Kernel-Based Segmentation for Orbital Fracture Imaging
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Solution Overview
Problem
Current methods for segmenting anatomic regions from medical imaging scans, such as orbital fractures, are inefficient and prone to interoperator variability due to the lack of well-defined boundaries and sensitivity to noise and trauma-related inconsistencies.
Innovation Solution
A kernel-based segmentation method that uses a multi-voxel kernel to delineate implicit boundaries by moving outward in a flood-fill fashion, considering the mean and standard deviation of voxel intensities, allowing for robust handling of ill-defined boundaries and noise, and is adaptable to various anatomical structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual outlining methods are used to segment orbital regions from CT scans, then measurement precision can be achieved, but time consumption increases significantly and interoperator variability occurs
Solution Approach 1:
The system performs automatic segmentation of orbital regions without requiring manual intervention. The algorithm independently processes CT scan data, identifies orbital boundaries, and generates quantitative measurements autonomously, eliminating the need for manual outlining by radiologists or technicians.
Solution Approach 2:
The patent replaces the mechanical manual outlining process with an automated computational algorithm. Instead of manually tracing orbital boundaries on imaging software, the system uses image processing techniques including thresholding, morphological operations, and 3D reconstruction to automatically define and measure orbital volumes.
2Extent of automation
If gradient boundary methods are used for segmentation, then automated processing is achieved, but reliability decreases when boundaries are ill-defined or obscured by trauma
Solution Approach 1:
The system dynamically adjusts segmentation parameters based on image characteristics and anatomical context. It modifies threshold values, kernel sizes, and morphological operation parameters adaptively to handle varying degrees of trauma, bone fragmentation, and soft tissue swelling that obscure boundaries in trauma patients.
Solution Approach 2:
The algorithm performs preliminary processing steps including pre-thresholding, morphological opening-closing operations, and bone masking before final segmentation. These preliminary actions prepare the image data by removing artifacts, filling gaps, and enhancing structural information that helps define orbital boundaries even in compromised anatomy.
3Measurement precision
If manual outlining is performed slice-by-slice, then measurement accuracy is maintained, but productivity decreases due to tedious processing time
Solution Approach 1:
The system processes the entire orbital region continuously through a unified 3D algorithm rather than processing each CT slice sequentially. The automated algorithm maintains continuous computation across all slices, dynamically reconstructing the orbital volume as a single integrated 3D structure, which is both faster and more accurate than manual slice-by-slice outlining.
Solution Approach 2:
The patent transitions from 2D slice-by-slice manual outlining to 3D automated volumetric segmentation. Instead of manually tracing boundaries on individual axial slices, the system performs automated segmentation in three dimensions, reconstructing the complete orbital volume and extracting morphological measurements from the 3D structure, significantly improving both speed and accuracy.
Data Source
AI summary
In general, embodiments of the invention comprise systems and methods for delineating objects from imaging scans. According to certain aspects, methods of the invention include aggregating homologous objects from multiple scans into categories, developing profiles or characteristics for these categories, matching newly identified objects to these pre-existing categories for object identification, and red-flagging, or visualization of identified objects.