Gastric cancer pathology image canceration region segmentation method based on deep learning

A technology for region segmentation and pathological images, applied in the field of computer vision, can solve problems such as labeling data training network can not converge, achieve the effect of improving generalization ability and prediction accuracy

Inactive Publication Date: 2019-10-11
BEIJING UNIV OF TECH
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Problems solved by technology

A new loss calculation method is proposed, which to a certain extent solves the problem that the network cannot be converged when using part of the labeled data, and combines an iterative training strategy to achieve more accurate segmentation of all cancerous regions

Method used

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  • Gastric cancer pathology image canceration region segmentation method based on deep learning
  • Gastric cancer pathology image canceration region segmentation method based on deep learning
  • Gastric cancer pathology image canceration region segmentation method based on deep learning

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Embodiment Construction

[0032] The present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0033] The used hardware equipment of the present invention has 1 PC machine, 1 titan xp graphics card;

[0034] like figure 1 As shown, the present invention provides a gastric cancer region segmentation method based on deep learning of partly labeled gastric cancer canceration images, which specifically includes the following steps:

[0035] Step 1, obtain the gastric cancer image dataset, and perform the first cleaning (for example, delete dirty data) on these data.

[0036] Step 2, use image enhancement technology to enhance the original data, so as to increase the number of samples and enrich the content of the data set.

[0037] Step 2.1, shape enhancement, mark the original image and its mask, scale its length and width according to a certain ratio, and then intercept the size required by the semantic segmen...

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Abstract

The invention discloses a gastric cancer pathological image canceration region segmentation method based on deep learning, which realizes complete segmentation of a gastric cancer canceration region and can greatly reduce the labeling time of professional doctors. Firstly, a data set is effectively expanded by utilizing a data enhancement technology; then adopting the semantic segmentation networkfor semantic segmentation of encoding-decoding, and adopting a new loss calculation mode, thus the problem that the network cannot converge when partial annotation data is used for training the network is solved, and accurate segmentation of all canceration areas is achieved in combination with an iterative training strategy.

Description

technical field [0001] The invention belongs to the technical field of computer vision, and in particular relates to a deep learning-based method for segmenting cancerous regions of gastric cancer pathological images. Background technique [0002] With the development of artificial intelligence, the application of computer vision has also been vigorously developed. In computer vision applications, image segmentation is an important branch, and image semantic segmentation is of great significance in geological image research, medical image analysis, automatic assisted driving systems and other fields. For example, the lack of medical resources in our country has always been a serious problem. According to the survey, there are only about 10,000 registered pathologists in my country, and the number of cases received each year is far greater than the number of pathologists. Therefore, research on computer-aided diagnosis and treatment systems, is very necessary. In recent year...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G16H30/40
CPCG06T7/0012G06T2207/20081G06T2207/20084G06T2207/30092G06T2207/30096G06T7/11G16H30/40
Inventor 朱青陈文
Owner BEIJING UNIV OF TECH
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