Image Segmentation for Anatomical Structure Identification
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Solution Overview
Problem
Current image guided radiation therapy (IGRT) systems face challenges in accurately identifying anatomical structures in patient images, which is crucial for precise radiation delivery and minimizing healthy tissue exposure.
Innovation Solution
The method involves acquiring patient images, segmenting them using a hierarchical series of image processing steps to identify landmarks, and aligning anatomical structures with probabilistic atlases to refine tissue probabilities, ultimately fitting a mesh to represent organ structures and establish expected shapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation methods are used to identify anatomical structures, then the process is simpler and faster, but the accuracy of structure identification is insufficient
Solution Approach 1:
The patent applies segmentation by dividing the image processing task into multiple hierarchical levels: initial segmentation to identify candidate regions, probabilistic atlas alignment to refine tissue classification, and mesh fitting to precisely define organ boundaries. This multi-stage segmentation approach improves identification accuracy while managing system complexity through structured decomposition of the processing workflow.
Solution Approach 2:
The patent introduces probabilistic atlases as an intermediary element that bridges raw image data and anatomical structure identification. The atlases provide pre-computed probability maps of tissue types that guide and refine the segmentation process, enabling more accurate structure identification without requiring the system to process all image data from scratch.
2Manufacturing precision
If traditional segmentation methods are used, then the processing time is shorter, but the precision of radiation therapy planning is reduced
Solution Approach 1:
The patent applies preliminary action by pre-computing probabilistic atlases from training data before actual image analysis. These pre-computed probability maps are stored and reused during clinical workflows, allowing rapid refinement of segmentation results without performing computationally intensive calculations in real-time, thus improving precision while minimizing additional processing time.
Solution Approach 2:
The patent implements continuity of useful action through iterative refinement processes where segmentation results are continuously improved by repeatedly applying probabilistic atlas alignment and mesh fitting operations. The system maintains and refines segmentation quality through multiple passes, ensuring high precision in radiation therapy planning while optimizing the balance between processing time and result quality.
Data Source
AI summary
A system and method of identifying anatomical structures in a patient. The method includes the acts of acquiring an image of the patient, the image including a set of image elements; segmenting the image to categorize each image elements according to its substance; computing the probability that the categorization of each image element is correct; resegmenting the image starting with image elements that have a high probability and progressing to image elements with lower probabilities; aligning at least one of the image elements with an anatomical atlas; and fitting the anatomical atlas to the segmented image.