Automated segmentation of new multiple sclerosis (MS) lesions in MRI data is crucial for monitoring and quantifying MS progression. Manual delineation of such lesions is laborious and time-consuming since experts need to deal with 3D images and numerous small lesions. We propose a 3D encoder-decoder architecture with pre-activation blocks to segment new MS lesions in longitudinal FLAIR images. We also applied intensive data augmentation and deep supervision to mitigate the limited data and the class imbalance problem. The proposed model, called Pre-U-Net, achieved a Dice score of 0.62 and a sensitivity of 0.58 on the public challenge MSSEG-2 dataset.
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