Medical Image Processing for Tubular Tissue Resection Planning
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Solution Overview
Problem
Existing medical image processing technologies fail to consider the functional impact of partial resection of organs with tubular tissues, such as blood vessels, leading to potential tissue dysfunction due to blood stream stagnation, increasing patient burden.
Innovation Solution
A medical image processing apparatus and method that visualizes a failure region by designating a resection region, determining a cutting position, and predicting a failure region due to tubular tissue resection, allowing for the display of both regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a resection region including tubular tissue is designated for organ resection, then the resection target can be removed, but a failure region may occur due to blood stream stagnation causing tissue dysfunction
Solution Approach 1:
The system performs preliminary analysis before resection by deriving cutting positions that avoid tubular tissues and predicting failure regions where blood stream stagnation would occur. This allows surgeons to plan resections that prevent tissue dysfunction before the surgery begins.
Solution Approach 2:
The system introduces an intermediary computational model that simulates blood flow and predicts failure regions. This intermediary analysis acts as a mediator between the resection plan and the actual surgical outcome, allowing optimization of the resection region to avoid harmful effects.
2Reliability
If the resection region is expanded to remove more tissue, then the disease can be more thoroughly treated, but the failure region and patient burden increase
Solution Approach 1:
The system applies local quality analysis by evaluating each region of the organ individually. It identifies areas where resection is necessary for disease treatment while preserving regions that would lead to failure if resected. This allows optimized resection that treats disease effectively while minimizing patient burden.
Solution Approach 2:
The system provides feedback by displaying the predicted failure region to the user. This visual feedback allows surgeons to adjust the resection region in real-time, finding the optimal balance between thorough disease treatment and minimizing patient burden.
3Productivity
If tubular tissue is cut during resection, then the resection can be completed, but circulation stagnation occurs causing tissue dysfunction
Solution Approach 1:
The system derives cutting positions that avoid tubular tissues before the resection is performed. By preliminarily identifying safe cutting paths, the surgery can be completed efficiently without compromising circulation function.
Solution Approach 2:
Instead of cutting tubular tissue and then managing the consequences, the system inverts the approach by specifically avoiding tubular tissues during the cutting process. This prevents circulation stagnation before it can occur, allowing resection completion without compromising tissue function.
Data Source
AI summary
A medical image processing apparatus includes a processor and configured to visualize an organ. The processor is configured to: acquire volume data including the organ; designate a tubular tissue included in the organ; designate a first resection region including the tubular tissue, the first resection region being a resection target region in the organ; derive, based on the designated first resection region, a first cutting position at which the tubular tissue is cut; derive, based on the tubular tissue and the first cutting position, a first failure region included in the organ, the first failure region being predicted to cause a tissue to be dysfunctional due to stagnation of circulation in the tubular tissue to be cut; and cause a display to display the organ, the first resection region, and the first failure region.