Lung-Liver Boundary Detection Using Machine Learning Pixel Specification
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Solution Overview
Problem
Current methods for detecting the boundary between the lung and liver using navigator sequences in magnetic resonance imaging are operator-dependent and lack precision, making it difficult to accurately set navigator regions for respiratory signal acquisition.
Innovation Solution
A detection device and method that utilizes pixel extraction and specification using machine learning algorithms to identify candidate pixels on the boundary between the lung and liver from image data, improving detection precision by narrowing down pixels and specifying the correct boundary pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If an operator manually finds the boundary between lung and liver by looking at image data, then the navigator region can be set, but the work becomes complicated and time-consuming for the operator
Solution Approach 1:
The system performs automatic boundary detection between lung and liver using image data processing algorithms, eliminating the need for operator intervention. The detection device automatically identifies the boundary by analyzing intensity differences and spatial relationships in the image data, making the system self-sufficient for this task.
Solution Approach 2:
The manual visual inspection process by the operator is replaced with an automated computational algorithm that processes image data. The system uses pixel intensity analysis and boundary detection algorithms to automatically identify the lung-liver boundary, substituting human visual-mechanical inspection with computational processing.
2Ease of operation
If automatic boundary detection is implemented, then operator workload is reduced, but detection precision of the boundary is difficult to improve
Solution Approach 1:
The detection algorithm focuses on local intensity variations and spatial relationships specifically at the boundary region between lung and liver. By analyzing local pixel intensity gradients and neighborhood patterns, the system achieves high precision in identifying the boundary location without being affected by variations in other regions of the image.
Solution Approach 2:
The system adjusts detection parameters such as intensity thresholds and spatial window sizes to optimize boundary detection precision. By dynamically changing these parameters based on the specific image characteristics and anatomical variations, the system maintains high detection precision across different subjects and imaging conditions.
3Reliability
If the navigator region is set manually by operator, then the region can be positioned, but the process becomes complicated work
Solution Approach 1:
The system automatically positions the navigator region by first detecting the lung-liver boundary, then using this boundary information to define the navigator region. This preliminary boundary detection action simplifies the subsequent navigator region positioning, eliminating the need for separate manual positioning steps.
Solution Approach 2:
The detection device serves multiple functions: it detects the boundary between lung and liver, determines the position of the boundary, and uses this information to set the navigator region. This multi-functionality consolidates multiple manual operations into a single automated process, reducing overall system complexity.
Data Source
AI summary
A detection apparatus for detecting the position of a boundary between a first part and a second part of a subject, includes a pixel extraction unit for extracting a plurality of candidate pixels acting as candidates for a pixel situated on the boundary on the basis of image data of a first section crossing the first part and the second part, and a pixel specification unit for specifying the pixel situated on the boundary from within the plurality of candidate pixels by using an identifier which has been prepared by using an algorithm of machine learning.