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.

Cardiovascular diseases (CVDs) are considered one of the leading causes of death worldwide. Myocardial infarction (MI) is one of the deadliest cardiac diseases that require more consideration. Recently, cardiac magnetic resonance imaging (MRI) has been applied as a standard technique for assessing such diseases. The segmentation of the left ventricle (LV) and myocardium from MRI images is vital in detecting MI disease at its early stages. The automatic segmentation of LV is still challenging due to the complex structures of MRI images, inhomogeneous LV shape and moving organs around the LV, such as the lungs and diaphragm. Thus, this study proposed a convolutional neural network (CNN) model for LV and myocardium segmentation to detect MI. The layers selection and hyper-parameters fine-tuning were applied before the training phase. The model showed robust performance based on the evaluation metrics such as accuracy, sensitivity, specificity, dice score coefficient (DSC), Jaccard index and intersection over union (IOU) with values of 0.86, 0.91, 0.84, 0.81, 0.69 and 0.83, respectively.

The automatic localization of the left ventricle (LV) in short-axis magnetic resonance (MR) images is a required step to process cardiac images using convolutional neural networks for the extraction of a region of interest (ROI). The precise extraction of the LV’s ROI from cardiac MRI images is crucial for detecting heart disorders via cardiac segmentation or registration. Nevertheless, this task appears to be intricate due to the diversities in the size and shape of the LV and the scattering of surrounding tissues across different slices. Thus, this study proposed a region-based convolutional network (Faster R-CNN) for the LV localization from short-axis cardiac MRI images using a region proposal network (RPN) integrated with deep feature classification and regression. The model was trained using images with corresponding bounding boxes (labels) around the LV, and various experiments were applied to select the appropriate layers and set the suitable hyper-parameters. The experimental findings show that the proposed model was adequate, with accuracy, precision, recall, and F1 score values of 0.91, 0.94, 0.95, and 0.95, respectively. This model also allows the cropping of the detected area of LV, which is vital in reducing the computational cost and time during segmentation and classification procedures. Therefore, it would be an ideal model and clinically applicable for diagnosing cardiac diseases.

Arm rehabilitation activities need to be monitored continuously in terms of analysis by experts within sufficient information to discover arm dysfunction and disorders such as stroke early. Although there are numerous previous researches about the home-based rehabilitation procedures and arm performance, some drawbacks still exist. For example, current rehabilitation devices are too complicated and required supervision by qualified therapists rather than their high prices. Moreover, data from these devices take much time to be sent to doctors for monitoring purpose. Therefore, this paper proposes a home-based online multisensory arm rehabilitation monitoring system. This system is designed through three main stages which are data acquisition, data processing, and data logging respectively. Data acquisition is by three different types of sensors; flex sensor (for arm’s bending), force-sensitive resistors (for hand fingers’ forces) and accelerometer (for arm movement directions). In data processing, Arduino Mega Controller and ESP Wi-Fi shield are used to design Internet of Things (IoT) web-based system (ThingSpeak) for data logging that allows doctors to diagnose stroke recovery and give feedbacks for patients. Overall, this project showed a robust system for arm rehabilitation using portable, user-friendly, and low power consumption devices at a low cost.