Advancing Medical Image Segmentation with Self-Supervised Learning: A 3D Student-Teacher Approach for Cardiac and Neurological Imaging
Abstract
We propose 3D-SegSync, a self-supervised learning (SSL) framework designed to improve segmentation accuracy for both cardiac and neurological structures. It integrates a student-teacher model with a 3D Vision-LSTM (xLSTM) backbone to capture spatial dependencies in volumetric data. The SSL phase utilizes large-scale unlabeled datasets for pretraining, followed by fine-tuning on labeled data to improve segmentation across CT and MRI scans. Experimental results demonstrate that 3D-SegSync achieves consistent performance across different anatomical structures. Additionally, its ability to generalize between CT and MRI without requiring modality-specific modifications highlights its adaptability for cardiac and neurological image segmentation. Given its strong performance, 3D-SegSync has the potential to be extended to other medical image segmentation tasks in the future. Code can be found here: https://github.com/Moona-Mazher/3D-SegSync_SSL.
Cite
Text
Mazher et al. "Advancing Medical Image Segmentation with Self-Supervised Learning: A 3D Student-Teacher Approach for Cardiac and Neurological Imaging." Medical Imaging with Deep Learning, 2025.Markdown
[Mazher et al. "Advancing Medical Image Segmentation with Self-Supervised Learning: A 3D Student-Teacher Approach for Cardiac and Neurological Imaging." Medical Imaging with Deep Learning, 2025.](https://mlanthology.org/midl/2025/mazher2025midl-advancing/)BibTeX
@inproceedings{mazher2025midl-advancing,
title = {{Advancing Medical Image Segmentation with Self-Supervised Learning: A 3D Student-Teacher Approach for Cardiac and Neurological Imaging}},
author = {Mazher, Moona and Alexander, Daniel C. and Qayyum, Abdul and Niederer, Steven A},
booktitle = {Medical Imaging with Deep Learning},
year = {2025},
url = {https://mlanthology.org/midl/2025/mazher2025midl-advancing/}
}