End-to-end colorectal polyp image segmentation method based on effective learning

A colorectal and polyp technology, applied in image analysis, image enhancement, image data processing, etc., can solve problems such as uneven proportion of foreground and background areas, irregular edges, etc., to reduce manual intervention, improve attention, The effect of increasing attention

Pending Publication Date: 2020-10-16
BEIJING UNIV OF TECH
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Problems solved by technology

[0004] Aiming at the uneven proportion of the foreground area and the background area of ​​colorectal polyps and the irregular edges, the present invention provides an end-to-end colorectal polyp segmentation method based on effective learning

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  • End-to-end colorectal polyp image segmentation method based on effective learning
  • End-to-end colorectal polyp image segmentation method based on effective learning
  • End-to-end colorectal polyp image segmentation method based on effective learning

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Embodiment Construction

[0031] The specific implementation of the end-to-end colorectal polyp segmentation method based on effective learning in the present invention uses an end-to-end network model as a tool for colorectal polyp segmentation, wherein the feature extraction process and classification process are integrated without manual combination. The images of 104 patients with confirmed colonic polyps were used as model training data. After data processing, 545 images of 650 CT images were divided into training set (for model fitting) and test set (for model fitting) according to the ratio of 9:1. The remaining 105 images are used as a test set (used to evaluate the generalization ability of the model).

[0032] In this example:

[0033] The original colorectal CT image data containing polyps in step 1 contains a lot of irrelevant information, and the proportion of polyps is too small compared to the entire CT image. After observation and practice, polyp segmentation is a local operation, whic...

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Abstract

The invention discloses an end-to-end colorectal polyp segmentation method based on effective learning. According to the method, a network model combining a full convolutional neural network with a fully connected conditional random field recurrent neural network is proposed and designed by using a deep learning technology and is used for colorectal polyp segmentation. For the phenomenon that polyp region feature training is insufficient and unrelated organization learning is excessive in the network training process, an effective learning loss function is introduced, and the loss function refers to difficult case mining and boundary perception ideas. The loss function refers to an objective function of a difficult case mining thought, sample training errors are weighted for the problem ofnon-uniform proportion of a colorectal polyp foreground region and a background region, the attention degree of a difficult case is improved, and meanwhile, the attention degree of boundary pixels isimproved in combination with boundary factors to improve the contour segmentation precision.

Description

technical field [0001] The invention belongs to the field of medical image segmentation, in particular to an end-to-end colorectal polyp segmentation method based on effective learning. Background technique [0002] With the continuous improvement of medical imaging technology and image quality, it is possible to complete the discovery, segmentation and classification of colonic polyps on CT images. Colorectal cancer is the third largest health killer in the world, and early prevention and treatment of it is very important. As a major risk factor for colorectal cancer, colorectal polyps can reduce the possibility of cancer if they can be detected as early as possible. For polyp detection, the first step is to segment the polyp area. Only by accurately segmenting the target area can further diagnosis be made. [0003] At present, deep learning technology is outstanding in the field of medical image segmentation, but there are relatively few related studies on applying deep l...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/10
CPCG06T7/0012G06T2207/10081G06T2207/20081G06T2207/20084G06T2207/30032G06T7/10
Inventor 李建强赵金珠王瑞乾解黎阳
Owner BEIJING UNIV OF TECH
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