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Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning Baskaran, Lohendran; Al’Aref, Subhi J.; Maliakal, Gabriel; Lee, Benjamin C.; Xu, Zhuoran; Choi, Jeong W.; Lee, Sang-Eun; Sung, Ji Min; Lin, Fay Y.; Dunham, Simon; Mosadegh, Bobak; Kim, Yong-Jin; Gottlieb, Ilan; Lee, Byoung Kwon; Chun, Eun Ju; Cademartiri, Filippo; Maffei, Erica; Marques, Hugo; Shin, Sanghoon; Choi, Jung Hyun; Chinnaiyan, Kavitha; Hadamitzky, Martin; Conte, Edoardo; Andreini, Daniele; Pontone, Gianluca; Budoff, Matthew J.; Leipsic, Jonathon A.; Raff, Gilbert L.; Virmani, Renu; Samady, Habib; Stone, Peter H.; Berman, Daniel S.; Narula, Jagat; Bax, Jeroen J.; Chang, Hyuk-Jae; Min, James K.; Shaw, Leslee J.

Description

<b>Abstract</b><br/>

<b>Objectives: </b>To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.</p>

<b>Background: </b>Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.</p>

<b>Methods: </b>Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split.</p>

<b>Results: </b>The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups.</p>

<b>Conclusions: </b>An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.</p>

Item Metadata

Title
Data from: Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning
Creator
Baskaran, Lohendran; Al’Aref, Subhi J.; Maliakal, Gabriel; Lee, Benjamin C.; Xu, Zhuoran; Choi, Jeong W.; Lee, Sang-Eun; Sung, Ji Min; Lin, Fay Y.; Dunham, Simon; Mosadegh, Bobak; Kim, Yong-Jin; Gottlieb, Ilan; Lee, Byoung Kwon; Chun, Eun Ju; Cademartiri, Filippo; Maffei, Erica; Marques, Hugo; Shin, Sanghoon; Choi, Jung Hyun; Chinnaiyan, Kavitha; Hadamitzky, Martin; Conte, Edoardo; Andreini, Daniele; Pontone, Gianluca; Budoff, Matthew J.; Leipsic, Jonathon A.; Raff, Gilbert L.; Virmani, Renu; Samady, Habib; Stone, Peter H.; Berman, Daniel S.; Narula, Jagat; Bax, Jeroen J.; Chang, Hyuk-Jae; Min, James K.; Shaw, Leslee J.
Date Issued
2021-05-19
Description
<b>Abstract</b><br/>

<b>Objectives: </b>To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.</p>

<b>Background: </b>Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images.</p>

<b>Methods: </b>Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split.</p>

<b>Results: </b>The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups.</p>

<b>Conclusions: </b>An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.</p>

Subject
Other; Aorta; Cardiovascular imaging; Pulmonary arteries; Veins; Computed axial tomography; Coronary arteries; Angiography
Type
Dataset
Notes

Dryad version number: 5</p>

Version status: submitted</p>

Dryad curation status: Published</p>

Sharing link: https://datadryad.org/stash/share/m8dHr_yFONWlQoLkRXOXjLs7zh7dzFgynEI3vpzYgfc</p>

Storage size: 425479421</p>

Visibility: public</p>

Date Available
2020-05-28
Provider
University of British Columbia Library
License
CC0 1.0
DOI
10.14288/1.0397762
URI
https://doi.org/10.5683/SP2/K1INBL
Publisher DOI
https://doi.org/10.5683/SP2/K1INBL; https://doi.org/10.5061/dryad.9s4mw6mc9
Rights URI
http://creativecommons.org/publicdomain/zero/1.0
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CC0 1.0