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Panoramic film impacted tooth recognition method and device based on deep learning

A technology of deep learning and recognition methods, applied in image data processing, instruments, calculations, etc., can solve problems such as inefficient diagnosis, reducing the accuracy and credibility of judgment results, and insufficient number of senior medical experts, so as to achieve good repeatability Effect

Active Publication Date: 2019-07-05
北京羽医甘蓝信息技术有限公司
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  • Abstract
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Problems solved by technology

The current main diagnostic method is the manual diagnosis method based on panoramic films, its disadvantages are low efficiency and certain diagnostic inconsistencies
What's more, my country's medical resources are relatively scarce, and the number of senior medical experts is insufficient. If relying entirely on manual work, the operator's eyes will be tired, and the accuracy and credibility of the judgment results will be further reduced.

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  • Panoramic film impacted tooth recognition method and device based on deep learning
  • Panoramic film impacted tooth recognition method and device based on deep learning
  • Panoramic film impacted tooth recognition method and device based on deep learning

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Embodiment Construction

[0019] figure 1 It is a schematic flow chart of the deep learning-based method for identifying impacted teeth in panoramic images according to an embodiment of the present invention, including the following steps A to C.

[0020] Step A: Input the original panorama into the alveolar bone line segmentation model based on deep learning to obtain the alveolar bone line segmentation result.

[0021] Optionally, the alveolar bone line segmentation model based on deep learning in step A is obtained in the following manner: (1) Model design stage: the network structure includes an encoder part and a decoder part, and the encoder part adopts the Xception network structure , the decoder part adopts the FPN multi-scale fusion structure, and extracts five convolutional layers with different depths from the encoder part as input; (2) Model training stage: normalize the size of the alveolar bone line segmentation training data, gray Degree normalization, data enhancement, and data balance...

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Abstract

The invention provides a panoramic film impacted tooth recognition method and device based on deep learning. The method comprises the following steps: inputting an original panoramic film into an alveolar bone line segmentation model based on deep learning to obtain an alveolar bone line segmentation result; generating an attached rectangular frame according to the alveolar bone segmentation result, generating an extended rectangular frame according to the attached rectangular frame, and cutting out periodontal region image blocks from the original panoramic sheet according to the extended rectangular frame; and inputting the periodontal region image block into the deep learning-based obstructive tooth segmentation model to obtain an impacted tooth segmentation result.

Description

technical field [0001] The invention relates to the technical field of computer image processing, in particular to a method and device for identifying impacted teeth on panoramic films based on deep learning. Background technique [0002] Oral panoramic film is the main basis for oral diagnosis, which can clearly and completely display the whole picture of maxilla, mandible, upper and lower jaw dentition, alveolar bone, maxillary sinus cavity, sinus wall, sinus floor and temporomandibular Joint conditions, and provide accurate and effective help for the diagnosis of diseases around the jaw. [0003] As one of the common oral problems, impacted teeth are defined as: the teeth cannot erupt into the normal occlusal position due to improper position in the jawbone, such as figure 1 shown. The current main diagnostic method is the manual diagnosis method based on panoramic films, which has the disadvantages of low efficiency and certain diagnostic inconsistencies. What's more,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T7/155
CPCG06T7/0012G06T2207/20081G06T2207/20084G06T2207/30036G06T7/11G06T7/155
Inventor 徐子能白海龙丁鹏汪子晨
Owner 北京羽医甘蓝信息技术有限公司
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