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The segregation of brain fiber tractography data into distinct andanatomically meaningful clusters can help to comprehend the complex brainstructure and early investigation and management of various neural disorders.We propose a novel stacked bidirectional long short-term memory(LSTM) basedsegmentation network, (BrainSegNet) for human brain fiber tractography dataclassification. We perform a two-level hierarchical classification a) White vsGrey matter (Macro) and b) White matter clusters (Micro). BrainSegNet istrained over three brain tractography data having over 250,000 fibers each. Ourexperimental evaluation shows that our model achieves state-of-the-art results.We have performed inter as well as intra class testing over three patient'sbrain tractography data and achieved a high classification accuracy for bothmacro and micro levels both under intra as well as inter brain testingscenario.

BrainSegNet : A Segmentation Network for Human Brain Fiber Tractography Data into Anatomically Meaningful Clusters