BrainSegNet : A Segmentation Network for Human Brain Fiber Tractography Data into Anatomically Meaningful Clusters
This work addresses the segmentation of brain fiber tractography for medical researchers and clinicians, but it is incremental as it applies a novel method to a known bottleneck in neuroimaging.
The authors tackled the problem of segregating brain fiber tractography data into anatomically meaningful clusters to aid in understanding brain structure and managing neural disorders, achieving state-of-the-art results with high classification accuracy in both macro and micro levels across intra and inter brain testing scenarios.
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.