  Cardiac magnetic resonance imaging (MRI) is a pivotal tool for assessing
cardiac function. Precise segmentation of cardiac structures is imperative for
accurate cardiac functional evaluation. This paper introduces a semi-supervised
model for automatic segmentation of cardiac images and auxiliary diagnosis. By
harnessing cardiac MRI images and necessitating only a small portion of
annotated image data, the model achieves fully automated, high-precision
segmentation of cardiac images, extraction of features, calculation of clinical
indices, and prediction of diseases. The provided segmentation results,
clinical indices, and prediction outcomes can aid physicians in diagnosis,
thereby serving as auxiliary diagnostic tools. Experimental results showcase
that this semi-supervised model for automatic segmentation of cardiac images
and auxiliary diagnosis attains high accuracy in segmentation and correctness
in prediction, demonstrating substantial practical guidance and application
value.
