Deep-learning based tractography for neonates

Sovanlal Mukherjee; Natacha Paquette; Marvin D. Nelson; Yalin Wang; Julia Wallace; Ashok Panigrahy; Natasha Lepore

doi:10.1117/12.2579609

3 November 2020 Deep-learning based tractography for neonates

Sovanlal Mukherjee, Natacha Paquette, Marvin D. Nelson, Yalin Wang, Julia Wallace, Ashok Panigrahy, Natasha Lepore

Proceedings Volume 11583, 16th International Symposium on Medical Information Processing and Analysis; 115830A (2020) https://doi.org/10.1117/12.2579609
Event: The 16th International Symposium on Medical Information Processing and Analysis, 2020, Lima, Peru

Abstract

Generation of white matter (WM) tractography for neonates primarily depends on a successful development of a diffuse tensor imaging (DTI)-based ATLAS. In this study, we present a deep-learning framework for WM tractography of neonates’ brain 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 DTI scan of 37 neonates (18 preterm and 19 term-born) that can be used to train the model. Although the proposed model is adopted for WM tractography, it can generally be applied for subcortical structures and cerebellum.

Citation Download Citation

Sovanlal Mukherjee, Natacha Paquette, Marvin D. Nelson, Yalin Wang, Julia Wallace, Ashok Panigrahy, and Natasha Lepore "Deep-learning based tractography for neonates", Proc. SPIE 11583, 16th International Symposium on Medical Information Processing and Analysis, 115830A (3 November 2020); https://doi.org/10.1117/12.2579609

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available