Medical Imaging Keypoint Detection via Fused Color-Depth Images
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Solution Overview
Problem
Existing keypoint detection methods for medical imaging require separate processing of color and depth images, leading to complex operations, high resource consumption, and low detection efficiency with inaccurate results.
Innovation Solution
Synchronize color and depth images in a temporal dimension, perform image fusion, and then conduct keypoint detection on the fused image to generate keypoint distribution information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate deep learning models are trained for color images and depth images, then keypoint detection can be performed on each image type, but the detection operation becomes complex and requires more computing resources
Solution Approach 1:
The patent merges the processing of color images and depth images by performing keypoint detection simultaneously on both images using a single deep learning model. The model takes both images as input and detects keypoints in one operation, eliminating the need for separate models and reducing operational complexity while maintaining detection accuracy.
2Reliability
If separate deep learning models are trained for color images and depth images, then comprehensive keypoint detection can be achieved, but the detection time increases and efficiency decreases
Solution Approach 1:
The patent combines the detection process for color and depth images into a single simultaneous operation using one deep learning model. This merging of operations reduces the total detection time and improves efficiency while still achieving comprehensive and accurate keypoint detection across both image types.
3Measurement precision
If separate deep learning models are trained for color images and depth images, then detailed keypoint analysis can be performed, but more computing resources are occupied
Solution Approach 1:
The patent consolidates the computing resources by using a single deep learning model to process both color and depth images simultaneously. This approach reduces the total computational overhead compared to running two separate models, while still achieving precise keypoint detection through the integrated processing of both image types.
Data Source
AI summary
A keypoint detection method for medical imaging, a medical imaging method and a medical imaging system is presented. The keypoint detection method for medical imaging includes: acquiring a color image and a depth image of a subject, the color image being synchronized with the depth image in a temporal dimension; performing image fusion on the color image and the depth image, to generate a fused image; and performing keypoint detection on the fused image, to generate keypoint distribution information of the subject.