Strategies To Combine 3D Vasculature and Brain CTA With Deep Neural Networks: Application To LVO

Authors

Uma M Lal-Trehan Estrada
Arnau Oliver
Sunil A Sheth
Xavier Lladó
Luca Giancardo

Language

English

Publication Date

2-16-2024

Journal

iScience

DOI

10.1016/j.isci.2024.108881

PMID

38318348

PMCID

PMC10838777

PubMedCentral® Posted Date

January 2024

PubMedCentral® Full Text Version

Post-Print

Abstract

Automated tools to detect large vessel occlusion (LVO) in acute ischemic stroke patients using brain computed tomography angiography (CTA) have been shown to reduce the time for treatment, leading to better clinical outcomes. There is a lot of information in a single CTA and deep learning models do not have an obvious way of being conditioned on areas most relevant for LVO detection, i.e., the vasculature structure. In this work, we compare and contrast strategies to make convolutional neural networks focus on the vasculature without discarding context information of the brain parenchyma and propose an attention-inspired strategy to encourage this. We use brain CTAs from which we obtain 3D vasculature images. Then, we compare ways of combining the vasculature and the CTA images using a general-purpose network trained to detect LVO. The results show that the proposed strategies allow to improve LVO detection and could potentially help to learn other cerebrovascular-related tasks.

Keywords

Medical imaging, Neuroanatomy, Neural networks, Machine learning

Published Open-Access

yes

Recommended Citation

Lal-Trehan Estrada, Uma M; Oliver, Arnau; Sheth, Sunil A; et al., "Strategies To Combine 3D Vasculature and Brain CTA With Deep Neural Networks: Application To LVO" (2024). Faculty, Staff and Student Publications. 167.
https://digitalcommons.library.tmc.edu/uthshis_docs/167