Atlas-Based Anatomical Region Assignment for Medical Anomalies
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Solution Overview
Problem
Current methods for determining the location of medical anomalies, such as tumors, in medical images are limited by the lack of metadata regarding anatomical labeling, making manual enrichment time-consuming and only applicable to specific diseases or spherical tumors, thus restricting the analysis of large patient datasets.
Innovation Solution
A computer-implemented method that registers patient image data with atlas data using image fusion algorithms to calculate score values for assigning anatomical regions, enabling accurate mapping and labeling of medical anomalies by determining volume intersections and envelope ratios, thereby automating the assignment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual collection and enrichment of patient data is performed, then data accuracy can be maintained, but time consumption increases significantly for large patient pools
Solution Approach 1:
The system performs self-service by automatically determining anatomical labeling locations through image fusion with atlas data, eliminating the need for manual data enrichment while maintaining high accuracy through algorithmic volume intersection calculations
Solution Approach 2:
Manual mechanical processes of data collection and enrichment are replaced with automated image processing algorithms that compute anatomical assignments through digital image fusion and volume intersection calculations
2Measurement precision
If existing labelling location detection tools are used, then specific diseases or spherical tumors can be identified, but applicability is limited to specific diseases or spherical tumors
Solution Approach 1:
The image fusion method provides universal applicability across all anatomical regions and disease types by registering patient images with comprehensive atlas data containing multiple anatomical structures, enabling detection of various tumor shapes and locations beyond spherical brain metastases
Solution Approach 2:
The method transitions from limited 2D slice-based analysis to 3D volume intersection calculations, enabling accurate anatomical assignment for tumors of any shape by computing spatial relationships in three-dimensional space
3Loss of information
If manual data enrichment is performed for large patient pools, then comprehensive analysis can be achieved, but productivity decreases due to time-consuming processes
Solution Approach 1:
The system automatically performs comprehensive data enrichment for entire patient cohorts through automated image fusion and anatomical assignment, maintaining complete data coverage while dramatically increasing processing throughput
Solution Approach 2:
Atlas data is prepared in advance with pre-defined anatomical structures and labeling locations, enabling rapid automated assignment during patient data processing without requiring manual enrichment for each case
4Productivity
If automated image fusion methods are implemented, then processing speed and productivity increase, but system complexity increases
Solution Approach 1:
The atlas data serves as an intermediary structure that mediates between patient images and anatomical labeling, providing a standardized reference framework that simplifies the automated assignment process while enabling high productivity
Solution Approach 2:
The anatomical assignment process is segmented into distinct computational steps: image registration, volume intersection calculation, and anatomical label assignment, making the complex automated system more manageable and implementable
Data Source
AI summary
Disclosed is a computer-implemented method of determining an assignment of an object acquire patient image data of interest recognizable in a digital medical patient image such as a tumour or other medical anomaly such as an implant to an anatomical region. The medical patient image is registered with atlas data, The assignment is then determined by calculating a score value defining an amount of volume intersection between the object of interest and a digital object defining a specific anatomic region, for example a bounding box around a specific organ, which is defined in the atlas data.