Image-Based Nerve Fiber Extraction With Automated ROI Mapping
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Solution Overview
Problem
Conventional manual drawing of regions of interest (ROIs) in DTI imaging for nerve fiber extraction is cumbersome, leading to inefficient neural fiber tracking.
Innovation Solution
An automated system and method for determining ROIs using image-based nerve fiber extraction, involving an imaging device, processing device, and storage device, which includes modules for obtaining, extracting, and tracking nerve fibers based on anatomical and diffusion images, utilizing trained extraction models and tracking algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual drawing of ROIs is used in DTI imaging, then the doctor can precisely select regions of interest, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The patent uses pre-stored template images containing anatomical information as copies to automatically determine ROIs. The system overlays the template image with the DTI image and automatically identifies corresponding regions, eliminating the need for manual drawing while maintaining anatomical accuracy. This copying approach allows precise ROI selection without time-consuming manual intervention.
Solution Approach 2:
The patent replaces the mechanical manual drawing process with an automated image processing system. Instead of requiring doctors to manually trace ROIs using graphical interfaces, the system uses computational algorithms to automatically identify and extract nerve fiber tracts based on template matching and diffusion tensor imaging data, substituting manual mechanical operations with automated digital processing.
2Measurement precision
If manual drawing of ROIs is used, then anatomical accuracy can be maintained, but the efficiency of neural fiber tracking decreases
Solution Approach 1:
The patent performs preliminary action by pre-storing template images with annotated anatomical information before actual nerve fiber tracking. These templates are prepared in advance and used to automatically determine ROIs during processing, eliminating the need for manual annotation during the tracking process and significantly improving efficiency while maintaining anatomical accuracy.
Solution Approach 2:
The system uses template images as copies of anatomical structures to automatically identify ROIs. By overlaying and matching these pre-prepared templates with patient-specific DTI images, the system achieves both anatomical accuracy and high processing efficiency, as the template matching algorithm can rapidly identify corresponding regions without manual intervention.
3Productivity
If automated ROI determination is implemented, then the efficiency of nerve fiber extraction is enhanced, but the complexity of the processing system increases
Solution Approach 1:
The patent simplifies the automated processing system by using template images as copies of anatomical structures. Instead of requiring complex real-time image analysis algorithms, the system uses pre-stored templates that can be directly overlaid and matched with DTI images, reducing computational complexity while maintaining high extraction efficiency.
Solution Approach 2:
The system performs all complex image processing and template preparation in advance during the preliminary action phase. By pre-computing and storing processed template images with annotated anatomical information, the actual nerve fiber tracking process becomes simpler and faster, as it only requires overlaying and matching pre-processed templates rather than performing complex real-time analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances the efficiency of nerve fiber extraction by automating the ROI determination process, improving the accuracy and speed of neural fiber tracking.
Implementation Method 1
DTI utilizes the diffusion of water molecules to reveal microscopic details about tissue architecture
Data Source
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AI summary
Methods and systems for image-based nerve fiber extraction. The methods may include obtaining an anatomical image of a subject and a diffusion image of the subject. The subject may include at least one region of interest (ROI) that relates to extraction of at least one target nerve fiber in the subject. The methods may further include determining, based on the anatomical image, the at least one ROI in the diffusion image; and extracting, from the diffusion image, at least one of the at least one target nerve fiber based on the at least one ROI.