In PloS one ; h5-index 176.0
OBJECTIVES : To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation.
BACKGROUND : 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.
METHODS : 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.
RESULTS : 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.
CONCLUSIONS : An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level.
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