Semi-Supervised Gan for medical image segmentation

Abstract

Echocardiography is a popular ultrasound imaging method used for the diagnosis of heart conditions. With the advent of numerous image processing algorithms, echocardiographic image segmentation has become more significant. This is a crucial stage since it offers a framework for evaluating numerous cardiac parameters, including LV volume and heart wall, valve motion, ejection fraction, thickness, etc. All these factors are crucial for determining a heart's health. The task of manual segmentation requires skilled operators and takes a lot of time. By requiring the discriminator network to output class labels, we extend Generative Adversarial Networks to the semi-supervised type. This paper examines image segmentation techniques for echocardiography to find the borders of the left ventricle. In this paper, we introduce a new convolution neural network model for the auto-segmentation of the left ventricle in echo images. The division of a picture into regions is known as image segmentation. Segments, that computer vision can use to automatically understand. This method makes it easier to simultaneously evaluate and diagnose echo pictures. The segmentation of echocardiographic images can be utilized to measure cardiac characteristics like heart wall thickness.