Comparing different head MRI segmentation techniques for use in EEG source analysis
Creators: name: Abinash Pant
name: Cho, Jae-Hyun
name: Wolters, Carsten
name: Xiaoyi  Jiang
name: Bornfleth, Harald
Publisher: name: F1000 Research Limited
Year: 2019

description: Introduction<br><br>Accurate segmentation of an individual human head and the resulting volume conductor model has been known to improve EEG and MEG source analysis. However, comparison of segmentation of state-of-the-art techniques have not been compared and their effectiveness in source analysis has not been evaluated. Multi-Atlas segmentation has been popular and is used extensively in segmenting MR images. They are adopted as a large data set can generalize the brain's shape and appearance which can vary a lot between different groups and acquisition sequence and removes any bias introduces by using a single atlas. Convolutional Neural Networks (CNNs) have become popular in solving classification problems. Its ability to learn new features that are much more discriminative than classical handcrafted features has increased its popularity further especially new classification problems where handcrafted features are still in their infancy. It has also been used in brain segmentation and has outperformed other methods in various open competition. This motivates us to explore and use these techniques for complete head segmentation. A comparison between these segmentation techniques on BrainWeb images have been performed and shows encouraging results for further research.<br><br>Method<br><br>The Multi-Atlas pipeline relies primarily on the consensus of the atlas thus a good registration technique is required. Various registration techniques were tested, but Aladin and F3D (Non-Rigid) were selected for the 2 step registration process. A classification step that involved fusion of labels from the registered atlas was performed. This step has been proven better than Majority voting techniques, STAPEL seems to give a better result compared to MV and STAPLE with weighted local similarity worked slightly better than STAPLE.<br>A simple 3D multi path pipeline inspired from deepmedic has been implemented. The 3D Convolutional Neural Network is trained to detect WM, GM, CSF, muscle and bone using the BrainWeb database. No registration is required but all images in the atlas are re-sampled to the same voxel size. Theano framework used as the base and GPU is used for faster-parallelized computation. An extra pathway for a sub-sampled image is used to exploit spatial cues and it is upsampled at the classification layer to maintain spatial consistency. The final architecture is 8 layers deep with a kernel of 3x3x3 at every layer with feature maps of size 30, 30, 40, 40, 40, 40, 50, 50 at every layer and receptive field of size 25x25x25.<br>Results<br> <br>Conclusion<br><br>The comparison of the segmentation techniques shows that 3D CNN outperforms multi-atlas based methods in segmenting WM, GM and CSF. This could be attributed to the structural variability of these 3 tissue classes across subjects which makes it a challenging problem for segmentation algorithms based on atlas consensus such as multi-atlas technique.  A preliminary test using 3D CNN for finer tissue classification yields promising results.