Surgical Organ Boundary Localization via Video Frame Integration
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Solution Overview
Problem
During surgery, the visibility of internal organs can be obstructed by blood, gases, tissues, tumor growths, and anatomical variations, leading to inaccurate assumptions about organ location, which may compromise surgical precision and safety.
Innovation Solution
A surgical assistive device that captures video frames of internal organs and uses image processing techniques to generate precise boundaries of the organs, integrating appearance likelihood, global segmentation, and edge detection results to provide real-time boundary localization, assisting surgeons in image-guided surgery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a surgeon visually estimates the location of internal organs during surgery, then the surgical procedure can be performed, but the accuracy of organ localization is reduced due to obstructions and anatomical variations
Solution Approach 1:
The system performs preliminary actions by capturing pre-operative medical images (CT, MRI, ultrasound) and processing them to generate three-dimensional models of internal organs before surgery begins. These models are then registered with real-time surgical video feeds, allowing the system to predict and display organ boundaries and locations in advance, compensating for visibility obstructions during the actual surgical procedure.
Solution Approach 2:
The system introduces an intermediary computational model that acts as a mediator between pre-operative imaging data and real-time surgical visualization. This three-dimensional model serves as a virtual reference that is overlaid on the actual surgical field, allowing surgeons to see through obstructions by referencing the virtual organ boundaries and locations generated from multiple imaging modalities.
2Measurement precision
If multiple image processing techniques are integrated for boundary localization, then the precision of organ boundary detection is improved, but the computational complexity and processing time increase
Solution Approach 1:
The image processing system is segmented into distinct functional modules: a medical image processing module that generates three-dimensional models from pre-operative scans, a video processing module that captures and processes real-time surgical footage, and a registration module that aligns the virtual model with the actual surgical field. This modular segmentation allows each component to be optimized independently while maintaining overall system precision.
Solution Approach 2:
The system merges multiple independent image processing techniques—including three-dimensional model generation from CT/MRI data, real-time video frame analysis, edge detection algorithms, and spatial registration methods—into a unified boundary localization system. By combining these complementary techniques, the system achieves high boundary detection accuracy while managing computational complexity through integrated processing pipelines.
3Productivity
If real-time video capture and processing is performed during surgery, then the surgical assistance is provided continuously, but the processing time and computational resources are consumed
Solution Approach 1:
The system performs preliminary processing of medical images to create three-dimensional organ models before surgery begins. This pre-computed virtual model is then efficiently registered with real-time surgical video feeds during the procedure, allowing the system to provide continuous surgical assistance with minimal real-time processing delay, as the computationally intensive model generation has already been completed.
Solution Approach 2:
The video processing system applies different processing qualities to different regions of the surgical field. High-resolution processing is applied to regions containing organ boundaries and critical surgical areas, while lower-resolution processing is applied to background regions. This local quality differentiation maintains surgical assistance accuracy in critical areas while reducing overall computational resource consumption and processing time.
Data Source
AI summary
A surgical assistive apparatus and method to provide assistance during surgery, includes an organ boundary localization circuit that selects a test video frame from a captured sequence of video frames and derives a first global region boundary of an internal organ of interest in the selected test video frame by integration of an appearance likelihood result, a global segmentation result, and a global edge detection result associated with the internal organ of interest. A plurality of local region boundaries are determined for a plurality of local sub regions of the internal organ of interest and a second global region boundary is generated for the internal organ of interest based on the determined plurality of local region boundaries and the first global region boundary. The internal organ of interest is localized based on the generated second global region boundary.