Lesion monitoring method and device, computer device and storage medium

Abstract

The invention discloses a lesion monitoring method and device, a computer device and a storage medium. The lesion monitoring method comprises the following steps: inputting the sample data of an original CT image into a preset segmentation model for segmentation calculation, and outputting segmented liver image data; inputting the sample data of the original CT image and the segmented liver imagedata into a preset identification model for calculation, and outputting an identification result, wherein the segmentation model and the recognition model are respectively trained through a first convolution neural network and a second convolution neural network, and the first convolution neural network and the second convolution neural network are in cascade arrangement. According to the method,the neural network is used for learning liver features and lesion features in the original CT image, and the relation between the CT slices and the tags is established through the two cascaded full-convolution neural networks to distribute the task training model, so that the optimal network parameters can be found as soon as possible, and the disease treatment efficiency and the accuracy of disease analysis are improved.

Description

technical field [0001] The present application relates to the application field of convolutional neural network, in particular to a lesion monitoring method, device, computer equipment and storage medium. Background technique [0002] The goal of liver cancer diagnosis is to judge whether the liver is lesioned or not in the cross-sectional image of the human body obtained by CT tomographic images. The traditional method is to use the doctor's experience to read multiple CT images to locate the lesion, so the doctor's experience is very important. However, because the CT tomographic image is a grayscale image and displays multiple organs at the same time, the CT slices related to the liver are equivalent. There are many, and the amount of data is very large, which will greatly consume the brainpower and time of doctors, resulting in doctors having no more time to receive more patients, analyze the condition, and design treatment plans. Contents of the invention [0003] Th...

AI Technical Summary

Problems solved by technology

[0003] The main purpose of this application is to provide a lesion monitoring method, which aims to solve the technical problems of long time-consuming and low efficiency in the diagnosis of liver cancer mainly relying on doctors' medical experience

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