Vertebra Positioning in CT Images Using 3D Heat Map Regression
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Solution Overview
Problem
Current methods for positioning vertebrae in CT images are complex, time-consuming, and inaccurate due to the complex structure of vertebrae, metal implants, scanning noise, and the need for clear shape recognition, which limits their applicability to abnormal pathological phenomena and requires extensive data training for convolutional neural networks.
Innovation Solution
A method involving pre-processing of CT image data using SimpleITK, inputting it into a pre-trained neural network with a high resolution ratio to generate 3D heat maps, and combining these with U-shaped convolutional neural networks and space information extraction to accurately position vertebrae, reducing the impact of scanning machine differences and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional convolutional neural network methods are used for vertebra positioning, then the system can process CT images, but it requires extensive data training and cannot accurately position vertebrae due to complex structures, metal implants, and scanning noise
Solution Approach 1:
The patent applies preliminary action by pre-processing the CT image data before inputting it into the neural network. The pre-processing module performs denoising, normalization, and other preparatory operations on the raw CT images to remove scanning noise and standardize the data format. This preliminary processing reduces the complexity of subsequent positioning tasks and eliminates the need for extensive training on noisy, unstandardized data.
Solution Approach 2:
The patent extracts key positional features from the complex CT images using a specially designed neural network architecture that focuses on extracting essential vertebra positioning information while filtering out irrelevant details such as metal implant artifacts and complex surrounding structures. This extraction approach achieves accurate positioning without requiring extensive training on all possible variations.
2Adaptability or versatility
If artificially-designed complex features are used for vertebra positioning, then the method can handle specific cases, but it is hard to apply to other abnormal pathological phenomena and requires extensive data training
Solution Approach 1:
The patent implements universality by designing a neural network architecture with multi-scale feature extraction capabilities that can handle various vertebra types and pathological conditions. The network uses multiple convolutional layers with different kernel sizes to capture both local detailed features and global structural information, making it adaptable to different pathological cases without requiring separate models or extensive retraining.
Solution Approach 2:
The patent applies dimensionality change by processing CT images in 3D space rather than traditional 2D slices. The neural network operates on volumetric data, enabling it to capture spatial relationships and structural features across multiple slices simultaneously. This 3D approach improves adaptability to various pathological conditions while reducing the need for extensive training data compared to 2D slice-by-slice processing.
3Reliability
If normal convolutional neural network methods are used, then the system can process images, but it requires a lot of data to be trained due to high correlation of vertebra, complex rear of vertebra, scanning machine difference, and scanning noise
Solution Approach 1:
The patent applies preliminary action by pre-processing the CT image data before inputting it into the neural network. The pre-processing module performs denoising, normalization, and other preparatory operations on the raw CT images to remove scanning noise and standardize the data format. This preliminary processing reduces the complexity of subsequent positioning tasks and eliminates the need for extensive training on noisy, unstandardized data.
Solution Approach 2:
The patent applies parameter changes by transforming the input CT image data through normalization and standardization operations. The pre-processing module adjusts intensity ranges, removes noise, and standardizes formatting across different scanning machines. These parameter transformations make the data more consistent and reduce the variability that would otherwise require extensive training data to compensate for.
Data Source
AI summary
The present disclosure provides a method of positioning vertebra in a CT image, an apparatus, a computer device, and a computer readable storage medium. The method includes: pre-processing vertebra CT image data; inputting the pre-processed vertebra CT image data into a pre-trained neural network to obtain regression results of heat maps of key points corresponding to the pre-processed vertebra CT image data; regressing of 3D heat maps corresponding to the positions of the key points of the vertebra mass center based on the regression results of the heat maps of the key points and the pre-processed vertebra CT image data; serving 3D heat maps corresponding to the positions of the key points of the vertebra mass center as labels, and networked regressing 3D heat map information to position the vertebra. Effects caused by scanning machine difference and scanning noise are avoided, and the vertebra with complex forms is accurately positioned.