Combining General and Domain-Specific Pretext Tasks for Brain MR Image Segmentation

2026-09-24

AI Summary

This study addresses the scarcity of annotated medical images and the heavy reliance of deep learning on manual annotations by proposing a multi-task self-supervised pre-training framework. Methodologically, this work innovatively jointly optimizes voxel-level brain age prediction, a domain-specific task, with image inpainting, a general-purpose task, to learn complementary neuroimaging representations and construct a highly generalizable foundation model. Experimental results demonstrate that the proposed framework significantly outperforms both single-task and from-scratch training baselines on segmentation tasks involving conditions such as multiple sclerosis. By effectively leveraging unlabeled data through complementary pretext tasks, this approach substantially mitigates the bottleneck imposed by data scarcity in medical image analysis.

Abstract

A key challenge in medical image analysis is the scarcity of large annotated datasets for specific populations and diseases. As deep learning models rely heavily on labeled data, effective transfer learning strategies are needed to reduce the dependence on manual annotations. Self-supervised learning has emerged as a promising approach for developing foundation models by enabling the learning of transferable feature representations from large-scale unlabeled medical imaging datasets. In this study, we investigate voxel-level brain age prediction as a domain-specific self-supervised pretext task and compare it with image inpainting, a widely used non-domain-specific alternative. We further propose a multitask self-supervised pretraining framework that jointly optimizes both objectives to learn complementary neuroimaging representations. The pretrained models are evaluated on three downstream magnetic resonance image segmentation tasks: multiple sclerosis lesion segmentation, ischemic stroke lesion segmentation, and cortical brain structure segmentation. Overall, the proposed multitask pretraining framework consistently outperformed the single-task pretrained models and training from scratch across most experimental settings, demonstrating the benefit of combining domain-specific and general self-supervised learning pretext tasks for the development of generalizable neuroimaging foundation models.\ Code Availability: The source code used in this study is publicly available at https://github.com/TasneemN/Combining-General-and-Domain-Specific-Pretext-Tasks-for-Brain-MR-Image-Segmentation/

Problem

• Medical Image Segmentation
• Self-Supervised Learning
• Transfer Learning
• Brain MRI
• Pretext Tasks

Innovation

• Self-supervised learning
• Multi-task pretraining
• Brain MR image segmentation
• Domain-specific pretext task
• Foundation models