Oral cavity image multi-tissue full-automatic segmentation method and system

A fully automatic, tissue technology, applied in image analysis, image enhancement, image data processing, etc., can solve the problems of complex tissue, difficult to distinguish between alveolar bone and root area, etc., to promote segmentation, improve universality, reduce artificial effect of difference

Pending Publication Date: 2021-08-06
PEKING UNIV SCHOOL OF STOMATOLOGY
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the tissue near the tooth root is more complex, and it is difficult to distinguish the alveolar bone and the root area with a single threshold range. For details, please refer to reference 2

Method used

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  • Oral cavity image multi-tissue full-automatic segmentation method and system
  • Oral cavity image multi-tissue full-automatic segmentation method and system
  • Oral cavity image multi-tissue full-automatic segmentation method and system

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Embodiment Construction

[0090] figure 1 A computer-executed method for fully automatic segmentation of multiple tissues on an oral cavity CBCT image is shown according to an embodiment of the present invention.

[0091] like figure 1 As shown, in step S110, multiple CBCT image data of the oral cavity are acquired.

[0092] In step S120, adjust the spatial coordinate system of the CBCT data, determine the coordinate origin, and based on the definition of the coordinate origin and the spatial coordinate system, ensure that the spatial similarity of the same organization meets the predetermined standard. For example, when analyzing patients with periodontal disease, periodontal disease will become more and more serious as time progresses. In the process of comparing the data before and after, it is necessary to ensure that the spatial coordinate system of the same organization is as consistent as possible.

[0093] In an example, adjusting the spatial coordinate system of the CBCT data includes: taki...

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Abstract

The invention provides a method and a system for performing full-automatic segmentation on multiple tissues of an oral cavity CBCT image. According to the obtained original oral cavity three-dimensional CBCT data, a deep learning method is utilized to label the data, a deep learning method is utilized again to segment a new image, the image processing process is simple, excessive manual labeling and intervention are not needed, the difficulty of existing medical data annotation is overcome, and meanwhile human differences in the image annotation process are reduced. In the network training process, the adversarial network is generated for the training data, the training data volume is increased, and the universality of the network can be effectively improved. The establishment of the method greatly promotes the segmentation of a medical image technology, and provides an effective technical support for the automatic diagnosis and prognosis of oral diseases.

Description

technical field [0001] This application relates to techniques for automatic segmentation of multiple tissues in oral images. Background technique [0002] The research of artificial intelligence in the medical field is an important direction of interdisciplinary development. This type of research uses advanced technologies and methods in computer science and mathematics to solve clinical problems in medicine, and has great application prospects and promotion value. [0003] The automatic segmentation and recognition of medical images is the focus of automatic medical diagnosis. Based on CT, MRI and other images, the automatic image segmentation can not only reduce the reading time of clinicians, but also improve the accuracy of diagnosis. [0004] In stomatology, CBCT data play an important role. How to conduct accurate and efficient analysis of CBCT data has gradually attracted attention. This patent is committed to using deep learning technology to lay a technical foun...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T7/155G06T7/00G06K9/62G06N3/04G06N3/08
CPCG06T7/11G06T7/0012G06T7/155G06N3/04G06N3/08G06T2207/20081G06T2207/20084G06T2207/20152G06T2207/30036G06F18/214
Inventor 杨慧芳张馨月
Owner PEKING UNIV SCHOOL OF STOMATOLOGY
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