Endoscope Image Overlay for Multi-Spectrum Lesion Detection
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Solution Overview
Problem
Existing endoscope systems struggle to accurately detect and highlight lesion regions using conventional illumination methods, leading to potential oversight and difficulty in precise diagnosis.
Innovation Solution
An endoscope device equipped with multiple light sources emitting different spectra of illumination light, combined with AI-driven identification devices, detects lesion candidates in multiple layers and selects a region for display based on reliability scores, superimposing this information onto a white light image for enhanced visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple illumination lights with different spectra are used to detect lesion regions, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The illumination system is segmented into multiple independent light sources, each emitting a specific spectrum (e.g., blue light, green light, red light). Each light source targets different tissue layers or lesion types, allowing parallel detection without requiring a single complex illumination system. This segmentation enables the system to achieve high detection accuracy while maintaining manageable device complexity through modular design.
Solution Approach 2:
The endoscope device integrates multiple illumination functions into a single universal system that can adaptively select and combine different spectrum lights based on the examination needs. The control unit coordinates multiple light sources to perform various detection tasks (surface lesion detection, subsurface lesion detection, vascular pattern analysis) using a unified platform, thereby improving detection accuracy without proportionally increasing overall device complexity.
2Reliability
If multiple lesion candidate regions are detected from different illumination images, then detection reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary detection using multiple illumination lights simultaneously or in rapid succession, capturing multiple image sets that highlight different lesion characteristics. By preparing these multiple candidate region detections in advance before final synthesis, the system ensures high detection reliability while optimizing the timing to minimize overall processing time and avoid delays in clinical diagnosis.
Solution Approach 2:
Multiple lesion candidate regions detected from different illumination images are merged and synthesized into a unified result by the control unit. The system combines the strengths of each illumination type (e.g., surface detail from blue light, subsurface structure from red light) to produce a comprehensive lesion detection result, achieving high reliability through data fusion while reducing redundant processing steps.
3Measurement precision
If lesion regions are highlighted by superimposing detection results onto white light images, then diagnostic precision is improved, but image processing complexity increases
Solution Approach 1:
The control unit acts as an intermediary that processes detection results from multiple illumination images and superimposes them onto the white light image in a coordinated manner. This intermediary processing layer manages the complexity of multi-image integration by implementing systematic algorithms for region alignment, overlay synthesis, and visual enhancement, thereby achieving high diagnostic precision while containing processing complexity through structured intermediate steps.
Solution Approach 2:
The superimposition process applies different processing qualities to different regions of the image. Lesion candidate regions are highlighted with enhanced visual characteristics (e.g., colored overlays, boundary markings) while normal tissue regions remain unchanged. This local quality enhancement focuses processing complexity only on relevant areas, improving diagnostic precision without unnecessarily increasing overall image processing complexity.
Data Source
AI summary
An endoscope processor includes a processor. The processor detects a first region of a lesion candidate from first image information which is acquired by irradiation with first illumination light; detects a second region of a lesion candidate from second image information acquired by irradiation with second illumination light having a different spectrum from the first illumination light; selects a region for display of the lesion candidate out of the first region and the second region, corresponding to an observation target site of a subject; and generates image information for display, in which the region for display is superimposed on the first image information.