Background: the process of cardiac segmentation using cardiac MRI images has been widely studied, and various deep learning models have been employed to address the complexities of heart chamber segmentation. Among these models, the 2-Dimensional (2D) UNet has demonstrated good performance in segmenting the left and right ventricles but has not been utilized to differential between the myocardium and papillary muscles. Consequently, researchers have proposed the use of the 3-Dimensional (3D) UNet as an alternative to the 2D UNet to improve segmentation outcomes. This study aims to compare the accuracy of 2D and 3D UNet models in segmenting the left ventricle using MRI images. Method: Both models were trained and tested on public ACDC dataset including 150 patients. Both models were trained for 140 epochs. To compare the accuracy of 2D and 3D UNet models, Dice Score Coefficient (DSC) and Hausdorff Distance (HD) were computed. Results: The 2D model achieved a mean Dice of 0.851 and a mean HD of 4.31 mm while the 3D UNet model achieved a higher performance in comparison with the 2D model with a mean Dice of 0.950 and a mean HD of 3.14 mm. Conclusion: The outcome of this study showed that 3D UNet is more suitable for the cardiac MRI segmentation.

The objective of this study was to assess the viability of establishing an interactive Virtual National Library of Medicine (VNL-M). This research proposes a solution that leverages machine learning algorithms and cloud computing technologies, including advanced techniques such as Hadoop, MapReduce, and Cascading, to virtualize a significant number of medical manuscripts from the National Library of Tunisia. The proposed solution has two phases: •The first presents a hybrid (KNN/SVM) approach for an optical character recognition (OCR) system. Because this technology necessitates high bandwidth and computational capacity, spreading it via distributed architecture or platforms may be a viable alternative for improving performance. •The second entails considering cloud computing as an infrastructure (IaaS) to deploy virtualization techniques for the National Library of Tunisia's “medicine and health” subject area, which belongs to the “natural sciences and mathematics” class in the Dewey Decimal Classification (DDC) system. Furthermore, Cloud storing as a Service (SaaS) is employed for the storing and retrieval of enormous amounts of medical manuscript information. The proposed solution was evaluated by conducting experiments using S3 and Amazon EC2 Elstic Map Reduce with an interesting-scale dataset from a database of manuscript heritage. Lastly, the VNL-M is published as a Web service (VNL-Mweb Service).

The cardiac right ventricle has a vital role in the cardiac cycle. To assess its function using Magnetic Resonance Imaging (MRI), the segmentation is an important task, but it is challenged by the complex shape of this cavity, its thin borders, and shape variability. Accordingly, several approaches have been proposed to overcome these issues. Yet, a significant divergence of precision still appears among the spatial slices. In this paper, we attempt to study the impact of short-axis slices from base to apex on the segmentation process. First, a comparative study is enabled to assess the segmentation quality among these slices using a U-Net- based convolutional neural network. Two public labelled datasets are exploited with our prepared data to allow the training process. The dice-coefficient assessment of each slice-level exhibits a significant accuracy decrease for the basal and apical slices. Next, a personalized investigation is carried out for each slice level apart. Accordingly, three sub-sets are retrieved from the initial training set regrouping slices into basal, central, and apical. Furthermore, to monitor the segmentation behaviour using these sub-datasets, different U-Net-based models are trained and evaluated. The obtained results show that the central slices scores enhanced from 0.87 to 0.92 using slice-level based. On the other hand, basal and apical slices obtained higher results using the global dataset.

Abstract The right ventricular assessment is crucial to heart disease diagnosis. Unfortunately, its segmentation is quite challenging due to its intricate shape, ill‐defined thin edges, large variability among patients, and pathologies. Besides, it is a very laborious and time‐consuming task to be done manually. Therefore, automated segmentation techniques are very suitable to reduce the strain on the expert. Here, it is attempted to review the taxonomy of the current RV segmentation approaches adopted to handle the afore‐mentioned issues. Enhanced by our expert's interpretation, the results of over forty research papers were evaluated based on several metrics such as the dice metric and the Hausdorff distance. Synthetic tables and charts were also used to discuss the reviewed approaches. The following study shows that none of the existing methods has proved accurate enough to meet all the RV challenging issues. Many misestimated results were reported for several cases. Eventually, global guidance is outlined, which supports combining different methods to enhance the expected results during the MRI short‐axis slice processing.

Magnetic resonance imaging (MRI) has emerged as the golden reference for cardiac examination. This modality allows the assessment of human cardiovascular morphology, functioning, and perfusion. Although a couple of challenging issues, such as the cardiac magnetic resonance (MR) image's features and the large variability of images among several patients, still influences the cardiac cavities’ segmentation and needs to be carried out. In this study, the authors have profoundly reviewed and fully compared semi‐automated segmentation methods performed on cardiac cine‐MR short‐axis images for the evaluation of the left ventricular functions. However, the number of parameters handled by the synthesised works is limited if not null. For the sake of ensuring the highest coverage of the left ventricle parameters computing, they have introduced a parallel watershed‐based approach to segment the left ventricular allowing hence the computation of six parameters (end‐diastolic volume, end‐systolic volume, ejection fraction, cardiac output, stroke volume, and left ventricular mass). An algorithm is associated with the main considered measurements. The experimental results that were obtained through studying 20 patients’ MRI data base demonstrate their approach's accuracy in estimating real values of the parameters’ set thanks to a faithful segmentation of the myocardium.

Image processing technology has grown significantly over the past decade. Its application on low-power mobile devices has been the interest of a wide research group related to newly emerging contexts such as augmented reality, visual search, object recognition, and so on. With the emergence of general-purpose computing on embedded GPUs and their programming models like OpenGL ES 2.0 and OpenCL, mobile processors are gaining a more parallel computing capability. Thereby, the adaptation of these advancements for accelerating mobile image processing algorithms has become actually an important topical issue. In this paper, our interest is based on reviewing recent challenging tasks related to mobile image processing using both serial and parallel computing approaches in several emerging application contexts.