3D Thoracic Surgery Planning with Selective Vascular Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current imaging and planning systems for thoracic surgeries, particularly segmentectomy procedures, face challenges in accurately identifying and distinguishing vascular structures within the lungs, leading to inefficiencies in tissue removal and potential complications during surgery.
Innovation Solution
A software application generates three-dimensional models from computed tomography data, allowing clinicians to selectively view and manipulate airways and blood vessels, focusing on the relevant structures around a target area by removing unnecessary generations and clutter, and providing tools for precise navigation and tissue segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If complete 3D models with all airways and blood vessels are displayed, then comprehensive anatomical information is provided, but visual clutter increases making it difficult to identify relevant structures
Solution Approach 1:
The system segments the complete 3D model into multiple hierarchical levels of detail. Users can selectively display different generations of airways and blood vessels based on surgical needs. The crop tool divides the visual field into relevant and irrelevant regions, allowing clinicians to focus on specific anatomical areas without being overwhelmed by the complete model.
Solution Approach 2:
The system implements partial action by allowing users to display only the necessary portions of the 3D model at any given time. The crop tool enables clinicians to selectively hide generations beyond a certain level and remove airways/blood vessels outside the region of interest, showing only the partial information needed for the current surgical planning task.
2Measurement precision
If detailed vascular structures are identified manually, then accurate segmentectomy planning is possible, but the process is time-consuming and challenging even for trained professionals
Solution Approach 1:
The system performs automated identification of airways and blood vessels within the cropped region and their connecting structures. The software automatically traces and highlights the relevant vascular anatomy based on the user-defined crop region, eliminating the need for manual tracing by clinicians while maintaining high accuracy.
Solution Approach 2:
The system pre-processes the 3D model by automatically identifying and marking all airways and blood vessels that enter the cropped region and their connecting structures before the surgeon needs this information. This preliminary automated annotation is available immediately when the crop tool is applied, saving significant time during surgical planning.
3Reliability
If lobectomy is performed to remove diseased tissue, then complete removal of pathology is achieved, but excessive healthy lung tissue is removed reducing surgical candidacy
Solution Approach 1:
The system enables local quality by allowing surgical planning at the segment level rather than requiring removal of entire lobes. By cropping to the specific region containing pathology and automatically identifying only the relevant airways and blood vessels supplying that region, the system allows surgeons to remove only the diseased segment while preserving the rest of the lung, thus maintaining local tissue quality where healthy.
Data Source
AI summary
Systems and method of planning thoracic surgery. A three-dimensional model is generated to provide greater clarity between lung segments as well as identifying the airways and vasculature supporting the lung segment to enable accurate surgical planning.