Paper
10 December 2021 Volumetric segmentation of the corpus callosum: training a deep learning model on diffusion MRI
Author Affiliations +
Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 120880N (2021) https://doi.org/10.1117/12.2606233
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil
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.
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joany Rodrigues, Gustavo Pinheiro, Diedre Carmo, and Letícia Rittner "Volumetric segmentation of the corpus callosum: training a deep learning model on diffusion MRI", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 120880N (10 December 2021); https://doi.org/10.1117/12.2606233
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KEYWORDS
Image segmentation

Diffusion

3D modeling

Diffusion tensor imaging

Anisotropy

Diffusion magnetic resonance imaging

Magnetic resonance imaging

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