A Deep-learning Based Computer Framework for Automatic Anatomical Segmentation of Mouse Brain

Abstract

In the research of neuroscience, it is significant for quantitative analysis based on accurate brain regions segmentation. A deep-learning enabled computer framework have been developed that can automatically segment fluorescence microscopy image data of mouse brain into several anatomical brain regions. Our approach includes an optimized registration algorithm for readily providing training datasets and a semantic segmentation neural network to infer brain regions efficiently. Using our deep-learning model, we can directly obtain the segmentation results of 18 brain regions in real time, and at high accuracy with averaged mean Dice value over 0.85.