Oral cavity CBCT image mandibular neural tube automatic recognition method based on Mask RCNN

An automatic recognition and neural tube technology, applied in the field of medical imaging, can solve the problems of low cost and low recognition stability, and achieve the effect of good recognition speed, accuracy and stability.

Pending Publication Date: 2019-12-24
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the existing problems caused by manual identification of the mandibular canal, the present invention proposes a method for automatically identifying the mandibular canal based on Mask RCNN with low cost and high recognition stability

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  • Oral cavity CBCT image mandibular neural tube automatic recognition method based on Mask RCNN
  • Oral cavity CBCT image mandibular neural tube automatic recognition method based on Mask RCNN

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Embodiment Construction

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] refer to figure 1 and figure 2 , a method for identifying a dental nerve canal based on a Mask RCNN neural network, comprising the following steps:

[0021] Step 1: Collect oral CBCT image data, and preprocess the coronal image: denoise, remove the image that does not show the mandibular nerve foramen in the coronal image of the CBCT sequence image; compress the format of the denoised image to reduce training complexity ; Use the aiming tool to manually aim along the outer contour of the mandibular nerve foramen, such as figure 2 The coordinates of the point shown in the mandibular nerve foramen aiming point are used as label data and stored in the form of a dictionary;

[0022] Step 2: Establish a neural network model: use the feature pyramid network to obtain multi-scale feature maps as the input of the region generation network, generate aiming frames, p...

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Abstract

An oral cavity CBCT image mandibular neural tube automatic recognition method based on Mask RCNN comprises the following steps: collecting oral cavity CBCT image data, preprocessing a coronal diagramin the oral cavity CBCT image data, removing an image which does not display a mandibular neural hole in the coronal diagram, performing format compression on the image, and manually aiming at the mandibular neural hole; generating a sighting frame, positioning the rectangular frame, obtaining a binary mask mask of the target instance, and establishing a neural network model; training a model; andrecognizing and displaying the mandibular nerve hole through the trained model. According to the method, the Mask RCNN is used for automatically identifying the mandibular neural tube in the CBCT image. Machine recognition is used for replacing manual recognition of coronal diagram tooth nerve holes, so that the labor cost is saved, the stability of the generated mandibular nerve tube track is improved, and the method has good recognition speed and precision.

Description

technical field [0001] The present invention relates to the field of medical imaging and the field of image recognition technology, in particular to a method for automatic recognition of mandibular canal in oral cavity CBCT images based on Mask RCNN. Background technique [0002] In recent years, my country's demand for dental implants has grown year by year. From the number of implants in the hundreds of thousands of teeth in 2011 to the number of implants in the order of millions today, China has become one of the fastest growing dental implant markets. According to conservative estimates, the potential market demand for dental implants can reach 400 billion yuan. In the actual clinical operation of the dentist, a key precaution is not to compress the dental nerve, especially the mandibular canal. Therefore, before dental implantation, oral CT examination must be performed to determine the position of the dental nerve tube to ensure successful implantation. [0003] CBCT...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06V2201/03G06F18/214G06F18/241
Inventor 杨旭华马钢峰徐新黎
Owner ZHEJIANG UNIV OF TECH
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