Korean J Radiol.  2020 Jun;21(6):660-669. 10.3348/kjr.2019.0378.

Automated Segmentation of Left Ventricular Myocardium on Cardiac Computed Tomography Using Deep Learning

• Koo HJ
• Lee JG
• Ko JY
• Lee G
• Kang JW
• Kim YH
• Yang DH

• KMID: 2502467
• DOI: http://doi.org/10.3348/kjr.2019.0378

Abstract

Objective
To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT.
Materials and Methods
To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks.
Results
The sensitivity and specificity of automated segmentation for each segment (1–16 segments) were high (85.5– 100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks.
Conclusion
We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

Keyword

Segmentation; Left ventricle; Deep learning; Machine learning; Computed tomography