Multiclass Lesion Detection Using Longitudinal MRI in Multiple Sclerosis

Accurate detection of white matter (WM) lesions is essential for diagnosing and monitoring Multiple Sclerosis (MS), but manual lesion identification is challenging and time-consuming. This study employs the “no new U-Net” (nnU-Net) version 2 architecture to enhance the lesion segmentation process. We trained our model with a fine-tuned version of the default nnU-Net configuration incorporating extreme oversampling and a smaller learning rate to improve new or evolving lesion detection. Results showed that our nnU-Net v2 achieved a F1 score of 0.73 for baseline lesions and 0.75 for new or evolving lesions, demonstrating notable performance in identifying both types of lesions, and that the model generalized well to the MSSEG-2 dataset. This study highlights the capabilities of the nnU-Net v2 architecture for robust WM lesion detection in longitudinal cohorts. The final phase involved packaging our top-performing ensemble of models into a Docker container for easy usage, enabling the automatic distinction between baseline and new or evolving lesions