Accurate and automated brain tumor segmentation using multi modal MR images is essential for the evaluation of the disease progression in order to improve disease diagnosis and treatment planning. We present a new fully automated method for high graded brain tumor segmentation combining sparse autoencoder and multimodal Fuzzy C-means clustering. The approach utilizes multimodal MRI contrast: T1, T2, FLAIR and T1c (contrast-enhanced) for 15 high graded glioma (HGG) subjects. The objective of the proposed study is to segment tumor tissues from HGG including edema and tumor core within edema. The segmentation was performed on the training data of the multimodal brain tumor image segmentation benchmark 2015. Sparse autoencoder, which is an unsupervised learning algorithm, was used to automatically learn features from unlabeled dataset of tumor in order to segment edema. Followed by edema segmentation, tumor core was segmented from edema using multimodal FCM clustering. Evaluating the performance of the segmentation results with the ground truth yields high dice score (DS) of 0.9866±0.01 and 0.9843±0.01 for edema and tumor core respectively and high Jaccard similarity (JS) of 0.9738±0.02 and 0.9692±0.02 for edema and tumor core respectively; showed high accuracy in segmenting the complex tumor structures from multi-contrast MR scans of HGG patients. We also compared our methodology in terms of segmentation efficiency with some recent techniques reported in proceedings of MICCAI-BRATS challenge 2015.
Multiple system atrophy (MSA) is a rare, non-curable, progressive neurodegenerative disorder that affects nervous system and movement, poses a considerable diagnostic challenge to medical researchers. Corpus callosum (CC) being the largest white matter structure in brain, enabling inter-hemispheric communication, quantification of callosal atrophy may provide vital information at the earliest possible stages. The main objective is to identify the differences in CC structure for this disease, based on quantitative analysis on the pattern of callosal atrophy. We report results of quantification of structural changes in regional anatomical thickness, area and length of CC between patient-groups with MSA with respect to healthy controls. The method utilizes isolating and parcellating the mid-sagittal CC into 100 segments along the length - measuring the width of each segment. It also measures areas within geometrically defined five callosal compartments of the well-known Witelson, and Hofer-Frahma schemes. For quantification, statistical tests are performed on these different callosal measurements. From the statistical analysis, it is concluded that compared to healthy controls, width is reduced drastically throughout CC for MSA group and as well as changes in area and length are also significant for MSA. The study is further extended to check if any significant difference in thickness is found between the two variations of MSA, Parkinsonian MSA and Cerebellar MSA group, using the same methodology. However area and length of this two sub-MSA group, no substantial difference is obtained. The study is performed on twenty subjects for each control and MSA group, who had T1-weighted MRI.