CERES: A new cerebellum lobule segmentation method

•We present a novel method for cerebellum lobule segmentation on MRI.•The method consists of a fast multi-atlas non-local patch-based label fusion.•Our proposed method was shown to improve the state-of-the-art methods with a reduced temporal cost (5 minutes).•The pipeline presented in this work will be made available to scientific community through our web -based platform volBrain.

The human cerebellum is involved in language, motor tasks and cognitive processes such as attention or emotional processing. Therefore, an automatic and accurate segmentation method is highly desirable to measure and understand the cerebellum role in normal and pathological brain development. In this work, we propose a patch-based multi-atlas segmentation tool called CERES (CEREbellum Segmentation) that is able to automatically parcellate the cerebellum lobules. The proposed method works with standard resolution magnetic resonance T1-weighted images and uses the Optimized PatchMatch algorithm to speed up the patch matching process. The proposed method was compared with related recent state-of-the-art methods showing competitive results in both accuracy (average DICE of 0.7729) and execution time (around 5 minutes).

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