Although the mental workload confronted by laparoscopic surgeons is rather high, there is presently no reliable, established method for evaluating this workload. In the present study, four evaluation indices of eye movement metrics were applied to evaluate surgeons’ mental workload. Correlations between these indices and National Aeronautics and Space Administration Task Load Index (NASA-TLX) scores were also explored. Sixteen participants were recruited to complete four laparoscopic procedures. Eye movement was recorded during the tasks, and NASA-TLX scales were also introduced for subjective evaluation. The data were analyzed using R 3.3.2. Significant differences in the mental workload of each task were observed. Statistically significant correlations between mean pupil diameter change and NASA-TLX scores were also observed. The correlation coefficients were 0.763, 0.675, 0.405, and 0.547, and the P values correspondingly were 0.001, 0.004, 0.12, and 0.028, respectively. The results clarify that the mental workload of laparoscopic surgeons is dependent on the specific demands of the operation. Appropriate objective physiological indices can be used to identify the mental workload state of the surgeon.
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.INDEX TERMS Convolutional neural network, snake model, left ventricle segmentation, magnetic resonance imaging, cardiac functional analysis.
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