Volumetric Segmentation of the Corpus Callosum: Training a Deep Learning model on diffusion MRI

Author(s)

Rodrigues, Joany ; Pinheiro, Gustavo ; Carmo, Diedre ; Rittner, Leticia ; Romero, E ; Costa, ET ; Brieva, J ; Rittner, L ; Linguraru, MG ; Lepore, N

• Document type: Journal article
• Source: 17TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION PROCESSING AND ANALYSIS; v. 12088, p. 10-pg., 2021-01-01.

Abstract

Corpus callosum (CC) segmentation is an important first step of MRI-based analysis, however most available automated methods and tools perform its segmentation on the midsagittal slice only. Additionally, the few volumetric CC segmentation methods available work on T1-weighted images, what requires an additional step of registering the T1 segmentation mask over diffusion tensor images (DTI) when conducting any DTI-based analysis. This work presents a volumetric segmentation method of the corpus callosum using a modified U-Net on diffusion tensor data, such as Fractional Anisotropy (FA), Mean Difusivity (MD) and Mode of Anisotropy (MO). The model was trained on 70 DTI acquisitions and tested on a dataset composed of 14 acquisitions with manual volumetric segmentation. Results indicate that using multiple DTI maps as input channels is better than using a single one. The best model obtained a mean dice of 83,29% on the test dataset, surpassing the performance of available softwares. (AU)

• FAPESP's process: 13/07559-3 - BRAINN - The Brazilian Institute of Neuroscience and Neurotechnology
• Grantee: Fernando Cendes
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 19/21964-4 - Lung diseases and COVID diagnosis from computed tomography images using explainable deep learning
• Grantee: Diedre Santos do Carmo
• Support Opportunities: Scholarships in Brazil - Doctorate