2017/10/14 by Tushar Gupta, Shreyas Malakarjun Patil, Gupta, Tushar +7 · 3 citations
Computer Science · Medicine · #Advanced Neuroimaging Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Fetal and Pediatric Neurological Disorders #Speech Recognition and Synthesis #cs.CV
paper · pdf · doi:10.48550/arxiv.1710.05158
Deep Learning in Irregular Domains - British Machine Vision Conference (DLID-BMVC)
arxiv created 2017/10/14 · openalex publication_date 2017/10/14 · arxiv updated 2017/10/17 · openalex created_date 2017/11/10 · openalex updated_date 2026/07/28
The segregation of brain fiber tractography data into distinct and anatomically meaningful clusters can help to comprehend the complex brain structure and early investigation and management of various neural disorders. We propose a novel stacked bidirectional long short-term memory(LSTM) based segmentation network, (BrainSegNet) for human brain fiber tractography data classification. We perform a two-level hierarchical classification a) White vs Grey matter (Macro) and b) White matter clusters (Micro). BrainSegNet is trained over three brain tractography data having over 250,000 fibers each. Our experimental evaluation shows that our model achieves state-of-the-art results. We have performed inter as well as intra class testing over three patient's brain tractography data and achieved a high classification accuracy for both macro and micro levels both under intra as well as inter brain testing scenario.