Automatic Upper Airway Volumetric Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for upper airway volumetric analysis, such as X-ray radiographs, are inadequate for accurate clinical diagnosis due to reliance on linear and angular measurements, and manual/semi-automatic segmentation techniques require significant human intervention, time, and expertise, making them inefficient for effective treatment planning.
Innovation Solution
A fully automatic segmentation method using a rule-based approach based on anatomical knowledge, involving adaptive thresholding, landmark detection, and three-dimensional morphological operators for accurate volume extraction and classification of upper airway and paranasal sinus sub-regions, employing level set segmentation algorithms for precise volumetric analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual/semi-automatic segmentation techniques are used, then segmentation can be performed with existing methods, but it requires high level of human interventions, time and efforts
Solution Approach 1:
The segmentation system performs automatic initialization of contours and self-adjusts segmentation parameters based on anatomical knowledge rules, eliminating the need for manual intervention. The system independently completes threshold selection, landmark detection, and contour initialization without human input, achieving fully automated segmentation that reduces time and effort requirements
Solution Approach 2:
The system pre-establishes anatomical knowledge rules and boundary definitions before segmentation begins. By preparing the rule-based framework in advance, the system enables rapid automatic segmentation execution without requiring manual setup during the actual segmentation process, significantly reducing time and effort
2Extent of automation
If manual/semi-automatic segmentation techniques are used, then segmentation can be performed, but it is dependent on human perception and experience making it tedious
Solution Approach 1:
The system replaces manual mechanical segmentation operations with an automated computational system based on anatomical knowledge rules. The rule-based algorithm automatically performs thresholding, landmark detection, and contour initialization, substituting human perception and experience with objective computational logic, thereby eliminating the tedious nature of manual segmentation
Solution Approach 2:
The system extracts and applies specific anatomical knowledge rules from the complex task of manual segmentation. By isolating key anatomical boundaries and characteristics into discrete rules, the system automates the segmentation process, removing the need for human observers to perform tedious manual operations while maintaining anatomical accuracy
3Measurement precision
If X-ray radiographs with linear and angular measurements are used, then airway analysis can be performed, but true volume calculation is not achieved leading to probable error
Solution Approach 1:
The system transitions from two-dimensional X-ray radiograph measurements to three-dimensional volumetric segmentation. By processing CT/CBCT volumetric data and applying rule-based segmentation in 3D space, the system accurately calculates true airway volumes, eliminating the information loss inherent in linear and angular measurements on 2D images
Solution Approach 2:
The system changes the measurement parameters from linear and angular dimensions to volumetric parameters. By segmenting the airway into three-dimensional regions and calculating volumes based on anatomical boundaries, the system provides accurate volumetric measurements that reflect true airway capacity, resolving the limitation of conventional 2D measurement approaches
Data Source
AI summary
Described herein is a method detecting a plurality of upper respiratory tract sub-regions automatically. Volume of interest (VOI) is identified based on the extraction of certain features, such as regional properties and shape-based features. The complete airway volume from a patient's data is identified by observing the area and eccentricity profiles of the certain volume/organ in the skull. Maxillary sinus area and eccentricity profile in the sagittal view is chosen in the present subject matter for level 1 VOI identification. Once a level 1VOI is identified, the other sub-regions existing in the same VOI are further identified as individual level 2 VOI. Level 3 VOI is extracted based on the shape and geometric features of the organ. The extracted level 3 VOI is considered as the active contour that is followed by the initialized contour for the accurate segmentation of upper airway sub-regions.