Radiologically based automated segmentation of cardiac MRI using an improved U-Net neural algorithm

Abstract

ObjectiveTo overcome the complexity and variability of the heart, along with factors like fuzzy boundaries and low contrast and produce automated segmentation based on machine learning. MethodsSegmentation of cardiac MRI plays a vital role in various clinical applications. In this study, we propose an improved U-Net-CSP (Cross Stage Partial) method for accurate and robust segmentation of cardiac MRI images.The U-Net-CSP architecture combines the U-Net framework with the CSP module to enhance the segmentation performance. The CSP module enables improved feature reuse and mitigates overfitting issues associated with deep networks. By incorporating the CSP module in both the encoding and decoding stages, the U-Net-CSP effectively captures complex features and preserves important details during the upsampling and downsampling processes.To optimize the segmentation results, we utilize a combination of weighted cross-entropy loss, dice loss, and L2 regularization as the loss function. This loss function facilitates accurate delineation of the regions of interest and enhances the segmentation performance of the U-Net-CSP model. ResultsThe proposed U-Net-CSP method achieves superior performance in cardiac MRI segmentation, surpassing conventional U-Net models and state-of-the-art methods. It demonstrates potential for clinical applications in cardiac therapy, disease research, and model reconstruction. Our method automates segmentation of the left ventricle, right ventricle, and aorta from cardiac MRI images. Comparative analyses with leading methods show consistent favorable outcomes under diverse conditions. The U-Net-CSP approach accurately delineates cardiac structures from MRI scans, contributing to medical image analysis and advancing cardiac healthcare. ConclusionIn conclusion, the proposed U-Net-CSP method provides an enhanced approach for cardiac MRI segmentation, overcoming the challenges posed by complex cardiac structures and image characteristics. This study contributes to the advancement of medical image analysis techniques, particularly in the field of cardiac MRI segmentation.