Automated Anatomical Structure Localization Using Deformable Mapping
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Solution Overview
Problem
Current methods for localizing anatomical structures in medical images, such as tumors or brain structures, are time-consuming and labor-intensive, requiring manual identification and alignment of images taken at different times, especially during procedures like radiotherapy or surgery, which is dependent on expert knowledge and not fully automated.
Innovation Solution
A method and device for automatically localizing anatomical structures in medical images by determining a mapping function between a reference data set and subsequent data sets using intensity distributions, allowing for the transfer of outlined objects from a reference data set to other data sets without manual interaction, utilizing algorithms for deformable registration and elastic transformation to maintain anatomical accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and alignment of anatomical structures is performed, then anatomical accuracy can be maintained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs automatic localization and alignment of anatomical structures using computational algorithms that process medical images independently, without requiring manual expert intervention. The mapping function automatically identifies corresponding structures across different imaging time points, enabling the system to serve itself rather than relying on human operators.
Solution Approach 2:
The patent replaces manual mechanical alignment procedures with automated computational image processing. Instead of experts manually comparing and aligning images, a mapping function computed from intensity distributions automatically transforms and aligns anatomical structures across different imaging sessions, substituting human mechanical operations with algorithmic processing.
2Measurement precision
If manual expert knowledge is used for localization, then accurate identification of structures can be achieved, but the process requires significant expertise and is not fully automated
Solution Approach 1:
The system achieves full automation by implementing self-service capabilities where the computational algorithm automatically performs localization without human intervention. The mapping function independently processes images, identifies anatomical structures, and performs alignment, making the system self-sufficient rather than dependent on expert operators.
Solution Approach 2:
The patent creates a computational model (mapping function) that copies and replicates the localization expertise previously held only by human experts. This digital model encapsulates the knowledge needed for accurate structure identification and applies it consistently across all images, replacing the need for human expert copying of anatomical knowledge.
3Loss of information
If multiple images are processed manually to track tumor movement or growth, then comprehensive analysis can be performed, but the time and expertise requirements increase significantly
Solution Approach 1:
The mapping function serves multiple purposes simultaneously: it localizes anatomical structures, tracks tumor movement, monitors growth changes, and aligns images across different time points. This single automated tool performs what previously required multiple separate manual analysis tasks, making the system multi-functional and efficient.
Solution Approach 2:
The system enables continuous automated processing of multiple images to track tumor progression over time. Rather than performing discrete manual analyses at each time point, the mapping function continuously processes the image series, maintaining uninterrupted analysis and preserving all temporal information about tumor movement and growth.
Data Source
AI summary
A method for automatically localizing at least one object or structure in a second data set is provided. A reference data set is provided, and at least one object or structure is outlined or marked in the reference data set, the outline or marking information being a first or reference label data set. A mapping function is determined, using which said reference data set is approximately mapped onto said second data set, and the reference label data set assigned to said reference data set is transformed into a second label data set using said mapping function.