Mammary gland ultrasonic image quality evaluation method based on auxiliary diagnosis and subjective aesthetics

Abstract

The invention provides a mammary gland ultrasonic image quality evaluation method based on auxiliary diagnosis and subjective aesthetics. The method comprises the following steps: firstly, constructing an ultrasonic image database of a small amount of strong supervision annotation information and a large amount of weak supervision annotation information; secondly, training an automatic segmentation deep network model DNN-1 of the focus ROI by adopting various small sample learning strategies; then, inputting the ROI image and the original image together, and performing training to obtain an ultrasonic auxiliary diagnosis network DNN-2; and finally, migrating a convolutional layer of the auxiliary diagnosis network, performing training to obtain an aesthetic evaluation convolutional neuralnetwork DNN-3 in combination with the aesthetic score of the doctor, and obtaining a breast ultrasonic image quality evaluation result in combination with DNN-2 based on an action mechanism.

Description

technical field [0001] The invention belongs to the technical field of medical image processing, and in particular relates to a breast ultrasound image quality evaluation method based on auxiliary diagnosis and subjective aesthetics. Background technique [0002] During medical sonography, image quality is critical to diagnostic accuracy. At present, although there is no unified standard worldwide, for gray-scale ultrasound (B-ultrasound) and Doppler ultrasound, each country or region has relevant measurement standards, such as probe configuration, display performance, image uniformity, sensitivity ( Maximum viewing depth, signal-to-noise ratio), geometric accuracy, spatial resolution and contrast resolution, etc. For example, "Sassaroli Elisabetta, Crake Calum, Scorza Andrea, Kim Don-Soo, Park Mi-Ae. Image quality evaluation of ultrasound imaging systems: advanced B-modes. [J]. Journal of applied clinical medical physics, 2019, 20(3) .” proposed a method to measure the im...

AI Technical Summary

Problems solved by technology

The model trained in the above literature can only achieve the approximate positioning of the target area, but cannot achieve accurate segmentation. Accurate segmentation requires a large number of manually marked images, and images of different parts need to be marked by physicians with corresponding rich experience

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