Endoscopic Image Processing for Connective Tissue Navigation
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Solution Overview
Problem
Endoscopic surgical operations face challenges in grasping the whole image of a target organ and determining the optimal state for treatment due to limited visualization and lack of tactile sensation, making it difficult to determine the appropriate state for surgical interventions.
Innovation Solution
A computer-based system that acquires an operation field image from endoscopic surgery, inputs it into a learning model trained to identify connective tissue between preservation and resection organs, and outputs navigation information to assist in treating the connective tissue, allowing for easier visualization of the surgical target's state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If endoscopic surgery is performed with small incisions, then patient trauma is reduced and recovery is faster, but the surgeon's ability to grasp the whole image of the organ and determine optimal treatment state is compromised
Solution Approach 1:
The patent introduces an image processing system as an intermediary between the endoscopic camera and the surgeon. The system processes endoscopic images to highlight connective tissue, preservation organs, and resection organs, effectively mediating the information gap caused by limited direct visualization in endoscopic surgery.
Solution Approach 2:
The patent replaces the mechanical/physical requirement of direct visual inspection with an automated image processing system using machine learning models. Instead of relying on the surgeon's direct observation capabilities, the system uses trained models to analyze and interpret the surgical field automatically.
2Ease of operation
If endoscopic surgery is performed without tactile sensation, then the procedure is simpler and less invasive, but the ability to determine optimal treatment state is reduced
Solution Approach 1:
The patent implements a feedback mechanism where the image processing system continuously analyzes the surgical field and provides real-time guidance to the surgeon. The system evaluates the current state of connective tissue and organ separation, then feeds back recommendations for optimal treatment timing and technique, compensating for the lack of tactile feedback.
Solution Approach 2:
The patent substitutes the tactile sensing capability with an automated visual analysis system. The machine learning model processes visual information to assess tissue properties and treatment appropriateness, replacing the tactile judgment that would normally be made through direct contact with the tissue.
3Measurement precision
If real-time image processing is implemented, then surgical guidance is improved, but computational time and processing load increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on extensive surgical datasets before actual surgery. The models learn to recognize surgical structures and treatment states in advance, enabling rapid real-time inference during the actual surgical procedure without requiring complex processing during the operation itself.
Data Source
AI summary
A non-transitory recording medium recoding a program that causes a computer to execute processing includes: acquiring an operation field image obtained by imaging an operation field of an endoscopic surgery; inputting the acquired operation field image to a learning model trained to output information on a connective tissue between a preservation organ and a resection organ in a case where the operation field image is input, and acquiring information on the connective tissue included in the operation field image; and outputting navigation information when treating the connective tissue between the preservation organ and the resection organ on the basis of the information acquired from the learning model.