Whole Tumor Segmentation from Brain MR images using Multi-view 2D Convolutional Neural Network.

In this paper, a study is reported on the popular BraTS dataset for segmentation of brain tumor. The BraTS 2019 dataset is used that comprises four MR modalities along with the ground-truth for 259 high grade glioma (HGG) and 76 low grade glioma (LGG) patient data. We have employed U-Net architecture based 2D convolutional neural network (CNN) for each of the orthogonal planes (sagittal, coronal and axial) and fused their predictions. The objective function is aimed to minimize Dice loss between the binary prediction and its actual labels. Samples having tumor information are considered for each patient data to avoid training on non-informative data. The models are trained on 222 HGG data and tested on 37 HGG data using performance metrics such as sensitivity, specificity, accuracy and Dice score. Test-time augmentation is also performed to improve the segmentation performance. 7-fold cross validation is conducted to analyze the performance on different sets of training and testing data.

BRAHMA: Population specific T1, T2, and FLAIR weighted brain templates and their impact in structural and functional imaging studies.

Differences in brain morphology across population groups necessitate creation of population-specific Magnetic Resonance Imaging (MRI) brain templates for interpretation of neuroimaging data. Variations in the neuroanatomy in a genetically heterogeneous population make the development of a population-specific brain template for the Indian subcontinent imperative. A dataset of high-resolution 3D T1, T2-weighted, and FLAIR images acquired from a group of 113 volunteers (M/F - 56/57, mean age-28.96 ±â€¯7.80 years) are used to construct T1, T2-weighted, and FLAIR templates, collectively referred to as Indian Brain Template, "BRAHMA". A processing pipeline is developed and implemented in a MATLAB based toolbox for template construction and generation of tissue probability maps and segmentation atlases, with additional labels for deep brain regions such as the Substantia Nigra generated from the T2-weighted and FLAIR templates. The use of BRAHMA template for analysis of structural and functional neuroimaging data obtained from Indian participants, provides improved accuracy with statistically significant results over that obtained using the ICBM-152 (International Consortium for Brain Mapping) template. Our results indicate that segmentations generated on structural images are closer in volume to those obtained from registration to the BRAHMA template than to the ICBM-152. Furthermore, functional MRI data obtained for Working Memory and Finger Tapping paradigms processed using the BRAHMA template show a significantly higher percentage of the activation area than ICBM-152 in relevant brain regions, i.e. the left middle frontal gyrus, and the left and right precentral gyri, respectively. The availability of different image contrasts, tissue maps, and segmentation atlases makes the BRAHMA template a comprehensive tool for multi-modal image analysis in laboratory and clinical settings.