Endoscope Learning Support Device for Training Image Generation
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Solution Overview
Problem
Existing endoscope systems face challenges in automatically recognizing treatment instruments within endoscopic images, particularly when there are limited training images available, as deep learning methods require a large number of images and struggle with variations in color due to background influences.
Innovation Solution
A learning support device that forms a training image by adjusting the hue, saturation, or brightness of a superimposed image containing treatment instruments, using Generative Adversarial Networks (GANs) or other GAN variants, to create images similar to clinical images, enabling the formation of a learning model with high recognition accuracy for treatment instruments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning methods are used to recognize treatment instruments, then recognition capability is improved, but a large number of training images are required which increases data requirements and complexity
Solution Approach 1:
The patent uses Generative Adversarial Networks (GANs) to generate synthetic training images that copy the visual characteristics of real clinical images. These generated images serve as artificial training data, replacing the need to collect large numbers of real clinical images. The GAN system creates realistic-looking endoscopic images with treatment instruments that can be used for training deep learning models without requiring actual surgical footage.
Solution Approach 2:
The patent applies parameter changes by adjusting color properties (hue, saturation, brightness) of generated images to match the statistical characteristics of real clinical images. This involves modifying image parameters to ensure the synthetic training data reflects the actual visual variations found in real endoscopic surgery, thereby improving recognition accuracy without needing extensive real image collections.
2Ease of manufacture
If simple averaging of two images is used to form training images, then image formation process is simplified, but color accuracy and realism are degraded
Solution Approach 1:
Instead of simple averaging, the patent employs GANs that dynamically adjust multiple image parameters including color distribution, brightness, and texture characteristics. The system learns the statistical parameters of real clinical images and applies these transformations to generated images, preserving color accuracy and visual realism while maintaining automated image formation.
Solution Approach 2:
The patent specifically addresses color accuracy by using GANs to learn and replicate the color distributions found in real endoscopic images. The system adjusts hue, saturation, and brightness parameters to match clinical image characteristics, ensuring that treatment instruments appear with accurate colors and that background tissues have realistic color variations, thereby overcoming the color degradation issue of simple averaging methods.
3Device complexity
If treatment instruments are placed on uniform backgrounds, then image processing is simplified, but recognition accuracy deteriorates due to lack of contextual variation
Solution Approach 1:
The patent uses GANs to generate diverse background parameters that replicate the complexity of real surgical fields. Instead of uniform backgrounds, the system creates varied tissue textures, colors, and patterns that match clinical environments. This contextual variation helps the recognition model learn to identify instruments against different background conditions, improving accuracy while the automated generation process keeps processing complexity manageable.
Data Source
AI summary
A learning support device includes a processor. The processor is configured to: form a foreground image containing at least one treatment instrument by placing an image of the at least one treatment instrument within an image region; form a superimposed image by superimposing the foreground image on a background image; and form a training image by adjusting at least one of hue, saturation, or brightness of the superimposed image.