3D Blood Vessel Segmentation via Machine Learning and Image Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for automatically segmenting blood vessels from three-dimensional medical image data are prone to errors and lack standardization, as they require operator intervention and are not fully automated, leading to inefficiencies and inaccuracies in diagnosis and treatment.
Innovation Solution
A method and system that combines machine learning and image processing algorithms to automatically segment blood vessels, using a convolutional neural network (CNN) or fully convolutional network (FCN) for initial segmentation and a region growing algorithm for correcting noise and missing data, resulting in fully automated and accurate three-dimensional blood vessel data generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual segmentation is performed by operators, then segmentation accuracy can be maintained through human judgment, but the process takes a long time and is prone to operator errors and lack of standardization
Solution Approach 1:
The patent segments the blood vessel extraction process into multiple processing stages: initial extraction, noise removal, and gap filling. This multi-stage automated pipeline replaces manual operator work while maintaining accuracy through systematic processing of different aspects of the segmentation task separately.
Solution Approach 2:
The patent replaces the mechanical human operator system with an automated computer-based image processing system. The manual judgment and segmentation operations are substituted with algorithmic processing that consistently applies the same rules without fatigue or variation, eliminating operator errors and standardizing the process.
2Productivity
If fully automated machine learning segmentation is used, then processing speed increases and operator intervention is eliminated, but noise and missing blood vessel regions occur due to insufficient or incorrectly labeled training data
Solution Approach 1:
The patent merges machine learning-based automated extraction with traditional image processing techniques (noise removal algorithm and gap filling algorithm). This combination allows the system to benefit from the speed of automated ML segmentation while compensating for its inaccuracies through subsequent processing stages that refine the results.
Solution Approach 2:
The patent implements a feedback mechanism where the output of each processing stage becomes the input for the next stage. The noise removal stage processes the output of the initial extraction, and the gap filling stage processes the output of noise removal. This sequential feedback loop allows errors to be progressively corrected while maintaining the overall automated workflow.
3Extent of automation
If traditional image processing algorithms are used, then some automation is achieved, but complete automation cannot be realized and operator intervention is still required for seed input and correction
Solution Approach 1:
The patent implements a self-service automated system where the computer system performs all segmentation operations without requiring operator input for seed points or corrections. The system automatically extracts blood vessels, removes noise, and fills gaps through its own internal algorithms, making the entire process independent of human intervention while maintaining ease of use through a simple interface.
Data Source
AI summary
A method for automatically segmenting three-dimensional blood vessel data from three-dimensional medical image data of a patient through the use of a computer is provided. The method includes: receiving the three-dimensional medical image data of the patient; generating three-dimensional shape machine-learning blood vessel data from the received three-dimensional medical image data through the use of a machine-learned segmentation program so as to generate three-dimensional blood vessel data; and generating corrected three-dimensional shape blood vessel data from the received three-dimensional medical image data and the generated three-dimensional shape machine-learning blood vessel data through the use of an image processing program.