VR Surgical Planning System for Lung Segmentectomy
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Solution Overview
Problem
Conventional 2D CT imaging techniques struggle to accurately identify segmental borders and branches of arteries, veins, and bronchi during complex surgeries like thoracoscopic lung segmentectomies due to anatomical variations and abnormalities in pulmonary vascular anatomy, limiting preoperative understanding and increasing surgical complexity.
Innovation Solution
A VR-based system combining AI and immersive 3D visualization, utilizing a control module, user interaction module, and input/output module to provide detailed, patient-specific anatomical insights through 2D and 3D visualizations, allowing real-time interaction and editing of anatomical structures, and facilitating dynamic collaboration among surgeons.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional 2D CT imaging techniques are used for preoperative planning, then the imaging process is simple and quick, but the identification of segmental borders and branches of arteries, veins, and bronchi becomes more challenging
Solution Approach 1:
The patent transforms 2D CT images into immersive 3D virtual reality visualizations, allowing surgeons to view anatomical structures from multiple angles and depths. This dimensional transition enables accurate identification of segmental borders and vascular branches by providing spatial context that is lost in 2D representations.
Solution Approach 2:
The system introduces an AI-based image processing intermediary that automatically segments and highlights critical anatomical structures (arteries, veins, bronchi) in the 3D visualization. This intermediary layer processes the raw CT data to emphasize clinically relevant features, making identification easier without requiring complex manual annotation.
2Reliability
If immersive 3D-VR platform with AI is implemented, then preoperative understanding of anatomy is improved, but system complexity and computational requirements increase
Solution Approach 1:
The AI-based automated imaging algorithms perform self-service by automatically segmenting, labeling, and organizing anatomical structures from CT scans without requiring manual intervention. The system independently processes images to generate 3D models with identified segmental borders and vascular structures, reducing the need for complex manual planning procedures.
Solution Approach 2:
The system performs preliminary processing of CT images by pre-segmenting and pre-visualizing anatomical structures before the surgical planning session. This advance preparation creates ready-to-use 3D models with identified critical structures, allowing surgeons to focus on decision-making rather than manual annotation during the actual planning meeting.
3Manufacturing precision
If detailed 3D visualizations are provided, then surgical precision is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs comprehensive 3D visualization and AI-based segmentation in advance, before the surgical procedure. This preliminary processing creates detailed patient-specific anatomical models that can be reviewed and annotated during preoperative planning meetings, allowing time-consuming computations to be completed beforehand rather than during surgery.
Solution Approach 2:
The system creates accurate digital copies (3D virtual models) of the patient's anatomy from CT scan data. These digital twins replicate the patient's specific anatomical variations and can be manipulated, measured, and analyzed repeatedly without additional processing time, as the computational work is performed once during model generation.
Data Source
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AI summary
The present invention relates to a system (100) for visualising organs for planning of resections of tumors prior to surgery, and for providing an immersive view into anatomical structures of a patient.