Lesion Segmentation via Refined Non-Rigid Registration
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Solution Overview
Problem
Current medical image processing algorithms struggle with fully automatic segmentation of lesions in three-dimensional images, particularly when lesions change shape or size over time, as they can only apply transformations like rotations, translations, scaling, and shearing, limiting their ability to accurately align and compare lesions across different image data sets.
Innovation Solution
The method allows for arbitrary transformations of lesions, including changes in shape, by using a refined registration process that aligns image data sets based on user input, enabling accurate segmentation and comparison of lesions across multiple data sets, even when lesions have undergone significant growth, shrinkage, or changes in density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional registration algorithms are used to align image data sets, then the alignment process is computationally efficient, but the ability to accurately align lesions that have changed shape or size is limited
Solution Approach 1:
The patent implements a two-stage registration process where an initial rigid registration is performed followed by a refined non-rigid registration. The refined registration dynamically adjusts the transformation parameters based on the segmented lesion characteristics, allowing the system to adapt to shape and size changes while maintaining computational efficiency through the staged approach.
Solution Approach 2:
The system performs preliminary lesion segmentation before the refined registration step. By pre-identifying the lesion boundaries and characteristics in the first stage, the system prepares the necessary input data for the subsequent refined registration, enabling more accurate alignment without requiring complex real-time processing during the registration itself.
2Measurement precision
If manual segmentation algorithms are used to segment lesions, then segmentation accuracy can be improved, but the time required for lesion detection and measurement increases
Solution Approach 1:
The system implements an automated iterative refinement process where the algorithm automatically adjusts segmentation parameters based on feedback from the registration process. The refined registration results are fed back to improve the segmentation mask without requiring manual intervention, allowing the system to self-correct and improve accuracy while maintaining automation and speed.
Solution Approach 2:
The patent establishes a feedback loop where the initial segmentation results inform the registration process, and the registration outcomes are used to refine the segmentation. This iterative feedback mechanism continuously improves segmentation accuracy automatically, eliminating the need for time-consuming manual correction while achieving high precision.
3Productivity
If automated segmentation algorithms are used to process lesions, then processing speed is improved, but reliability and accuracy of segmentation decrease
Solution Approach 1:
The patent divides the segmentation process into distinct stages: initial automated segmentation, lesion transformation to reference space, refined registration, and iterative mask refinement. By segmenting the overall process into specialized sub-tasks, the system maintains the speed advantages of automation while incorporating reliability-improving refinement steps that automatically correct errors without manual intervention.
Solution Approach 2:
The system performs more processing than a simple automated segmentation would require by implementing iterative refinement with multiple passes. The refined registration and mask refinement steps perform additional computational work beyond basic automated segmentation, but this excessive action is automated and significantly improves reliability while the overall process remains much faster than manual methods.
4Measurement precision
If linear measurements are used to assess lesions, then the measurement process is simple, but the accuracy of lesion characterization is insufficient
Solution Approach 1:
The patent transitions from linear measurements (1D) to volumetric measurements (3D) by performing automated segmentation that captures the full three-dimensional extent of lesions. The system generates 3D masks and calculates volume, surface area, and other multi-dimensional characteristics, providing comprehensive lesion characterization that goes far beyond simple linear dimensions while maintaining automated efficiency.
Data Source
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AI summary
A system and method for automatic detection of an object feature, such as a lesion, across a plurality of sets of image data, taken from the same subject, which may optionally be a human patient but which may also optionally be any type of animal or a non-biological subject.