@inproceedings{d078e37687404a2591a8283e9cfdada4,
title = "Deep-learning based tractography for neonates",
abstract = "Generation of white matter (WM) tractography for neonates has primarily depended on a successful development of a diffusion tensor imaging (DTI)-based ATLAS. In this study, we present a deep-learning framework for WM tractography of neonates{\textquoteright} brains that is independent of any specific ATLAS. A convolutional neural network (CNN)-based deep-learning architecture is proposed for automated generation of WM tractography. Our dataset consists of DWI scans of 40 neonates that were used to train the model. Although the proposed model is adopted for WM tractography, it can generally be applied for subcortical structures and cerebellum.",
keywords = "Convolutional neural network, Deep-learning, Tractography, White-matter",
author = "Sovanlal Mukherjee and Natacha Paquette and Niharika Gajawelli and Yalin Wang and Julia Wallace and Nelson, {Marvin D.} and Ashok Panigrahy and Natasha Lepore",
note = "Publisher Copyright: {\textcopyright} 2020 SPIE Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 16th International Symposium on Medical Information Processing and Analysis 2020 ; Conference date: 03-10-2020 Through 04-10-2020",
year = "2020",
doi = "10.1117/12.2579609",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Eduardo Romero and Natasha Lepore and Jorge Brieva and Marius Linguraru",
booktitle = "16th International Symposium on Medical Information Processing and Analysis",
}