Pneumonia lesion segmentation method and device

Abstract

The embodiment of the invention provides a pneumonia lesion segmentation method and device, and solves the problems of low accuracy and low efficiency of an existing pneumonia lesion segmentation mode. The pneumonia lesion segmentation method comprises the following steps: predicting a lesion area on medical image data of a positive level based on an image semantic segmentation model; counting thelesion area of each parallel layer, and calculating the lesion volume by combining the lesion area of each parallel layer, wherein the image semantic segmentation model is established through the following training steps: inputting all or part of marked sample data into a focus segmentation model to obtain a prediction result output by the focus segmentation model; based on a focus detection frame predicted by a focus detection model and a lung region predicted by a lung lobe lung segment segmentation model, screening out a low-level false positive region from a prediction result to obtain afalse label of sample data, and adding unmarked sample data; and rechecking the pseudo label, and marking the marked sample data to update the marked sample data.

Description

technical field [0001] The present application relates to the technical field of image analysis, in particular to a pneumonia lesion segmentation method, device, electronic equipment, and computer-readable storage medium. Background technique [0002] In recent years, deep machine learning has been widely used in the field of image understanding. Among them, the deep full convolutional network proposed for image semantic segmentation has obvious advantages in terms of segmentation accuracy compared with traditional algorithms, and it can better control the time consumed in reasoning. In addition, the widespread use of GPUs can further greatly improve the inference speed of fully convolutional networks. This makes it possible to apply high-precision fully convolutional networks in medical imaging scenarios. Traditional medical imaging diagnosis relies on the empirical and subjective judgment of clinicians, so there are problems such as time-consuming and poor stability, whi...

AI Technical Summary

Problems solved by technology

[0005] In view of this, the embodiment of the present application provides a pneumonia lesion segmentation method and device, which solves the problems of low accuracy and low efficiency of the existing pneumonia lesion segmentation methods

Images