Automated Medical Image Segmentation Using Statistical Models
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Solution Overview
Problem
Current medical imaging technologies, particularly MRI systems, lack automated data processing tools that can efficiently and accurately segment anatomical data from various imaging modalities, leading to underutilization due to modality-specific tools and the need for human intervention, especially in differentiating soft tissues and handling complex intensity variations.
Innovation Solution
A system and process for automated segmentation of multidimensional anatomical data using a registration module to align datasets with anatomical models, determining core regions, and computing threshold characteristics to segment organs, capable of handling data from multiple imaging modalities like CT and MRI, using a single software tool.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If modality-specific automated tools are used for CT data processing, then manufacturing precision and measurement precision are improved, but adaptability deteriorates
Solution Approach 1:
The patent implements a unified automated segmentation tool that processes both CT and MRI data using the same software platform. The system employs modality-agnostic preprocessing steps and statistical models that can handle intensity variations across different imaging modalities, enabling a single tool to perform segmentation for multiple modalities without requiring separate specialized software for each.
2Measurement precision
If manual segmentation techniques are used, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent implements an automated segmentation system that performs organ segmentation without requiring manual human intervention. The system uses statistical models and intensity thresholding algorithms to automatically identify and segment organs from medical images, eliminating the need for radiologists or technicians to manually trace organ boundaries, thereby dramatically increasing processing speed while maintaining consistent accuracy.
Solution Approach 2:
The patent replaces manual mechanical segmentation operations with automated computational algorithms. Instead of human operators physically tracing organ boundaries on images, the system uses computer-based statistical models and image processing algorithms to automatically perform segmentation, substituting human manual labor with automated mechanical computation.
3Measurement precision
If human intervention is required for segmentation, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements an automated segmentation system that performs organ segmentation without requiring manual human intervention. The system uses statistical models and intensity thresholding algorithms to automatically identify and segment organs from medical images, eliminating the need for radiologists or technicians to manually trace organ boundaries, thereby dramatically increasing processing speed while maintaining consistent accuracy.
4Adaptability or versatility
If MRI systems are deployed without automated processing tools, then adaptability is improved, but productivity deteriorates
Solution Approach 1:
The patent implements a unified automated segmentation tool that processes both CT and MRI data using the same software platform. The system employs modality-agnostic preprocessing steps and statistical models that can handle intensity variations across different imaging modalities, enabling a single tool to perform segmentation for multiple modalities without requiring separate specialized software for each.
Solution Approach 2:
The patent replaces manual mechanical segmentation operations with automated computational algorithms. Instead of human operators physically tracing organ boundaries on images, the system uses computer-based statistical models and image processing algorithms to automatically perform segmentation, substituting human manual labor with automated mechanical computation.
Data Source
AI summary
A system and process for analyzing multidimensional data characterizing a least a portion of a subject is described. An input module accepts at least one multidimensional dataset which is derived from any of several types of data sources. A registration module accepts the dataset from the input module and registers the dataset from the input module to a selected anatomical model to provide a registered dataset. A processing module is coupled to the registration module and uses this in determining a core region and associated core region information, and computes threshold characteristics of the registered dataset. A segmentation module accepts the registered dataset and the core region information from the processing module, and segments the registered dataset to provide a segmented description of an organ from the registered dataset and core region information, where the segmented, registered dataset describes characteristics of the organ of the subject.