Learning Model for Laparoscopic Blood Vessel Recognition
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Solution Overview
Problem
In laparoscopic surgery, it is difficult for surgeons to recognize and identify blood vessels within the operation field image, which can lead to complications such as damage to blood vessels during procedures.
Innovation Solution
A computer-based system that uses learning models to acquire and process operation field images, distinctively recognizing small and notable blood vessels, and providing support information to surgeons through a dedicated device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a laparoscope is used to shoot operation field images, then the inside of the body can be visualized, but blood vessels are difficult to recognize and identify
Solution Approach 1:
A learning model serves as an intermediary between the operation field image and the surgeon's visual perception. The learning model processes the operation field image to generate enhanced images that highlight blood vessels, making them easier to detect and recognize without requiring the surgeon to directly interpret the raw image data.
Solution Approach 2:
The learning model changes the color or visual characteristics of blood vessels in the operation field image. By enhancing the color contrast or applying color overlays to blood vessel regions, the system makes blood vessels more distinguishable from surrounding tissues, thereby improving recognition capability.
2Loss of information
If the operation field image is processed to recognize blood vessels, then blood vessel information can be obtained, but the processing complexity increases
Solution Approach 1:
The learning model is pre-trained to automatically recognize and extract blood vessel information from operation field images. Once trained, the model can independently process new images without requiring complex manual intervention or additional sophisticated processing systems, thereby reducing operational complexity while maintaining information extraction capability.
Solution Approach 2:
The learning model is trained in advance using a training set of operation field images and corresponding blood vessel annotations. This preliminary training allows the model to automatically identify and extract blood vessel information during actual surgery without requiring complex real-time processing or expert annotation, simplifying the overall system operation.
3Reliability
If blood vessel recognition is performed to prevent damage, then surgical safety improves, but the time required for processing increases
Solution Approach 1:
The learning model is pre-trained offline using a comprehensive training set, allowing it to perform rapid inference during actual surgery. The model learns to identify blood vessels and their characteristics in advance, enabling fast and accurate recognition during time-sensitive surgical procedures without requiring lengthy real-time processing.
Solution Approach 2:
The system replaces manual visual inspection and real-time computational analysis with a pre-trained learning model that can quickly infer blood vessel locations and characteristics. This substitution of mechanical processing with intelligent inference significantly reduces processing time while maintaining or improving surgical safety through more accurate and faster blood vessel identification.
Data Source
AI summary
A computer program causes a computer to execute processing of acquiring an operation field image obtained by shooting an operation field of a scopic surgery, and distinctively recognizing blood vessels included in the acquired operation field image and a notable blood vessel among the blood vessels by using a learning model trained to output information relevant to a blood vessel when the operation field image is input.