Patent ID: 11941811
Assignee: CHINA MEDICAL UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 9:
10. A cardiothoracic ratio assessment system, comprising:
an image capturing device for capturing a testing X-ray image database of a subject; and
a processer electronically connected to the image capturing device, wherein the processer comprises a reference X-ray image database and a cardiothoracic ratio assessment program, and the reference X-ray image database comprises a plurality of reference X-ray image datasets;
wherein the cardiothoracic ratio assessment program comprises:
a first reference image data classifying module for classifying each of the reference X-ray image datasets by the first deep learning neural network classifier so as to obtain a plurality of training chest X-ray image data, wherein each of the training chest X-ray image data comprises a PA chest X-ray image data or an AP chest X-ray image data;
a second reference image data classifying module for classifying the training chest X-ray image data by the second deep learning neural network classifier so as to obtain a plurality of training PA chest X-ray image data;
a training module for capturing a diameter of thoracic cavity and a diameter of cardiac silhouette of each of the training PA chest X-ray image data by a deep learning calculating module automatically, wherein the diameter of thoracic cavity and the diameter of cardiac silhouette of the training PA chest X-ray image data are trained to achieve a convergence by the deep learning calculating module so as to obtain a third deep learning neural network classifier;
a first target image data classifying module for classifying the testing X-ray image database by the first deep learning neural network classifier so as to obtain a testing chest X-ray image data, wherein the testing chest X-ray image data comprises a PA chest X-ray image data or an AP chest X-ray image data;
a second target image data classifying module for classifying the testing chest X-ray image data by the second deep learning neural network classifier so as to obtain a target chest X-ray image data; and
a comparing module for capturing a diameter of thoracic cavity and a diameter of cardiac silhouette of the target chest X-ray image data by the third deep learning neural network automatically, wherein the diameter of thoracic cavity and the diameter of cardiac silhouette of the target chest X-ray image data are trained to achieve a convergence by the third deep learning neural network so as to obtain a feature of CTR, and an assessing result of CTR is obtained according to the feature of CTR by the third deep learning neural network classifier.