Population specific template construction and brain structure segmentation using deep learning methods

Raghav Mehta 


A brain template, such as MNI152 is a digital (magentic resonance image or MRI) representation of the brain in a reference coordinate system for the neuroscience research. Structural atlases, such as AAL and DKA, delineate the brain into cortical and subcortical structures which are used in Voxel Based Morphometry (VBM) and fMRI analysis. Many population specific templates, i.e. Chinese, Korean, etc., have been constructed recently. It was observed that there are morphological differences between the average brain of the eastern and the western population. In this thesis, we report on the development of a population specific brain template for the young Indian population. This is derived from a multi-centeric MRI dataset of 100 Indian adults (21 - 30 years old). Measurements made with this template indicated that the Indian brain, on average, is smaller in height and width compared to the Caucasian and the Chinese brain. A second problem this thesis examines is automated segmentation of cortical and non-cortical human brain structures, using multiple structural atlases. This has been hitherto approached using computationally expensive non-rigid registration followed by label fusion. We propose an alternative approach for this using a Convolutional Neural Network (CNN) which classifies a voxel into one of many structures. Evaluation of the proposed method on various datasets showed that the mean Dice coefficient varied from 0.844±0.031 to 0.743±0.019 for datasets with the least (32) and the most (134) number of labels, respectively. These figures are marginally better or on par with those obtained with the current state of the art methods on nearly all datasets, at a reduced computational time. We also propose an end-to-end trainable Fully Convolutional Neural Network (FCNN) architecture called the M-net, for segmenting deep (human) brain structures. A novel scheme is used to learn to combine and represent 3D context information of a given slice in a 2D slice. Consequently, the M-net utilizes only 2D convolution though it operates on 3D data. Experiment results show that the M-net outperforms other state-of-the-art model-based segmentation methods in terms of dice coefficient and is at least 3 times faster than them.


Year of completion:  July 2017
 Advisor : Jayanthi Sivaswamy

Related Publications

  • Jayanthi Sivaswamy, Thottupattu AJ , Mehta R, Sheelakumari R and Kesavadas C - Construction of Indian Human Brain Atlas, Neurology India (To appear).[PDF]

  • Majumdar A, Mehta R and Jayanthi Sivaswamy - To Learn Or Not To Learn Features For Deformable Registration? Deep Learning Fails, MICCAI 2018[PDF]

  • Raghav Mehta and Jayanthi Sivaswamy - M-net: A Convolutional Neural Network for deep brain structure segmentation Biomedical Imaging (ISBI 2017), 2017 IEEE 14th International Symposium on. IEEE, 2017. [PDF]

  • R Mehta, Aabhas Majumdar and Jayanthi Sivaswamy - BrainSegNet: a convolutional neural network architecture for automated segmentation of human brain structures Journal of Medical Imaging 4.2 (2017): 024003-024003. [PDF]

  • Raghav Mehta and Jayanthi SivaswamyA Hybrid Approach to Tissue-based Intensity Standardization of Brain MRI Images Proc. of IEEE International Symposium on Bio-Medical Imaging(ISBI), 2016, 13 - 16 April, 2016, Prague. [PDF]