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.
The thalamus is an internal structure of the brain whose changes are related to diseases such as multiple sclerosis and Parkinson’s disease. Thus, the thalamus segmentation is an important step in studies and applications related to these disorders, for example, for shape measuring and surgical planning. The most used software and tools for brain structures segmentation employ atlas-based algorithms that usually require long processing times and sometimes lead to inaccurate results on sub-cortical structures. New methods, that minimize those problems, using deep learning for segmenting brain structures have been recently proposed. However, for some structures such as the thalamus, these methods still tend to have unsatisfactory results since they rely only on T1w images, where the contrast can be low or absent. Aiming to overcome these issues, we proposed a Convolutional Neural Network (CNN) trained with multi-modal data (structural and diffusion MRI) and the use of silver standard masks created from multiple automatic segmentations. Results on a subset of 190 subjects from the Human Connectome Project (HCP) showed an improvement in segmentation quality, confirming the effectiveness of diffusion data in differentiating tissues due to measured micro-structural properties.
The Corpus Callosum (CC) is the largest white matter structure in the brain and subject of many relevant studies. In order to properly analyze this structure in 2D studies, the midsagittal plane (MSP) determination of the CC is required. Usually, this computation is done on structural MR images and transformed to diffusion space when necessary. Furthermore, most existing methods take into account the whole brain structure instead of only the object of study. Differently, our work proposes a plane computation based on the structure of interest, directly on Diffusion Tensor Images (DTI), through the DTI-based divergence map.1 Since our plane is computed in the diffusion domain, the method explores the high organization of the fibers in the CC to establish a reference system that can be used to perform 2D CC studies, while most existing MSP computation algorithms are based on structural characteristics of the brain, such as shape symmetry and inter-hemispheric fissure location. Experiments showed that the proposed method is reliable regarding repeatability and parameters choices. Results also indicate that the callosal fiber convergence plane (CFCP) found by our method is similar to MSP in most subjects. Nevertheless, when the CC is not well aligned with the brain intercommissural fissure, CFCP and MSP presented significant differences.
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