Tubular Structure Recognition via Neural Centerline Extraction
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Solution Overview
Problem
Existing medical imaging techniques for analyzing tubular structures, such as blood vessels and nerves, are inefficient and inaccurate, requiring manual analysis of two-dimensional layers in three-dimensional images, which limits the ability to accurately view the full appearance of these structures.
Innovation Solution
A system utilizing a trained neural network model to determine the centerline of tubular structures in medical images, employing a first recognition module for preliminary recognition and a recurrent neural network model for feature extraction, to automatically recognize and analyze tubular structures with improved efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of two-dimensional layers is used, then the doctor can view tubular structures, but the efficiency and accuracy are low
Solution Approach 1:
The patent replaces the manual mechanical process of layer-by-layer viewing with an automated neural network system. The neural network automatically processes three-dimensional image data to extract centerlines and recognize tubular structures, eliminating the need for manual intervention and significantly improving analysis efficiency while maintaining high accuracy.
Solution Approach 2:
The patent creates a digital representation (centerline) of the tubular structure from the three-dimensional image data. This centerline serves as a simplified copy that captures the essential geometric information, allowing for efficient automated recognition and analysis without requiring the doctor to manually process every layer of the original image.
2Measurement precision
If manual analysis is used, then the doctor can identify tubular structures, but the accuracy is insufficient for full appearance analysis
Solution Approach 1:
The patent substitutes manual visual analysis with an automated neural network system that processes three-dimensional image data to generate precise centerlines and recognition results. The neural network's ability to automatically extract and analyze geometric features provides high measurement precision without the time cost of manual layer-by-layer examination.
Solution Approach 2:
The patent performs preliminary extraction of centerline information from the three-dimensional image data before final recognition. This preliminary action of generating centerlines from image data enables subsequent accurate recognition of tubular structures, preparing the essential geometric representation in advance for high-precision analysis.
3Measurement precision
If the doctor views full appearance of tubular structures, then analysis accuracy improves, but the process becomes more time-consuming
Solution Approach 1:
The patent extracts the essential geometric information (centerline) from the complex three-dimensional image data. This extraction creates a simplified representation that captures the full appearance characteristics of tubular structures, enabling accurate analysis while significantly reducing the time required compared to manually viewing all original image layers.
Solution Approach 2:
The patent creates a centerline copy that represents the full appearance of tubular structures in a condensed format. This centerline copy contains all necessary geometric information for accurate analysis but presents it in a way that can be processed instantly by the neural network, eliminating the time-consuming manual review process while maintaining analytical accuracy.
Data Source
AI summary
The present disclosure relates to systems and methods for object recognition. The systems may obtain image data captured by an imaging device. The image data may include one or more objects. The systems may determine a centerline of a target object in the one or more objects based on the image data. The systems may determine a recognition result of the target object using a trained neural network model based on at least one feature parameter of the centerline of the target object. The recognition result may include a name of the target object. The systems may perform an anomaly detection on the target object based on the recognition result of the target object.