Hybrid Network Model for Cardiac Image Segmentation Using MRI Images

IF 1 Q4 OPTICS
A. Rasmi
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引用次数: 0

Abstract

Cardiac magnetic resonance imaging (MRI) commonly yields numerous images per scan, and manually delineating structures from these images is a laborious and time-intensive task. The automation of this process is highly desirable as it would enable the generation of crucial clinical measurements like ejection fraction and stroke volume. However, due to variations in scanning settings and patient characteristics, automated segmentation faces several challenges that lead to a high degree of variability in picture statistics and quality. Our study presents a neural network approach that utilizes the UNet and ResNet-50 architectures to efficiently partition the left and right ventricles' endocardial and epicardial boundaries. The Dice metric is used as the loss function in our strategy to maximize the trainable parameters in the network. Additionally, in the neural network’s predicted binary picture, we employed a preprocessing step to save just the segmentation labels' most connected component. Using datasets from the Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge, the suggested method was learned. The test set of 160 that had been reserved for testing was used by the challenge organizers to evaluate the approach.

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来源期刊
CiteScore
1.50
自引率
11.10%
发文量
25
期刊介绍: The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
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