3D Image Reference Cross Section Estimation
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Solution Overview
Problem
Conventional methods for estimating a reference cross section in medical images lack sufficient accuracy, placing a heavy burden on physicians and requiring manual identification of anatomical landmarks in three-dimensional space.
Innovation Solution
An image processing apparatus and method that includes a processor to acquire a three-dimensional image, estimate an initial parameter, and correct it using cross-sectional images to improve the estimation accuracy of the reference cross section, utilizing a convolutional neural network for parameter estimation and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setting of reference cross section is performed by finding anatomical landmarks in three-dimensional space, then the reference cross section can be accurately identified, but a large burden is placed on the physician requiring time-consuming manual operations
Solution Approach 1:
The system performs automatic estimation of the reference cross section by processing the three-dimensional image data independently without requiring physician intervention for landmark identification. The processor automatically determines the reference cross section based on the input three-dimensional image, enabling the system to serve itself rather than requiring manual guidance from the physician.
Solution Approach 2:
The manual mechanical process of visually searching for and identifying anatomical landmarks in three-dimensional space is replaced by an automated computational system. The processor uses image processing algorithms to automatically locate and determine the reference cross section, substituting the physician's manual visual inspection with automated digital processing.
2Ease of operation
If automatic estimation of reference cross section is performed using conventional methods, then manual operation burden is reduced, but estimation accuracy is insufficient
Solution Approach 1:
The automatic estimation process is divided into multiple sequential steps: first acquiring the three-dimensional image, then processing it to estimate the reference cross section, and finally correcting the estimation based on cross-sectional images. This segmentation of the estimation process into distinct stages allows each step to be optimized for accuracy while maintaining automation.
Solution Approach 2:
The system uses cross-sectional images as feedback to correct and refine the initial automatic estimation of the reference cross section. By comparing the estimated reference cross section with actual cross-sectional images and using this feedback information, the system iteratively improves the estimation accuracy while maintaining the automated operation framework.
3Measurement precision
If high-resolution three-dimensional image processing is performed to improve estimation accuracy, then reference cross section can be identified more precisely, but computational burden and processing time increase
Solution Approach 1:
The image processing is segmented into distinct functional stages: acquisition of three-dimensional image data, initial estimation of reference cross section parameters, generation of cross-sectional images, and correction based on feedback. This segmentation allows each stage to be optimized independently, using appropriate computational intensity for each specific task rather than applying high computational burden uniformly across the entire process.
Solution Approach 2:
The system performs preliminary processing of the three-dimensional image to obtain an initial estimation of the reference cross section before generating cross-sectional images for correction. This preliminary action prepares the data in advance and establishes a baseline estimation that can be efficiently refined in subsequent steps, avoiding the need for exhaustive high-resolution processing from the beginning.
Data Source
AI summary
An image processing apparatus includes at least one memory and at least one processor which function as an image acquiring unit configured to reduce a resolution of the three-dimensional image and acquire a three-dimensional image of an object, an initial parameter estimating unit configured to estimate an initial parameter in accordance with an initial value of an adjustable parameter to be utilized in obtaining a reference cross section, a cross sectional image acquiring unit configured to acquire one or more cross sectional images based on the three-dimensional image and the initial parameter, and an indicator estimating unit configured to estimate an indicator to be contributed to correcting the initial parameter, wherein the estimated indicator is in association with a specified cross sectional image out of the one or more cross sectional images.