Single Vertebra Segmentation in CT Images Using Neural Networks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for segmenting CT images of the spine on a single segment basis face challenges due to the similarity of adjacent structures and differences in vertebrae shape and size, leading to difficulties in achieving high precision and accuracy.
Innovation Solution
A method utilizing neural network models to acquire spine boundary information, vertebra position information, and updating image processing to effectively segment and isolate individual vertebrae within CT images, employing multiple network models for segmentation, morphological expansion, and coordinate system transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional image segmentation methods are used to segment the entire spine from background information, then the segmentation process becomes simpler, but the precision of single-segment vertebrae segmentation deteriorates due to similarity of adjacent structures and differences in vertebrae shape and size
Solution Approach 1:
The patent divides the spine segmentation task into multiple stages: first segmenting the entire spine from background, then further segmenting individual vertebrae from the spine. This multi-level segmentation approach allows the system to leverage the simplicity of whole-spine segmentation while achieving high precision in individual vertebrae segmentation through subsequent refinement steps including boundary detection and coordinate system transformations.
2Manufacturing precision
If multiple network models and processing steps are employed to improve single vertebra segmentation accuracy, then segmentation precision improves, but device complexity and processing time increase
Solution Approach 1:
The patent performs preliminary segmentation of the entire spine from background information before proceeding to individual vertebrae segmentation. This preliminary action creates a simplified working region that reduces the complexity of subsequent segmentation tasks, allowing multiple network models to be applied more efficiently to isolated vertebrae rather than the entire spinal column.
Solution Approach 2:
The patent transforms the segmentation problem from a 2D image space to a 3D coordinate system by introducing spatial transformations and coordinate system conversions. This dimensional change allows the system to process vertebrae in their native anatomical coordinate system, improving segmentation accuracy while organizing the complexity of multiple processing steps into a structured multi-dimensional framework.
3Manufacturing precision
If morphological expansion and coordinate system transformations are applied to improve vertebrae isolation, then segmentation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies morphological expansion selectively to spine regions identified in preliminary segmentation, rather than processing the entire image. This targeted application of computationally intensive operations reduces overall processing time while maintaining high accuracy in vertebrae isolation where it is most needed.
Solution Approach 2:
The patent performs coordinate system transformations and morphological operations as preliminary steps before final vertebrae segmentation. By establishing the correct coordinate framework and expanding relevant regions in advance, the system reduces the computational burden of subsequent segmentation operations, improving overall processing efficiency.
Data Source
AI summary
A single vertebra segmentation method, device, equipment and storage medium. The method comprises: acquiring first spine boundary information, which is boundary information of a spine in a segmentation result obtained through image segmentation of a raw CT image; acquiring first vertebra position information, which is information on the position of a single vertebra in the spine in the raw CT image; acquiring an initial image of the single vertebra according to the first spine boundary information and the first vertebra position information; marking the position of the single vertebra in the initial image of the single vertebra to generate an updated image of the single vertebra; and determining the single vertebra in a restored image corresponding to the raw CT image according to the updated image of the single vertebra. According to the present application, the spine in the CT image can be segmented on a single segment basis.