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Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.

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This paper elucidate on the effects of Artocarpus Altilis Pulp part on cervix HeLa cancer cell. IC50 values of pulp extract were determined on HeLa cell with different concentration (12.5µg/ml, 25µg/ml, 50µg/ml, 100µg/ml). Cell viability and cell growth were observed up to 72 hours with comparative to control cells. The results obtained in this research quantitatively revealed the dependence of cell proliferation on extract concentration. Control, 12.5µg/ml, 25µg/ml, 50µg/ml, 100µg/ml of concentration showed 100%, 90%, 80%, 50%, 44% cell viability after 72 hours in culture respectively. This study result demonstrates that Artocarpus Altilis has the ability to inhibit cervical cancer cell proliferation.