Medical image segmentation method, device and equipment and storage medium

Abstract

The embodiment of the invention discloses a medical image segmentation method and device, equipment and a storage medium. The method comprises the following steps: acquiring a tissue image of a detected object, and extracting a preliminary segmentation image of a cell nucleus from the tissue image based on a preset image segmentation algorithm; and inputting the tissue image and the preliminary segmentation image into a trained cell nucleus segmentation model, and obtaining a target segmentation image of the cell nucleus according to an output result of the cell nucleus segmentation model. According to the embodiment of the invention, a traditional image segmentation algorithm and a deep learning algorithm are combined; the good segmentation performance of the deep learning algorithm can be brought into full play, the requirement of a deep learning algorithm for manually annotated perfect data can be greatly relieved, so that good segmentation performance can still be achieved under the condition that only a small amount of manually annotated perfect data exists, and the effect of accurately segmenting the cell nucleus image from the medical image with low labor cost and time costis achieved.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of medical image processing, and in particular, to a medical image segmentation method, device, equipment, and storage medium. Background technique [0002] As an important part of pathology, histopathological images are an important reference factor for medical staff to diagnose and prognose various diseases. In the analysis of histopathological images, the different shapes of cell nuclei are an important basis for the occurrence of many diseases. Therefore, how to accurately extract the segmented images of cell nuclei from histopathological images is an important topic for computer-aided diagnosis and automatic medical image analysis. . [0003] Medical image segmentation is the process of segmenting medical images into several regions with similar properties, and it is a pixel-by-pixel classification task. For the cell nucleus segmentation task in medical image segmentation, existi...

AI Technical Summary

Problems solved by technology

However, the traditional image segmentation algorithm has the disadvantages of low segmentation accuracy, easy to cause over-segmentation and under-segmentation, and poor generalization ability to pathological images of different tissues; while the deep learning algorithm requires a large amount of artificially labeled perfect data for model training , especially for the cell nucleus segmentation task, which requires fine annotation at the pixel level, which makes the labor cost and time cost of obtaining the perfect data manually annotated higher

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