Cephalometric Feature Point Recognition via Local Region Regression
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Solution Overview
Problem
The identification of anatomical feature points in cephalometric images relies heavily on expert judgment and has not been fully automated due to the complexity and requirement of sophisticated expertise, and existing automated methods are not clinically applicable due to high computational demands.
Innovation Solution
An automatic measurement point recognition method using a deep learning model that detects and estimates candidate positions of feature points in predetermined peripheral regions, determining the most likely position based on distribution density, without the need for special hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automatic recognition methods using knowledge information or pattern recognition are used, then automation is improved, but computational load increases to supercomputer-level requirements
Solution Approach 1:
The patent divides the cephalometric image into multiple peripheral partial regions around the feature point of interest, each processed independently by the deep learning model. This segmentation reduces the computational load compared to processing the entire image at once, while still achieving accurate automated recognition of measurement points.
Solution Approach 2:
The patent focuses computational resources on local peripheral regions around the feature point rather than the entire image. By training the deep learning model to recognize patterns in these localized areas, the system achieves high automation with reduced computational requirements compared to global image analysis methods.
2Measurement precision
If deep learning models are trained on entire cephalometric images, then recognition accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts and processes only the relevant peripheral partial regions containing the feature point of interest, rather than analyzing the entire cephalometric image. This extraction approach maintains measurement precision by focusing on critical areas while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The patent applies partial action by processing only the necessary peripheral regions around the feature point rather than the complete image. This selective processing achieves sufficient recognition accuracy for clinical applications while avoiding the excessive computational burden of full-image analysis.
Data Source
AI summary
A technique for automating the identifying of a measurement point in cephalometric image analysis is provided. An automatic measurement point recognition method includes a step of detecting, from a cephalometric image 14 acquired from a subject, a plurality of peripheral partial regions 31, 32, 33, 34 for recognizing a target feature point, a step of estimating a candidate position of the feature point in each of the peripheral partial regions 31, 32, 33, 34 by the application of a regression CNN model 10, and a step of determining the position of the feature point in the cephalometric image 14 based on the distribution of the candidate positions estimated. In the step of detecting, for example, the peripheral partial region 32, a classification CNN model 13 trained with a control image 52 is applied.