Anatomical Positioning Framework Using Neural Networks
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Solution Overview
Problem
Existing methods for automating the mapping of radiology reports to anatomical landmarks in medical images are limited by their reliance on manual keyword extraction, one-dimensional estimations, and limited applicability to natural language sentences.
Innovation Solution
A framework that uses an artificial neural network to map input text from radiology reports into normalized coordinates, allowing for precise anatomical positioning and generating text data associated with points-of-interest in medical images using a large language model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual keyword extraction and parsing methods are used to map text to anatomical landmarks, then the implementation is simple and straightforward, but the scalability and applicability to natural language sentences are limited
Solution Approach 1:
The patent replaces manual keyword extraction and parsing methods with a deep learning-based neural network system. The neural network automatically learns to map natural language text descriptions to anatomical landmarks in medical images, eliminating the need for manual keyword engineering and significantly improving scalability and adaptability to various natural language formulations.
Solution Approach 2:
The patent transforms the mapping problem from discrete keyword matching to continuous coordinate prediction. By outputting normalized coordinates (e.g., [0,1] range) instead of discrete landmark labels, the system can handle nuanced variations in natural language descriptions and provide precise spatial localization.
2Device complexity
If one-dimensional range estimation is used for anatomical positioning, then the computational complexity is low, but the precision and accuracy of landmark localization are insufficient
Solution Approach 1:
The patent extends the positioning from one-dimensional range estimation to two-dimensional (or three-dimensional for volumetric images) coordinate prediction. The neural network outputs precise x, y coordinates (and z for 3D images) that directly correspond to landmark locations in the medical images, significantly improving localization accuracy while maintaining reasonable computational efficiency.
3Reliability
If segmentation or landmarking methods are used to identify anatomical landmarks, then the structural definition is clear, but the scope is limited to only landmarks and organs with defined structure
Solution Approach 1:
The patent creates a universal text-to-landmark mapping system that can handle any anatomical structure mentioned in the radiology report, not just pre-defined segmented organs. The neural network is trained on diverse anatomical landmarks and can generalize to any structure with a text description, making the system universally applicable across different anatomical regions and pathologies.
4Ease of operation
If unstructured text is used to store radiology report information, then the natural language expression is flexible, but the ability to process data on a large scale is limited
Solution Approach 1:
The patent replaces manual text processing with an automated neural network system that can process large volumes of radiology reports. The network automatically extracts and maps anatomical information from unstructured text to image coordinates, enabling scalable processing of large datasets while preserving the flexibility of natural language expressions in the original reports.
Data Source
AI summary
A framework for anatomical positioning. In accordance with one aspect, input text is mapped into normalized coordinates using an artificial neural network. A location in a target image that corresponds to the normalized coordinates is determined and presented. In accordance with another aspect, a user selection of a point-of-interest in a medical image is received. A context set of points nearest to the point-of-interest is determined. A prompt containing the point-of-interest and context set of points is constructed. A large language model may then generate text data associated with the point-of-interest in response to the prompt