Endoscopic Lesion Extraction by Symptom-Specific Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current endoscopic examination systems lack an efficient method for identifying and extracting lesion candidates from endoscopic images based on specific abnormal symptoms, leading to variability in diagnosis accuracy and sensitivity.
Innovation Solution
A control apparatus and method that utilize a processor to identify abnormal symptoms in a diagnosis target organ by analyzing physical information, selecting appropriate processing for each symptom, and performing specialized lesion extraction processing on endoscopic images, thereby enhancing the accuracy of lesion candidate region extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different specialized processing is performed for each abnormal symptom, then the accuracy of lesion extraction is improved, but the device complexity increases
Solution Approach 1:
The system segments the lesion extraction task by creating multiple specialized processing paths, each dedicated to a specific abnormal symptom type. The processor identifies the symptom type first, then routes the endoscopic image to the corresponding specialized processing module. This segmentation allows each module to be optimized for its specific symptom, improving extraction accuracy while keeping the overall system manageable through modular design.
Solution Approach 2:
Different processing algorithms and parameters are applied locally according to the specific abnormal symptom detected. Each symptom type receives tailored processing that is optimized for its characteristics, rather than using a single generic processing approach. This local quality optimization ensures that the extraction accuracy is maximized for each specific lesion type while maintaining system efficiency.
2Reliability
If specialized processing for each abnormal symptom is implemented, then the sensitivity and specificity of diagnosis are improved, but the processing time increases
Solution Approach 1:
The system performs preliminary identification of the abnormal symptom type before executing the specialized lesion extraction processing. By first analyzing the endoscopic image to determine what type of abnormality is present, the system can then select and execute only the relevant specialized processing path. This preliminary action prevents unnecessary processing and reduces overall processing time while maintaining high diagnostic reliability.
Solution Approach 2:
The processing system is designed to be dynamic, automatically adapting the processing approach based on the detected symptom type. The processor dynamically selects from multiple specialized processing algorithms, executing only the one that matches the identified abnormality. This dynamic adaptation ensures optimal processing efficiency and diagnostic accuracy without requiring all processing paths to run simultaneously, thereby reducing processing time.
Data Source
AI summary
A diagnosis support apparatus includes a processor including at least one piece of hardware. The processor identifies, based on physical information including one or more kinds of information capable of estimating a state of a diagnosis target organ of a subject, one abnormal symptom appearing in the diagnosis target organ, performs, as lesion extraction processing for extracting a lesion candidate region from an endoscopic image, different processing specialized for each abnormal symptom that appears in the diagnosis target organ, and performs the lesion extraction processing corresponding to the identified one abnormal symptom.