The Corpus Callosum is the major interhemispheric commisure and, because of its highly organized fibers, it is often studied using diffusion tensor images (DTI). A firstnecessary step for CC studies is its segmentation, preferably automated. Since most available softwares are not able to perform CC volumetric segmentation, and the only ones that do it, are based on T1-weighted images and not DTI, this work presents the extension of an open-source software, called inCCsight, incorporating a DTI-based CC volumetric segmentation method into it. The software is open-source and offers the possibility of incorporating customized plots and integrating other segmentation and/or parcellation methods by the user.
Corpus Callosum (CC) segmentation is required when the analysis from this structure is desirable. Many of these studies require the CC segmentation on diffusion tensor images (DTI). However, few methods perform segmentation directly in the DTI. Segmenting on DTI makes it possible to disregard the registration step after segmenting on T1 images. This work studies the possibility of improving automated segmentation of the CC using silver standard annotations. With incomplete silver standard annotations, limited to 5 or 7 central slices, experiments performed throughout this work were done to compare methods of pre-training and fine tuning in an attempt to translate silver standard knowledge to improved performance in 3D CC segmentation. Experiments include 3D and 2D U-Net as deep learning architectures. Results point to central limited silver standard annotations not being useful for improving the performance in gold standard 3D annotations. Our best method involved training a 3D U-Net with gold standards and post processing, achieving a 3D Dice of 83.33 Dice, surpassing 2D U-Net.
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.
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