Longitudinal detection of new MS lesions using Deep Learning
- URL: http://arxiv.org/abs/2206.08272v1
- Date: Thu, 16 Jun 2022 16:09:04 GMT
- Title: Longitudinal detection of new MS lesions using Deep Learning
- Authors: Reda Abdellah Kamraoui, Boris Mansencal, Jos\'e V Manjon, Pierrick
Coup\'e
- Abstract summary: We describe a deep-learning-based pipeline addressing the task of detecting and segmenting new MS lesions.
First, we propose to use transfer-learning from a model trained on a segmentation task using single time-points.
Second, we propose a data synthesis strategy to generate realistic longitudinal time-points with new lesions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The detection of new multiple sclerosis (MS) lesions is an important marker
of the evolution of the disease. The applicability of learning-based methods
could automate this task efficiently. However, the lack of annotated
longitudinal data with new-appearing lesions is a limiting factor for the
training of robust and generalizing models. In this work, we describe a
deep-learning-based pipeline addressing the challenging task of detecting and
segmenting new MS lesions. First, we propose to use transfer-learning from a
model trained on a segmentation task using single time-points. Therefore, we
exploit knowledge from an easier task and for which more annotated datasets are
available. Second, we propose a data synthesis strategy to generate realistic
longitudinal time-points with new lesions using single time-point scans. In
this way, we pretrain our detection model on large synthetic annotated
datasets. Finally, we use a data-augmentation technique designed to simulate
data diversity in MRI. By doing that, we increase the size of the available
small annotated longitudinal datasets. Our ablation study showed that each
contribution lead to an enhancement of the segmentation accuracy. Using the
proposed pipeline, we obtained the best score for the segmentation and the
detection of new MS lesions in the MSSEG2 MICCAI challenge.
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