Iterative Medical Imaging Platform Segmentation Registration
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Solution Overview
Problem
Existing medical imaging processing techniques are computationally intensive, leading to unacceptable processing times and economically unfeasible computational capacities in clinical settings, often resulting in coarse or rough processing results.
Innovation Solution
A medical-imaging digital-computing platform that automatically employs both segmentation and registration modules within an iterative process, dynamically determining which module to use based on prior results and generating intermediate data objects to facilitate efficient processing of medical images, thereby optimizing the use of both methodologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative processes using segmentation or registration are employed to process medical images, then processing accuracy and detail are improved, but computational time and resource requirements increase significantly
Solution Approach 1:
The patent applies segmentation by dividing the medical image processing task into multiple discrete iterations, where each iteration processes a subset of pixels or image regions. This allows the system to work on manageable portions sequentially, improving accuracy through repeated refinement while controlling computational time by limiting each iteration's scope.
Solution Approach 2:
The patent implements dynamics by making the iterative processing adaptive - the system dynamically adjusts which pixels or regions are processed in each iteration based on convergence criteria and image characteristics. This dynamic approach ensures computational resources are focused on areas needing improvement, maintaining accuracy while reducing unnecessary processing time.
2Measurement precision
If iterative processes using segmentation or registration are employed to process medical images, then processing accuracy and detail are improved, but computational capacity requirements become economically unfeasible
Solution Approach 1:
The patent segments the computational workload into iterative passes over different pixel subsets, allowing standard hardware to handle complex processing tasks. By dividing the problem into manageable iterations rather than requiring all pixels to be processed simultaneously, the system achieves high accuracy without needing prohibitively expensive computational infrastructure.
Solution Approach 2:
The patent applies partial action by processing only the necessary portions of the image in each iteration - specifically targeting pixels that benefit from registration or segmentation operations. This selective approach maintains processing accuracy while avoiding the excessive computational capacity requirements that would result from processing every pixel uniformly.
3Productivity
If existing segmentation or registration approaches are used separately, then processing speed is maintained, but processing results become too coarse or rough for clinical needs
Solution Approach 1:
The patent merges segmentation and registration approaches into a unified iterative framework where both methods work together across multiple passes. This combination allows the system to maintain processing speed through efficient algorithm integration while achieving clinically acceptable result quality by leveraging the complementary strengths of both segmentation and registration in each iteration.
Solution Approach 2:
The patent ensures continuity of useful action by implementing an iterative process where segmentation and registration operations continuously refine the image processing results. Each iteration builds upon previous results, maintaining productive processing speed while progressively improving result quality until clinical needs are met, avoiding the coarse results of single-pass approaches.
Data Source
AI summary
A medical-imaging digital-computing platform serves to access a plurality of data objects and to execute an iterative process with respect to these data objects. The data objects themselves each at least generally pertain to portions of the human anatomy and may comprise, for example, both a source data object and a target data object. The platform executes the iterative process to determine at least one of a labeling of a portion of one of the data objects and a geometric relationship between at least portions of at least two of the data objects. This can be done, for example, by automatically employing both a segmentation module and a registration module as steps within the iterative process. This can also comprise determining when to automatically generate an intermediate data object to provide as input to at least one of these modules.