2017/12/28 by Shreyas Malakarjun Patil, Aditya Nigam, Patil, Shreyas Malakarjun +5
Computer Science · Medicine · #Advanced Neuroimaging Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Image Segmentation Techniques #Speech Recognition and Synthesis #cs.CV
paper · pdf · doi:10.48550/arxiv.1712.09792
arxiv created 2017/12/28 · openalex publication_date 2017/12/28 · arxiv updated 2017/12/29 · openalex created_date 2018/01/05 · openalex updated_date 2026/07/28
In this paper, we propose a novel deep learning architecture combining stacked Bi-directional LSTM and LSTMs with the Siamese network architecture for segmentation of brain fibers, obtained from tractography data, into anatomically meaningful clusters. The proposed network learns the structural difference between fibers of different classes, which enables it to classify fibers with high accuracy. Importantly, capturing such deep inter and intra class structural relationship also ensures that the segmentation is robust to relative rotation among test and training data, hence can be used with unregistered data. Our extensive experimentation over order of hundred-thousands of fibers show that the proposed model achieves state-of-the-art results, even in cases of large relative rotations between test and training data.