Unsupervised brain MRI tumour segmentation via two-stage image synthesis
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Deep learning shows promise in automated brain tumour segmentation, but it depends on costly expert annotations. Recent advances in unsupervised learning offer an alternative by using synthetic data for training. However, the discrepancy between real and synthetic data limits the accuracy of the unsupervised approaches. In this paper, we propose an approach for unsupervised brain tumour segmentation on magnetic resonance (MR) images via a two-stage image synthesis strategy. This approach accounts for the domain gap between real and synthetic data and aims to generate realistic synthetic data for model training. In the first stage, we train a junior segmentation model using synthetic brain tumour images generated by hand-crafted tumour shape and intensity models, and employs a validation set with distribution shift for model selection. The trained junior model is applied to segment unlabelled real tumour images, generating pseudo labels that capture realistic tumour shape, intensity, and texture. In the second stage, realistic synthetic tumour images are generated by mixing brain images with tumour pseudo labels, closing the domain gap between real and synthetic images. The generated synthetic data is then used to train a senior model for final segmentation. In experiments on five brain imaging datasets, the proposed approach, named as SynthTumour, surpasses existing unsupervised methods and demonstrates high performance for both brain tumour segmentation and ischemic stroke lesion segmentation tasks.
Date Issued
2025-05-01
Date Acceptance
2025-03-25
Citation
Medical Image Analysis, 2025, 102
ISSN
1361-8415
Publisher
Elsevier BV
Journal / Book Title
Medical Image Analysis
Volume
102
Copyright Statement
© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.media.2025.103568
Publication Status
Published
Article Number
103568
Date Publish Online
2025-04-05