Automatic segmentation of histogical images of the brain of mice

Abstract

The mouse brain offers unique features for the study of genes involved in human brain development. Specifically, genetic manipulation, such as gene inactivation, so easily achieved in the mouse, allows us to explore the effects of genes on brain morphogenesis. Using a high-throughput neuroanatomical screen on coronal and parasagittal brain sections involving 1566 mutants lines developed by the International Mouse Phenotyping Consortium, was published a list of 198 genes whose inactivation lead to neuroanatomical phenotypes. To achieve this milestone, tens of thousands of hours of segmentation were necessary since manual segmentation of a single brain takes approximately 1 hour. Our work consisted in applying deep learning methods to produce automated segmentation of 24 anatomical regions which were used in the aforementioned screen. The dataset comprises about 2000 annotated images each of 1 GB in size which required compression. Training was achieved for each region of interest and with two image resolution (512x256 and 2048x1024) using a U-Net and an Attention U-Net architecture. At 2048x1024, an overall DSC (Dice Score Coefficient) of 0.90 ± 0.01 was achieved for all 24 regions with the best performance for the total brain (DSC 0.99 ± 0.01) and the worst for the fibers of the pons (DSC 0.71 ± 0.18). Using a one command line, the end-user is now able to pre-analyze images automatically then runs the existing analytical pipeline made of ImageJ macros to validate the automatically generated regions of interest resulting. We estimate the time saved by 6 to 10 times.