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 or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ 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