Local Segmentation Extension for Peripheral Lung Tissue Navigation
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Solution Overview
Problem
Current medical devices and procedures face challenges in accurately visualizing, accessing, and manipulating small peripheral target tissues in the lungs, such as nodules or tumors, due to limitations in existing bronchoscopic tools and imaging techniques, particularly for tissues less than 2 cm in size, which are difficult to reach and require improved segmentation and image processing methods for precise localization and sampling.
Innovation Solution
The method involves a local extension technique for segmenting anatomical treelike structures in 3D image data, using spillage-constrained region growing and skeletonization to enhance the segmentation of tubular structures, allowing for the detection of target tissues and navigation systems to accurately locate and extend the segmentation, enabling precise targeting of peripheral lung tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional bronchoscopic tools and imaging techniques are used, then the procedure is simple and device complexity is low, but the ability to visualize and access small peripheral target tissues is insufficient
Solution Approach 1:
The patent applies segmentation by dividing the lung tissue into distinct anatomical regions and treelike structures (bronchial trees, vascular trees). The method segments 3D image data to identify and separate target tissues from surrounding structures, enabling precise visualization of small peripheral lesions. This segmentation approach directly improves measurement precision by creating clear boundaries and hierarchical organization of anatomical features.
Solution Approach 2:
The patent transitions from 2D imaging to 3D volumetric visualization by reconstructing treelike structures in three dimensions. This dimensional enhancement allows practitioners to navigate and visualize peripheral target tissues with greater spatial accuracy, improving the ability to locate and access small lesions that are difficult to identify in conventional 2D images.
2Measurement precision
If advanced segmentation methods are applied to improve target tissue localization, then measurement precision improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by performing skeletonization and pre-processing of 3D image data before the actual segmentation and analysis. The method pre-identifies treelike structures and creates skeletal representations that simplify subsequent processing. This preliminary organization of data reduces the computational burden during real-time navigation and improves processing efficiency while maintaining high localization accuracy.
3Ease of operation
If conventional imaging techniques are used, then device complexity is low, but the ability to access and sample peripheral target tissues is insufficient
Solution Approach 1:
The patent introduces an intermediary navigation system that acts as a mediator between the practitioner and the target tissue. This system includes software tools that integrate segmentation results with real-time imaging, providing guidance for instrument navigation. The intermediary system translates complex segmentation data into intuitive visual displays and navigation paths, making it easier for practitioners to access peripheral target tissues without directly managing the complexity of the underlying algorithms.
4Loss of information
If 3D image data segmentation is performed to extend treelike structures, then visualization of peripheral tissues improves, but computational requirements and processing complexity increase
Solution Approach 1:
The patent applies extraction by isolating and focusing on specific treelike structures (bronchial trees, vascular trees) from the complete 3D image data. The method extracts these anatomical features through region growing algorithms that selectively expand from seed points along tubular structures. By extracting only the relevant treelike structures and their connections to target tissues, the system achieves complete anatomical visualization without processing the entire volumetric dataset, thereby reducing computational requirements.
Data Source
AI summary
A local extension method for segmentation of anatomical treelike structures includes receiving an initial segmentation of 3D image data including an initial treelike structure. A target point in the 3D image data is defined, and a region of interest based on the target point is extracted to create a sub-image. Highly tubular voxels are detected in the sub-image, and a spillage-constrained region growing is performed using the highly tubular voxels as seed points. Connected components are extracted from the results of the region growing. The extracted components are pruned to discard components not likely to be connected to the initial treelike structure, keeping only candidate components likely to be a valid sub-tree of the initial treelike structure. The candidate components are connected to the initial treelike structure, thereby extending the initial segmentation in the region of interest.