entropy (CRC) loss, which improves training convergence and keeps the robustness to co-training pseudo-labelling mistakes. Our method yields state-of-the-art (SOTA) results for several 3D data benchmarks, such as the Left Atrium (LA), Pancreas-CT (Pancreas), and Brain Tumor Segmentation (BraTS19). Our method also attains best results on a 2D-slice benchmark, namely the Automated Cardiac Diagnosis Challenge (ACDC), further demonstrating its effectiveness. Our code, training logs and checkpoints are available at https://github.com/yyliu01/ TraCoCo.

Authors with CRIS profile

Vasileios Belagiannis Professur für Maschinelles Lernen in der Signalverarbeitung

Involved external institutions

University of Adelaide AU Australia (AU) Veterans Affairs Healthcare System Boston and Harvard Medical School US United States (USA) (US) Tongji University / 同济大学 CN China (CN)

How to cite

APA:

Liu, Y., Tian, Y., Wang, C., Chen, Y., Liu, F., Belagiannis, V., & Carneiro, G. (2024). Translation Consistent Semi-supervised Segmentation for 3D Medical Images. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2024.3468896

MLA:

Liu, Yuyuan, et al. "Translation Consistent Semi-supervised Segmentation for 3D Medical Images." IEEE Transactions on Medical Imaging (2024).

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