Attention-UNet architectures with pretrained backbones for multi-class cardiac MR image segmentation.

Segmentation architectures based on deep learning proficient extraordinary results in medical imaging technologies. Computed tomography (CT) images and Magnetic Resonance Imaging (MRI) in diagnosis and treatment are increasing and significantly support the diagnostic process by removing the bottlenecks of manual segmentation. Cardiac Magnetic Resonance Imaging (CMRI) is a state-of-the-art imaging technique used to acquire vital heart measurements and has received extensive attention from researchers for automatic segmentation. Deep learning methods offer high-precision segmentation but still pose several difficulties, such as pixel homogeneity in nearby organs. The motivated study using the attention mechanism approach was introduced for medical images for automated algorithms. The experiment focuses on observing the impact of the attention mechanism with and without pretrained backbone networks on the UNet model. For the same, three networks are considered: Attention-UNet, Attention-UNet with resnet50 pretrained backbone and Attention-UNet with densenet121 pretrained backbone. The experiments are performed on the ACDC Challenge 2017 dataset. The performance is evaluated by conducting a comparative analysis based on the Dice Coefficient, IoU Coefficient, and cross-entropy loss calculations. The Attention-UNet, Attention-UNet with resnet50 pretrained backbone, and Attention-UNet with densenet121 pretrained backbone networks obtained Dice Coefficients of 0.9889, 0.9720, and 0.9801, respectively, along with corresponding IoU scores of 0.9781, 0.9457, and 0.9612. Results compared with the state-of-the-art methods indicate that the methods are on par with, or even superior in terms of both the Dice coefficient and Intersection-over-union.

Epoch and accuracy based empirical study for cardiac MRI segmentation using deep learning technique.

Cardiac magnetic resonance imaging (CMRI) is a non-invasive imaging technique to analyse the structure and function of the heart. It was enhanced considerably over several years to deliver functional information for diagnosing and managing cardiovascular disease. CMRI image delivers non-invasive, clear access to the heart and great vessels. The segmentation of CMRI provides quantification parameters such as myocardial viability, ejection fraction, cardiac chamber volume, and morphological details. In general, experts interpret the CMR images by delineating the images manually. The manual segmentation process is time-consuming, and it has been observed that the final observation varied with the opinion of the different experts. Convolution neural network is a new-age technology that provides impressive results compared to manual ones. In this study convolution neural network model is used for the segmentation task. The neural network parameters have been optimized to perform on the novel data set for accurate predictions. With other parameters, epochs play an essential role in training the network, as the network should not be under-fitted or over-fitted. The relationship between the hyperparameter epoch and accuracy is established in the model. The model delivers the accuracy of 0.88 in terms of the IoU coefficient.

Editing the genome of common cereals (Rice and Wheat): techniques, applications, and industrial aspects.

Gene editing techniques have made a significant contribution to the development of better crops. Gene editing enables precise changes in the genome of crops, which can introduce new possibilities for altering the crops' traits. Since the last three decades, various gene editing techniques such as meganucleases, zinc finger nuclease (ZFN), transcription activator-like effector nuclease (TALEN), and clustered regularly interspersed short palindromic repeats (CRISPR)/Cas (CRISPR-associated proteins) have been discovered. In this review, we discuss various gene editing techniques and their applications to common cereals. Further, we elucidate the future of gene-edited crops, their regulatory features, and industrial aspects globally. To achieve this, we perform a comprehensive literature survey using databases such as PubMed, Web of Science, SCOPUS, Google Scholar etc. For the literature search, we used keywords such as gene editing, crop genome modification, CRISPR/Cas, ZFN, TALEN, meganucleases etc. With the advent of the CRISPR/Cas technology in the last decade, the future of gene editing has transitioned into a new dimension. The functionality of CRISPR/Cas in both DNA and RNA has increased through the use of various Cas enzymes and their orthologs. Constant research efforts in this direction have improved the gene editing process for crops by minimizing its off-target effects. Scientists also use computational tools, which help them to design experiments and analyze the results of gene editing experiments in advance. Gene editing has diverse potential applications. In the future, gene editing will open new avenues for solving more agricultural issues and boosting crop production, which may have great industrial prospects.