Medical Image Segmentation via Shared Low-Order Layer Weights
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Solution Overview
Problem
Current medical image segmentation techniques based on local features are prone to errors, particularly in distinguishing between normal lung regions and Ground Glass Opacity (GGO) patterns, due to noise and high CT values in IPF patients, leading to inaccurate classification.
Innovation Solution
An image processing apparatus that employs deep learning for both local and global feature classification, sharing weights between low-order layers of the segmentation and global feature classification units to reflect global features in the segmentation process, thereby reducing classification errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If segmentation is performed based on local region information only, then segmentation speed is maintained, but segmentation accuracy deteriorates due to noise and high CT values in IPF patients
Solution Approach 1:
The system divides the segmentation task into two independent modules: a segmentation unit that processes local region information for pixel classification, and a global feature classification unit that processes overall image features. These modules operate separately but share low-order layer weights, allowing the system to maintain segmentation speed while improving accuracy through global feature integration.
Solution Approach 2:
The low-order layers of both the segmentation unit and global feature classification unit share the same weight parameters. This weight sharing mechanism allows a single set of learned features to serve dual purposes: local segmentation and global classification, thereby improving segmentation accuracy without proportionally increasing system complexity.
2Reliability
If segmentation is performed based on local region information only, then processing speed is maintained, but reliability of classification deteriorates due to noise in IPF patient images
Solution Approach 1:
The system separates classification tasks into local segmentation (pixel-level) and global classification (image-level), with each handling different aspects of the problem. The global feature classification unit provides reliable contextual information that improves classification reliability without significantly increasing processing time.
Solution Approach 2:
The global feature classification unit performs preliminary classification based on overall image features before the detailed segmentation process. This preliminary action provides contextual guidance that improves the reliability of subsequent local segmentation, reducing the need for multiple iterative refinements and thus minimizing time loss.
3Measurement precision
If only CT value information is used for segmentation, then processing simplicity is maintained, but measurement precision deteriorates due to inability to distinguish normal lung from GGO regions
Solution Approach 1:
The system segments the feature extraction process into local feature extraction (including CT values and texture features) and global feature extraction. By processing features at different scales and combining them through weight sharing, the system achieves accurate differentiation between normal lung and GGO regions without excessive complexity.
Solution Approach 2:
The system combines multiple types of features (CT values, texture features, and global structural features) into a composite feature representation. This composite approach enables accurate differentiation between normal lung and GGO regions by leveraging complementary information from different feature sources.
Data Source
AI summary
Provided are an image processing apparatus, an image processing method, and a program that can suppress an error in the segmentation of a medical image. An image processing apparatus includes: a segmentation unit (42) that applies deep learning to perform segmentation which classifies a medical image (200) into a specific class on the basis of a local feature of the medical image; and a global feature classification unit (46) that applies deep learning to classify the medical image into a global feature which is an overall feature of the medical image. The segmentation unit shares a weight of a first low-order layer which is a low-order layer with a second low-order layer which is a low-order layer in the global feature classification unit.