Medical Image Processing for Stent Sizing
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Solution Overview
Problem
Current medical imaging technologies, such as IVUS and OCT, struggle to accurately determine the size and placement of stents within luminal organs during procedures, lacking efficient methods for real-time object detection and size determination within medical images.
Innovation Solution
A medical system comprising a catheter with integrated sensors, an image processing apparatus, and a display unit that uses machine learning models to analyze IVUS and OCT images, determining stent size and placement based on object detection within the images and displaying this information for medical professionals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to detect objects and determine stent size in medical images, then measurement precision and diagnostic accuracy are improved, but device complexity and computational requirements increase
Solution Approach 1:
A machine learning model serves as an intermediary between the medical image input and the stent size determination output. The model processes the complex task of object detection and size measurement, acting as a mediator that translates image data into actionable medical information without requiring the entire system to handle all computational complexity directly.
Solution Approach 2:
The machine learning model performs preliminary detection and measurement actions on medical images before final stent selection and implantation. By pre-identifying objects (lumen walls, plaques, existing stents) and calculating size parameters in advance, the system reduces the complexity of real-time decision-making during the medical procedure.
2Productivity
If automated machine learning analysis is implemented for real-time stent size determination, then productivity and procedural efficiency are improved, but measurement precision may be compromised due to algorithmic limitations
Solution Approach 1:
The system incorporates feedback mechanisms where the machine learning model's detections are validated and refined. The model processes images rapidly to provide initial measurements, then these results can be reviewed, adjusted, or fed back for iterative improvement, ensuring both speed and precision are maintained throughout the determination process.
3Loss of information
If detailed object detection is performed to identify lumen walls, plaques, and existing stents, then information completeness is improved, but loss of time during image processing increases
Solution Approach 1:
The machine learning model segments the image processing task by detecting different objects (lumen walls, plaques, existing stents) as separate entities with distinct features. This segmentation allows the system to process and identify multiple medical features simultaneously through specialized detection pathways, maintaining information completeness while reducing overall processing time compared to sequential analysis.
Data Source
AI summary
A medical system includes a catheter that includes a sensor and can be inserted into a luminal organ, a display apparatus, and an image processing apparatus configured to: generate an image of the luminal organ based on a signal output from the sensor of the catheter, input the generated image to a machine learning model and acquire an output indicating a type and a region of an object in the image, determine a size of a stent to be implanted into the luminal organ based on the type and region of the object, and cause the display apparatus to display information indicating the determined size of the stent.