Medical Image Segmentation Correction via Global Features
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Solution Overview
Problem
The segmentation of medical images using deep learning can be inaccurate and time-consuming for doctors to correct, as existing methods require manual intervention and extensive effort to correct each pixel, leading to inefficiencies in the correction process.
Innovation Solution
An image processing apparatus that includes an image acquisition unit, a segmentation unit, a global feature acquisition unit, and a correction unit, which uses global features to automate the correction of segmentation errors by referencing relationships between overall image features and local region classes, reducing the time and effort required for correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning is used for medical image segmentation, then segmentation capability is improved, but segmentation accuracy deteriorates due to complex task nature
Solution Approach 1:
The system uses feedback from global feature acquisition to correct local segmentation errors. The correction unit receives feedback about segmentation inaccuracies and automatically adjusts the classification results by referencing the relationship between global features and local region classes, thereby improving segmentation accuracy while maintaining deep learning's adaptability.
Solution Approach 2:
The patent replaces manual mechanical correction (doctor reviewing and correcting each pixel) with an automated correction unit that uses global feature information to identify and correct segmentation errors. This substitution maintains high segmentation capability while improving accuracy through automated error detection and correction.
2Measurement precision
If manual correction is performed for each pixel, then segmentation accuracy is improved, but time and effort required increases significantly
Solution Approach 1:
The correction unit performs self-service by automatically detecting and correcting segmentation errors using global feature information. Instead of requiring external manual intervention for each pixel, the system serves itself by autonomously identifying misclassified regions and correcting them, thereby maintaining high accuracy while dramatically reducing the time and effort required.
Solution Approach 2:
The patent introduces global feature information as an intermediary to bridge the gap between automated segmentation and accurate correction. The correction unit uses this intermediary information to automatically identify and correct errors without requiring direct manual pixel-by-pixel review, thus reducing correction time while maintaining accuracy.
3Productivity
If automated correction using global features is implemented, then correction efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the correction process into distinct functional units: a global feature acquisition unit that extracts overall image characteristics, and a correction unit that uses these features to correct local segmentation errors. This segmentation of functionality improves correction efficiency by automating the process while managing complexity through modular design.
Solution Approach 2:
The correction unit serves multiple functions: it acquires global features, identifies segmentation errors, and performs corrections all within a single integrated component. This multi-functionality improves correction efficiency by consolidating operations while managing system complexity through a unified approach rather than separate dedicated components for each function.
Data Source
AI summary
Provided are an image processing apparatus, an image processing method, and a program that can reduce the time and effort required to correct the segmentation of a medical image. An image processing apparatus includes: an image acquisition unit (40) that acquires a medical image (200); a segmentation unit (42) that performs segmentation on the medical image acquired by the image acquisition unit and classifies the medical image into prescribed classes for each local region; a global feature acquisition unit (46) that acquires a global feature indicating an overall feature of the medical image; and a correction unit (44) that corrects a class of a correction target region that is a local region whose class is to be corrected in the medical image according to the global feature with reference to a relationship between the global feature and the class.