Enhancing New Multiple Sclerosis Lesion Segmentation via Self-supervised Pre-training and Synthetic Lesion Integration

Multiple Sclerosis (MS) is a chronic and severe inflammatory disease of the central nervous system. In MS, the myelin sheath covering nerve fibres is attacked by the self-immune system, leading to communication issues between the brain and the rest of the body. Image-based biomarkers, such as lesions seen with Magnetic Resonance Imaging (MRI), are essential in MS diagnosis and monitoring. Further, detecting newly formed lesions provides crucial information for assessing disease progression and treatment outcomes. However, annotating changes between MRI scans is time-consuming and subject to inter-expert variability. Methods proposed for new lesion segmentation have utilized limited data available for training the model, failing to harness the full capacity of the models and resulting in limited generalizability. To enhance the performance of the new MS lesion segmentation model, we propose a self-supervised pre-training scheme based on image masking that is used to initialize the weights of the model, which then is trained for the new lesion segmentation task using a mix of real and synthetic data created by a synthetic lesion data augmentation method that we propose. Experiments on the MSSEG-2 challenge dataset demonstrate that utilizing self-supervised pre-training and adding synthetic lesions during training improves the model's performance. We achieved a Dice score of 56.15 +/- 7.06% and an F1 score of 56.69 +/- 9.12%, which is 2.06% points and 3.3% higher, respectively, than the previous best existing method. Code is available at: https://github.com/PeymanTahghighi/SSLMRI.