3D Lung Segment Identification for CT Surgical Planning
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Solution Overview
Problem
Existing 3D reconstruction techniques for lungs in medical imaging do not include an automatically generated depiction of lung segments, making it difficult for surgeons to accurately plan surgical interventions such as segmentectomy, which requires precise identification of segment boundaries.
Innovation Solution
A method for computationally identifying lung segments by analyzing 3D medical images, such as CT scans, to generate a visual depiction of segment boundaries using machine learning techniques, including supervised learning and anatomical structure recognition, to assist surgeons in planning surgeries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing 3D reconstruction techniques are used, then the lung surface and lobes can be depicted, but the segment boundaries within lobes cannot be automatically identified
Solution Approach 1:
The patent applies segmentation by dividing the lung into lobes and further dividing lobes into segments based on anatomical structures. The system identifies segment boundaries by analyzing the spatial relationships between anatomical features (veins, arteries, airways) and the lung surface, automatically creating segmented representations that preserve segment boundary information without requiring manual intervention.
2Extent of automation
If manual identification of segment boundaries is performed, then accurate segment boundaries can be obtained, but the process is time-consuming and not automated
Solution Approach 1:
The system performs self-service by automatically identifying segment boundaries through computational analysis of medical images. The algorithm autonomously detects anatomical structures, determines their spatial relationships, and generates segment boundary definitions without requiring manual annotation or surgeon intervention, thereby achieving full automation while maintaining accuracy.
3Measurement precision
If detailed anatomical structures are visualized, then surgical planning accuracy is improved, but the visual complexity of the representation increases
Solution Approach 1:
The patent applies local quality by differentiating the visual representation based on anatomical location and significance. The system enhances the visualization of segment boundaries and critical anatomical structures (such as veins, arteries, and airways) while maintaining appropriate detail levels in other regions, allowing surgeons to focus on clinically relevant features without being overwhelmed by unnecessary complexity.
Data Source
AI summary
Methods and apparatuses for identifying lung features are provided herein.