Tissue Treatment Planning Algorithm for Detachment Area Placement
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Solution Overview
Problem
Current methods for treating tissue sections are inefficient in placing detachment chambers, leading to suboptimal tissue extraction due to the combinatorial explosion of placement options, overlap, and size variability, making it difficult for human operators to determine the best placement for fluid reservoirs.
Innovation Solution
The implementation of a treatment planning algorithm that uses a distance transform to preferentially place detachment areas near the edge of the area of interest, considering the expected gain and constraints, to optimize tissue extraction while reducing the complexity of multi-area placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If multiple fluid reservoirs are used to treat multiple areas of interest, then the amount of detached tissue increases, but the placement complexity and time required for determining optimal positions increases exponentially
Solution Approach 1:
The treatment area is divided into multiple discrete areas of interest, each treated by a separate fluid reservoir. The system segments the complex placement problem into individual reservoir positioning tasks, where each reservoir's position is determined independently based on its own area of interest, rather than trying to optimize all placements simultaneously
Solution Approach 2:
The positions of all fluid reservoirs are determined in advance through automated computation before the actual treatment begins. The system pre-calculates optimal positions for all reservoirs based on the marked areas of interest, avoiding the need for sequential decision-making during treatment execution
2Productivity
If larger fluid reservoirs are used, then fewer reservoirs are needed to cover the area of interest, but the precision of placement and ability to avoid overlap with non-desired areas decreases
Solution Approach 1:
The system dynamically selects the appropriate size of fluid reservoir based on the characteristics of each area of interest. Larger reservoirs are used for large, simple areas to maximize efficiency, while smaller reservoirs are used for small or complex areas requiring precise placement. This dynamic adaptation allows the system to optimize both productivity and precision for each specific case
3Adaptability or versatility
If manual placement of fluid reservoirs is performed by human operators, then flexibility in decision-making is maintained, but the accuracy and consistency of placement deteriorates due to the difficulty of optimizing among multiple possibilities
Solution Approach 1:
The system uses image processing to detect marked areas of interest on the tissue section and provides feedback to the automated placement algorithm. This feedback loop allows the system to adapt to the specific geometry and location of each area of interest, maintaining flexibility while achieving consistent, accurate placements that are difficult for human operators to reproduce
Data Source
Figure 1
Figure 2a~2b
Figure 3a
AI summary
Some embodiments are directed to planning treatment of an area of interest on a tissue section with a motorized treatment tip. The planning may include computing for each pixel in the area of interest a distance to the nearest pixel in the tissue section outside the area of interest or in the background, and selecting a position for a treatment area from the result of the distance transform.