Abstract
The left ventricle segmentation (LVS) is of great important for the evaluation of cardiac function. This study aimed to establish new segmentation algorithms that can enhance the accuracy and robustness of automatic LVS on magnetic resonance images. The datasets involved 45 subjects, including 12 heart failure patients with ischemia, 12 heart failure patients without ischemia, 12 hypertrophy patients and 9 normal individuals. The experiments consisted of three important steps. At first, deep learning was employed for the coarse LVS on myocardial images. Next, a double snake model was applied to assess the endo- and epi-cardial boundaries. Finally, the optimal epicardial boundary was obtained by adopting radial region growing method. Additionally, the performance of the developed LVS method was evaluated by the previously established software. Furthermore, the developed LVS method was validated by applying the datasets of 45 subjects. The results showed that the good contours, overlapping dice metric and average perpendicular distance of both epi- and endo-cardial contours were approximately 97%, 0.97 and 1.8 mm respectively. The regression coefficient and coefficient of determination between the proposed method and clinical experts were 0.96 and 1.039, respectively for ejection fraction, while 0.92 and 0.994 for left ventricle mass. These findings reveal that the developed method can enhance the accuracy and robustness of LVS. This novel LVS approach exhibits outstanding performance and possesses promising potential to increase the reliability of computer-aided imaging detection system for cardiovascular disease.
Highlights
Cardiovascular disease is the number one cause of mortality worldwide, which has emerged as the most serious health problem [1], [2]
Assessment of the left ventricle segmentation (LVS) has gained increasing attention, as it allows the direct measurement of essential parameters including myocardial mass, end-diastolic volume and ejection fraction
A total of 45 subjects were enrolled in this study, including heart failure patients with ischemia (HF-I; n = 12), heart failure patients without ischemia (HF-NI; n = 12), hypertrophy patients (HYP; n = 12) and normal individuals (N; n = 9) Their cardiac MRI images were acquired during breath-hold sessions lasting for 10 to 15 s with a temporal resolution of 20 cardiac phases per cardiac cycle
Summary
Cardiovascular disease is the number one cause of mortality worldwide, which has emerged as the most serious health problem [1], [2]. Assessment of the left ventricle segmentation (LVS) has gained increasing attention, as it allows the direct measurement of essential parameters including myocardial mass, end-diastolic volume and ejection fraction. It is necessary to establish a novel automatic extraction method of the left ventricular. Considerable efforts have been devoted to the establishment of automatic LVS, this remains a difficult clinical problem. There have been extensive researches to overcome the potential shortcomings of LVS, such as dynamic programming (DP) [4]–[6], snake models [7]–[9], thresholding [10], region growing [11], pixel classification [12]–[14]. Among the above-mentioned approaches, the snake model has been commonly used to solve a wide range of segmentation issues, including LVS.
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