The Corpus callosum (CC) is a massive white matter structure in the brain, and changes in its shape and volume are associated with subject characteristics, several diseases, and clinical conditions. The CC is mostly studied in magnetic resonance imaging (MRI), where it is segmented to extract valuable information. With the increasing availability of MRI data and the proliferation of automated algorithms to perform CC segmentation, quality control (QC) verification is mandatory to assure reliability in the entire analysis pipeline. We propose a convolutional neural network (CNN) for QC of CC segmentations. The CNN gets information on the mask and contextual information on the image and performs deep feature extraction using a pre-trained model. The CNN model was fine-tuned using T1-weighted MR images with CC masks, in the task of classifying correct or incorrect segmentations. The CNN-based approach got an area under the curve (AUC) of 97.98% on the test set. We used an additional test set of patients with tumor to test generalization capability of the trained model to other domains.
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.
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