Medical image labeling method and device for deep learning

Abstract

The invention discloses a medical image labeling method and device for deep learning, and the method comprises the following steps: carrying out the preprocessing of an inputted image diagnosis report, electronic medical record information and a medical image, and generating data comprising the medical image and the extracted related diagnosis information, wherein the medical image is pre-annotated based on deep learning, the image is segmented based on an image semantic segmentation technology to obtain a boundary range of each lesion area, pixel-level segmentation annotation is performed onthe image, and classification annotation is performed on a disease type to which the image belongs based on an image classification technology in combination with image related diagnosis information;and displaying the pre-annotated image and the related diagnosis information through an interface to receive an interactive instruction for a doctor to finely adjust a pre-annotated image result, andexporting an annotation result. According to the method, the labeling efficiency is improved, and the labeling precision is ensured.

Description

technical field [0001] The invention relates to the technical field of image labeling, in particular to a deep learning-oriented medical image labeling method and device. Background technique [0002] With the rapid development of medical imaging inspection equipment and the wide application of PACS systems, various types of medical image big data have been acquired and stored, making medical image analysis based on artificial intelligence a current research hotspot. In recent years, artificial intelligence algorithms have been applied in the field of medical imaging-aided diagnosis, and have achieved many remarkable results. Supervised artificial intelligence algorithms usually require a large amount of labeled data for training. How to efficiently obtain a large amount of high-quality labeled data for deep learning algorithms is an urgent problem to be solved. [0003] Related technologies, (1) A method and device for extracting fundus image annotations. The method inclu...

AI Technical Summary

Problems solved by technology

This method is only based on image information for labeling, without using other information, and the information is not fully utilized. If applied to the field of medical imaging, this method does not conform to the actual diagnosis process

[0005] In summary, the current medical image labeling technology has the following problems: (1) Manual labeling is inefficient and error-prone (2) The accuracy of automatic labeling cannot meet the requirements (3 ) information is not fully utilized, does not conform to the actual diagnosis process, and needs to be resolved

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