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Oral and palate soft and hard tissue segmentation method based on attention mechanism and integrated registration

An attention and oral technology, applied in the intersection of stomatology and computer science, can solve the problems of large individual differences in CBCT images and inaccurate tissue segmentation, so as to reduce the time of implantation sites, solve the problem of individual differences, and prevent network The effect of overfitting

Active Publication Date: 2022-06-03
SICHUAN UNIV
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  • Abstract
  • Description
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Problems solved by technology

[0010] The present invention fills in the gaps in the automatic segmentation of hard and soft tissues in the mouth and palate, improves the distinguishability and robustness of captured tissue features, and solves the problems of large individual differences in CBCT images and inaccurate tissue segmentation in existing cases. The technical solution is: a method for segmenting soft and hard tissues of the oral cavity and palate based on the residual perceptual attention mechanism and integrated registration, including the following steps:

Method used

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  • Oral and palate soft and hard tissue segmentation method based on attention mechanism and integrated registration
  • Oral and palate soft and hard tissue segmentation method based on attention mechanism and integrated registration
  • Oral and palate soft and hard tissue segmentation method based on attention mechanism and integrated registration

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Embodiment 1

[0052] The present embodiment provides an automatic segmentation method for the soft and hard tissues of the oral cavity and palate. The overall flow diagram of the method is shown in figure 2 , the main steps include: data acquisition, model building, model training and model network testing.

[0053] 1. Data acquisition, including the following steps:

[0054] (a) Collection of datasets, including CBCT images and physicians' delineation of the soft and hard tissues of the oral palate;

[0055] (b) Preprocessing a target image dataset, wherein the target image dataset includes a CBCT image dataset and a delineation label dataset, and the preprocessing includes:

[0056] Normalized. Image normalization is a pixel-level operation, and different CBCT scanning devices may have different configurations. In order to eliminate the difference, the normalization operation is performed on these images, and the formula is as follows:

[0057] (13)

[0058] where x i Represents ...

Embodiment 2

[0104] First, the target image data set is preprocessed, wherein the target image data set includes the CBCT image data set and the delineation label data set, and the preprocessing includes: normalization, grayscale and ROI extraction.

[0105] Secondly, a random augmentation method is added to the training of the segmentation network model, and the types of all possible transformations, the number of augmented transformations applied in turn, and the magnitudes of all transformations are taken into account in the algorithm. The augmented transformation methods include X-axis translation and Y-axis translation. , X-axis clipping, Y-axis clipping, and image rotation, and augmented transform strengths include constant amplitude, random amplitude, linearly increasing amplitude, and random amplitude with an increasing upper limit. The before-and-after comparison of the augmented data is as follows: the structural similarity of the segmented palatal soft and hard tissues before ran...

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Abstract

A soft and hard tissue segmentation method based on attention mechanism and integrated registration, including: firstly, acquiring CBCT images, after data annotation, dividing the images into training set, verification set and test set; secondly, training The set is input to the established soft and hard tissue segmentation network model of the oral cavity and palate; in addition, a random augmentation method is added to the network training to enhance the input data with random scales and different types of transformations; predict the tissue segmentation results, and test the results on the test set The prediction effect of the model was quantitatively evaluated; finally, the integrated registration was performed according to the segmentation results of soft and hard tissues in multiple palates. The invention fills the gap in the segmentation and registration of soft and hard tissues in the oral cavity and palate, solves the problem of inaccurate tissue segmentation, reduces the time to find implantation sites in different cases, and provides technical support for case analysis and design of orthodontic implant nails .

Description

technical field [0001] The invention relates to the cross field of stomatology and computer science, in particular to a soft and hard tissue segmentation method of the oral cavity and palate based on an attention mechanism and integrated registration. Background technique [0002] This study is mainly aimed at the segmentation of soft and hard tissues of the palate in the field of stomatology. (1) Assist in diagnosing some oral diseases, including cleft lip and palate, oral cysts and tumors; (2) Assist in guiding the precise extraction of impacted canines; (3) Due to individual differences, use multi-oral and palate soft and hard tissue image integration Registration solves the problem of choosing the best implantation site for oral micro-implant anchorage. [0003] In oral medical physiology and histology, the oral palate is composed of the hard palate and the soft palate. The former contains the palatine bone and the soft tissue covering the surface of the palatine bone, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06V10/25G06V10/26G06V10/762G06V10/82G06K9/62G06N3/04
CPCG06T7/0012G06T2207/10081G06T2207/30008G06N3/045G06F18/23
Inventor 袁学东邹可邹联军陶天金龙虎赖文莉李沿宏江茜
Owner SICHUAN UNIV
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