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

A technology of deep learning and artificial teeth, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as eye fatigue of operators, reduce the accuracy and credibility of judgment results, and low efficiency, and achieve repeatability Good results

Active Publication Date: 2019-05-17
北京羽医甘蓝信息技术有限公司
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  • Summary
  • Abstract
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AI Technical Summary

Problems solved by technology

The current method is mainly based on manual recognition of panoramic films, and its disadvantages lie in 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 artificial tooth recognition method and device based on deep learning
  • Panoramic film artificial tooth recognition method and device based on deep learning
  • Panoramic film artificial 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 panoramic artificial tooth recognition method of the 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 processing. The para...

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Abstract

The invention provides a panorama artificial tooth recognition method and device based on deep learning. The method comprises the following steps: inputting an original panoramic film into an alveolarbone 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; Inputting the periodontal region image blocks into an artificial tooth body segmentation model based on deep learning to obtain an artificial tooth body segmentation result which comprisesan artificial restoration tooth body segmentation result and an artificial implant tooth body segmentation result.

Description

technical field [0001] The present invention relates to the technical field of computer image processing, in particular to a method and device for artificial tooth recognition 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] Since the patient may have received dental treatment, the panoramic film may have implants and restorations in addition to natural teeth, which are collectively referred to as artificial teeth, such as figure 1 shown. The current method is mainly based on manual recognition of panoramic films, and its disadvantages are low efficiency and certain diagnostic inconsistencies. What's more,...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
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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