Adaptive Slice Selection for 3D Manual Segmentation
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Solution Overview
Problem
Uniform slice selection in 3D manual segmentation is not optimal for minimizing propagation error in 3D reconstruction, as anatomical information is not uniformly distributed, leading to suboptimal 3D segmentation reconstruction accuracy.
Innovation Solution
Adaptive slice selection through clustering, where slices are selected as cluster centers based on estimated pairwise label propagation errors, reducing the number of slices required for manual annotation while maintaining accuracy, using deformable registration and intensity-based label propagation error models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform slice selection is used for 3D manual segmentation, then the selection process is simple and fast, but the propagation error increases and reconstruction accuracy deteriorates
Solution Approach 1:
The patent changes the selection criterion from uniform spatial distribution to error-based selection. Slices are selected based on their contribution to minimizing propagation error, using parameters such as intensity difference, structural similarity, and registration accuracy to determine which slices should be manually segmented versus interpolated.
Solution Approach 2:
The patent applies different selection strategies to different regions of the 3D volume based on local anatomical characteristics. Regions with high anatomical variability or low registration accuracy receive more manual annotations, while homogeneous regions use interpolation, optimizing the balance between annotation effort and reconstruction accuracy.
2Manufacturing precision
If more slices are selected for manual annotation, then reconstruction accuracy improves, but the annotation time and cost increase
Solution Approach 1:
The patent applies partial annotation strategy, selecting only the essential slices that provide maximum information for accurate reconstruction. Instead of annotating all slices or using uniform sampling, the method identifies and annotates only those slices that are critical for minimizing propagation error, reducing annotation workload while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary analysis of the 3D volume using automated segmentation or rough registration to identify regions that require manual correction. This preliminary step guides the selection of slices for manual annotation, ensuring that annotation efforts are focused on the most problematic areas before final reconstruction.
3Manufacturing precision
If adaptive slice selection based on error estimation is implemented, then propagation error decreases, but the complexity of the selection process increases
Solution Approach 1:
The patent replaces complex manual error analysis with automated computational methods. Error estimation is performed using image processing algorithms that calculate intensity differences, structural similarity metrics, and registration accuracy automatically, substituting manual assessment with efficient computational approaches.
Solution Approach 2:
The patent introduces intermediate computational steps that bridge the gap between raw image data and slice selection decisions. Error estimation metrics serve as intermediaries that translate complex image characteristics into quantifiable selection criteria, making the adaptive selection process systematic and reproducible.
Data Source
AI summary
Slice selection for interpolation-based 3D manual segmentation is provided such that propagation error is minimized during 3D reconstruction. In various embodiments, a plurality of 2D images is read. Each of the plurality of 2D images represents a slice of a 3D volume. Deformable registration is performed between each adjacent pair of the plurality of 2D images. From the deformable registration, propagation error is estimated between each pair of the plurality of 2D images. The plurality of 2D images is clustered into a predetermined number of clusters. A slice is selected for annotation from each of the predetermined number of clusters.