Medical image marking method and system thereof

Abstract

The invention discloses a medical image marking method and a system thereof, wherein the method and the system belong to the field of medical image processing. The method comprises the steps of performing analysis and image preprocessing on the medical image which is uploaded by a user, and obtaining a processed medical image; according to a preset operation distribution strategy, distributing themarking operation of the processed medical image to an assigned user supplying a marking process which corresponds with the processed medical image to the assigned user, wherein the marking process is used for marking the processed medical image by the assigned user; acquiring the marking result of the processed medical image by the assigned user, and performing storage. The medical image markingmethod and the system thereof can perform optimized marking on the medical image, and particularly the multi-mode multi-sequence MR image, and furthermore realize flexible and quick professional marking on the medical image.

Description

technical field [0001] The invention relates to the field of medical image processing, in particular to a medical image labeling method and system. Background technique [0002] With the maturity of artificial intelligence technology, the scope of application is more extensive. In the field of medical AI, compared with the rapid development of other fields, the pace is relatively slow. The main reasons are small data size, poor annotation quality, and the need for cross-field and interdisciplinary professionals for annotation. First of all, the hospital system itself has a large amount of medical data, but there is no efficient, scientific and safe method for technicians in the AI ​​industry to directly extract and use it. Secondly, compared with general daily data, the labeling quality of medical data with industry barriers is generally lower, and even if technicians obtain the data, due to the professionalism of the medical industry itself, interventional medical clinica...

AI Technical Summary

Problems solved by technology

[0003] Now there are a variety of tools and platforms that can be used for labeling in the market, such as Labelme, LabelImg, VGGImage Annotator, etc. These labeling tools are mainly based on the general labeling tools developed for the target detection, recognition, and segmentation functions in artificial intelligence computer vision. Most of the daily annotations (objects, characters, symbols, text) can have better results, but for medical images, although some annotations can be completed, they still face the problem of unprofessional, inflexible, and inefficient annotations. question

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