Medical image automatic labeling method and system based on small sample segmentation

An automatic labeling and medical image technology, applied in neural learning methods, image enhancement, image analysis, etc., can solve problems such as the poor effect of the nearest neighbor method, and achieve high-precision automatic labeling technology and the effect of strong engineering practice

Pending Publication Date: 2021-12-24
CHONGQING UNIV OF POSTS & TELECOMM
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And when the number of training image samples is li

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  • Medical image automatic labeling method and system based on small sample segmentation
  • Medical image automatic labeling method and system based on small sample segmentation
  • Medical image automatic labeling method and system based on small sample segmentation

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[0046] The technical solutions in the embodiments of the present invention will be described in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are merely a part of the embodiments of the invention.

[0047] The technical solution to solve the above technical problem is:

[0048] The present invention is based on a small sample-based image labeling method, and the specific embodiments are as follows:

[0049] (1) Get the real image data of the medical image in the original picture according to the required segmentation scene and randomly extract 20% of the sample from each category medium to the training data; use the open source tool Labelme, labeled the target in the image to get the corresponding The label of the format gives a standard data sample. That is, an original image and a real label mask Mask; the labeling training data is divided into the training set and verify the feedback of the indicators when tra...

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Abstract

The invention requests to protect a medical image automatic labeling method and system based on small sample segmentation, and aims to solve the problems that a large amount of labeled data is needed for medical image segmentation, the process of labeling new data is tedious and single, but a large amount of manual labeling work is needed, and the cost of a data set is increased. On the basis of a small sample segmentation technology, an automatic annotation network structure Siamese-DCNet is provided, a double-branch structure is utilized, a query branch and a support branch are included, features of an unannotated image and features of an annotated image are extracted preliminarily, and by means of a result obtained by the double branches and in combination with known annotations, unimportant information except the annotation is removed; a preliminary annotation is predicted by calculating cosine similarity and is input into an iterative optimization module, and a final annotation result is obtained through refining of several iterations. According to the method, automatic annotation of all other images in the same scene can be realized only by a small number of images with annotations.

Description

technical field [0001] The invention belongs to the technical fields of deep learning, image processing, medical image segmentation, and automatic labeling, and in particular relates to an automatic labeling method for medical images based on small sample segmentation. Background technique [0002] In the field of medical images, the annotation results of medical images can assist medical workers to make reasonable judgments on patients' conditions and formulate corresponding diagnostic methods. In recent years, with the widespread application of deep learning image segmentation technology in many computer vision applications (eg, autonomous driving, medical imaging, remote sensing technology), more and more image data needs to be used to train deep learning models. However, since the objects in medical images are of different sizes, poses and shapes, and the boundaries are not obvious, the labeling of images is a very time-consuming and laborious work. In addition, in orde...

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Application Information

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IPC IPC(8): G06T7/10G06N3/04G06N3/08
CPCG06T7/10G06N3/08G06T2207/20081G06T2207/20084G06T2207/20132G06N3/045
Inventor 孙开伟刘虎王支浩冉雪李彦宣立德
Owner CHONGQING UNIV OF POSTS & TELECOMM
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