Paper 1: Computer Vision-based Efficient Segmentation Method for Left Ventricular Epicardium and Endocardium using Deep Learning
Abstract: Segmentation of the Left Ventricular Epicardium and Endocardium remains challenging and significant for valuable investigation of cardiac image classification. Previous research methods did not consider the flexibility of the heart area, so measurements needed to be more consistent and accurate. In addition, previous methods ignored the presence of affectability and additional parts, such as the lung organ inside the frame, during segmentation. Deep learning architectures, specifically convolutional neural networks, have become the primary choice for assessing cardiac medical images. In this context, a Convolutional Neural Network (CNN) can be an effective way to segment the left ventricular epicardium and endocardium as CNN can take data pictures, move enormity to various centers or objects in the image and have the choice to separate one from the other. This research proposes an efficient method for segmenting the left ventricular epicardium and endocardium using the InceptionV3 convolutional neural network. Rather than including fully connected layers on the head of the component maps, the proposed method considers the average of each element map, and the subsequent vector was taken care of legitimately into the SoftMax layer. Data augmentation technique was used to validate the proposed method on large number of dataset images. Besides, the proposed method was validated in publicly available MRI cardiac image datasets. Comprehensive experimental analysis was done by analyzing a large number of performance metrics, i.e., cosine similarity, log cos error, mean absolute error, mean absolute percentage error, mean squared error, mean squared logarithmic error, and root mean squared error. The proposed method depicted superior performance for localization of the left ventricular epicardium and endocardium in terms of all these performance metrics. In addition, the proposed method performed efficiently to get smooth curve for covering the region due to usage of interpolation technique to draw the curve, which made it smoother compared with previous research.
Keywords: Convolutional neural network; segmentation; computer vision; deep learning