Deep learning atrial septal defect detection method and device

Abstract

The invention provides a deep learning atrial septal defect detection method and device, and the method comprises the steps: obtaining an ultrasonic cardiogram, preprocessing the ultrasonic cardiogram, and extracting a region of interest; carrying out feature extraction on the region of interest, and identifying a subsword process atrial septal frontal section, a subsword process atrial septal sagittal section, a cardiac apex four-cavity cardiac tangent plane, a low-position paracarpal four-cavity cardiac tangent plane and a paracarpal aorta short-axis tangent plane; detecting the minimum distance point of the atrial septal defect; segmenting heart structures of a subsword atrial septal frontal section, a subsword atrial septal sagittal section, a cardiac apex four-cavity cardiac tangent plane, a low paracardial four-cavity cardiac tangent plane and a paracardial aorta minor axis section to obtain segmentation results, the heart structures including a left atrium, a right atrium, a left ventricle, a right ventricle, an aorta and pulmonary arteries; and according to the segmentation results, filtering the detected minimum distance point bounding box of the atrial septal defect, andtaking the filtered result as an atrial septal defect detection result.

Description

technical field [0001] The present invention relates to the field of computers, in particular to a deep learning method and device for detecting atrial septal defect. Background technique [0002] As a noninvasive diagnostic technique, two-dimensional echocardiography is the main imaging method for the diagnosis of atrial septal defect. The correct rate of diagnosis of congenital heart disease by echocardiography is closely related to the technical level of the operating doctor. An excellent echocardiography doctor not only has high-level operation skills, image recognition and interpretation capabilities, but also has a comprehensive understanding of various diseases of congenital heart disease. Have holistic understanding. Due to the high technical level requirements and long personnel training cycle, my country has a large population base and a shortage of pediatricians; ultrasound diagnosis of children's cardiovascular diseases is difficult, expensive, and ineffective i...

AI Technical Summary

Problems solved by technology

However, no detection of atrial septal defect has been done so far. One of the reasons is that the image background of cardiac ultrasound detection is complex, and it is difficult to collect data and train a better result.

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