Use of deep learning-based optic chiasm segmentation for the investigation of visual system pathophysiology and neuroplasticity
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Description: Convolutional Neural Network (CNN) models are of great promise to aid segmentation and analysis of brain structures. Here, we tested, whether CNN trained to segment normal optic chiasms from T1w MRI image can be also applied to abnormal chiasms, specifically with optic nerve misrouting as typical for human albinism. We performed supervised training of the CNN on the T1w images of control participants (n=1049) from Human Connectome Project repository and automatically generated algorithm-based optic chiasm masks. The trained CNN was subsequently tested on data of persons with albinism (PWA; n=9) and controls (n=8) from the CHIASM repository. The quality of outcome segmentation was assessed via the comparison to manually defined optic chiasm masks using the Dice Similarity Coefficient (DSC). The results revealed contrasting quality of masks obtained for control (mean DSC ± SEM = 0.75 ± 0.03) and PWA data (0.43 ± 0.8, FWE corrected p=0.04). The fact that the CNN recognition of the optic chiasm fails for chiasm abnormalities in PWA underlines the fundamental differences in their spatial features. This finding thus provides proof-of-concept for a novel deep-learning based diagnostics-approach of chiasmal misrouting from T1w images, as well as further analyses on chiasmal misrouting and their impact on structure and function of visual system.