eprintid: 10130338
rev_number: 38
eprint_status: archive
userid: 608
dir: disk0/10/13/03/38
datestamp: 2021-06-30 10:22:15
lastmod: 2021-12-02 23:27:59
status_changed: 2021-11-26 14:14:31
type: article
metadata_visibility: show
creators_name: Jacob, J
title: A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning
ispublished: pub
divisions: UCL
divisions: B02
divisions: C10
divisions: D17
divisions: K71
divisions: D14
divisions: GA4
divisions: GA3
divisions: GA2
divisions: B04
divisions: C05
divisions: F48
divisions: F45
note: This work is licensed under a Creative Commons Attribution 4.0 International License. The images
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abstract: Early detection and diagnosis of coronary artery disease could reduce the risk of developing a heart attack. The coronary arteries are optimally visualised using computed tomography coronary angiography (CTCA) imaging. These images are reviewed by specialist radiologists who evaluate the coronary arteries for potential narrowing. A lack of radiologists in the UK is a constraint to timely diagnosis of coronary artery disease, particularly in the acute accident and emergency department setting. The development of automated methods by which coronary artery narrowing can be identified rapidly and accurately are therefore timely. Such complex computer based tools also need to be sufficiently computationally efficient that they can run on servers typically found in hospital settings, where graphical processing units for example are unavailable. We propose a fully automatic two-dimensional Unet model to segment the aorta and coronary arteries on CTCA images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Importantly, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed without requiring graphical processing units, and therefore can be used in a hospital setting.
date: 2021-07-21
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/ACCESS.2021.3099030
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1873147
doi: 10.1109/ACCESS.2021.3099030
lyricists_name: Alexander, Daniel
lyricists_name: Bell, Robert
lyricists_name: Cheung, Wing Keung
lyricists_name: Davies, Rhodri
lyricists_name: Jacob, Joseph
lyricists_name: Moon, James
lyricists_name: Patel, Riyaz
lyricists_name: Torii, Ryo
lyricists_id: DALEX06
lyricists_id: RMBEL96
lyricists_id: WKCHE76
lyricists_id: RDAVA45
lyricists_id: JJACO76
lyricists_id: JMOON31
lyricists_id: RSPAT27
lyricists_id: RTORI16
actors_name: Jacob, Joseph
actors_id: JJACO76
actors_role: owner
full_text_status: public
publication: IEEE Access
volume: 9
pagerange: 108873-108888
citation:        Jacob, J;      (2021)    A computationally efficient approach to segmentation of the aorta and coronary arteries using deep learning.                   IEEE Access , 9    pp. 108873-108888.    10.1109/ACCESS.2021.3099030 <https://doi.org/10.1109/ACCESS.2021.3099030>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10130338/1/Jacob_A_Computationally_Efficient_Approach_to_Segmentation_of_the_Aorta_and_Coronary_Arteries_Using_Deep_Learning.pdf