Semantics-Driven Medical Image Registration
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Solution Overview
Problem
Conventional medical image registration techniques, especially elastic image registration, fail to precisely align anatomical structures of specific interest and struggle with changes due to interventions or partial organ resections, as they treat all regions uniformly and have too many degrees of freedom, leading to misalignments and divergence.
Innovation Solution
A semantics-driven image registration framework that extracts knowledge about anatomy, pathology, and clinical context from images and text-based data, using automatic parsing and semantic labeling to tune the registration focus for precise alignment of structures of diagnostic interest, employing transformation models with optimal degrees of freedom and weights to prioritize regions of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If elastic image registration techniques are used with many degrees of freedom, then flexibility in handling anatomical variations is improved, but precision of aligning specific anatomical structures deteriorates
Solution Approach 1:
The patent divides the image registration process into multiple levels: global rigid/affine registration first, then local elastic registration in specific regions of interest. This segmentation allows different transformation models to be applied to different parts of the image, maintaining both overall flexibility and local precision.
Solution Approach 2:
The patent applies different registration strategies to different regions: rigid transformation for the entire image, affine transformation for specific organs, and elastic transformation only for regions with significant anatomical variations. This local quality approach ensures precision where needed while maintaining adaptability elsewhere.
2Reliability
If global regularization is applied to ensure numerical stability, then convergence of the registration algorithm is improved, but precision of aligning regions of specific interest deteriorates
Solution Approach 1:
The patent applies regularization selectively rather than globally. Strong regularization is applied in regions requiring numerical stability, while weak or no regularization is applied in regions of specific diagnostic interest where precise alignment is critical. This allows the algorithm to converge reliably while maintaining precision in important areas.
Solution Approach 2:
The patent segments the regularization application by identifying regions of interest through semantic labeling and applying different regularization strengths to different segments. This ensures that convergence is achieved through regularization where needed, while precision is maintained in diagnostically important regions.
3Device complexity
If conventional image registration techniques treat all regions uniformly, then simplicity of the algorithm is improved, but ability to handle changes due to interventions or partial organ resections deteriorates
Solution Approach 1:
The patent makes the registration algorithm dynamic by automatically adapting the transformation model based on detected anatomical structures and semantic information. The system selects between rigid, affine, and elastic transformations dynamically based on the specific anatomical region and detected changes, allowing it to handle interventions and resections effectively.
Solution Approach 2:
The patent changes key parameters of the registration algorithm based on semantic information: transformation model type, regularization strength, and region weights are all adjusted based on detected anatomy and semantic labeling. This allows the algorithm to adapt to interventions and partial organ resections without requiring manual reconfiguration.
4Measurement precision
If semantic parsing and automatic labeling are implemented, then precision of aligning diagnostic structures is improved, but complexity of the system increases
Solution Approach 1:
The patent introduces semantic labeling and automatic anatomical structure detection as intermediary steps between image acquisition and registration. These intermediaries provide structured information that guides the registration process, improving precision while keeping the overall system manageable through modular architecture.
Solution Approach 2:
The patent implements automatic semantic parsing and anatomical labeling that performs much of the work without human intervention. The system automatically identifies regions of interest, selects appropriate transformation models, and adjusts registration parameters based on detected anatomy, reducing the need for manual configuration despite the increased computational complexity.
Data Source
AI summary
A method and system for automatic semantics driven registration of medical images is disclosed. Anatomic landmarks and organs are detected in a first image and a second image. Pathologies are also detected in the first image and the second image. Semantic information is automatically extracted from text-based documents associated with the first and second images, and the second image is registered to the first image based the detected anatomic landmarks, organs, and pathologies, and the extracted semantic information.