Deep Neural Network for Automatic Spine Image Labeling
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Solution Overview
Problem
Current methods for labeling spine images, especially those with anatomical or pathological variations such as scoliosis, fused vertebrae, and spine implants, are inaccurate due to the limitations of semi-automatic annotation tools.
Innovation Solution
A deep neural network system that analyzes input spine images using a training dataset to generate mask images and associate labels with vertebral candidates, enabling accurate automatic labeling of spine images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semi-automatic annotation tools are used with manual marking by radiologists or technicians, then the labeling process requires less automation complexity, but the labeling accuracy deteriorates when anatomical or pathological variations are present
Solution Approach 1:
The system performs automatic spine labeling without requiring manual intervention. The deep neural network independently processes spine images, generates mask images, and produces labels for vertebral bodies, inter-vertebral discs, and other structures, making the system self-sufficient and eliminating dependency on radiologist or technician input
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated deep learning system. The neural network architecture substitutes human expertise and manual marking operations with algorithmic image processing, using convolutional layers, pooling operations, and activation functions to achieve accurate labeling
2Productivity
If manual annotation by radiologists is performed, then the labeling can be reviewed and corrected, but the annotation time increases significantly
Solution Approach 1:
The system performs complete automatic labeling without human intervention, processing spine images through the deep neural network to generate labels for all vertebral structures independently, eliminating the time-consuming manual annotation and review process
Solution Approach 2:
The system performs all labeling operations in advance without waiting for manual review. The deep neural network generates mask images and extracts labels for multiple structures simultaneously, providing immediate results that would otherwise require sequential manual processing
3Reliability
If annotation tools assume standard spine anatomy, then the processing is simpler, but the reliability fails when anatomical or pathological variations such as scoliosis, fused vertebrae, or spine implants are present
Solution Approach 1:
The deep neural network dynamically adjusts its processing based on the specific characteristics of each spine image. The model learns to recognize and adapt to various anatomical configurations including scoliosis, fused vertebrae, and implant presence by processing raw image data without assuming standard anatomy, allowing parameters like vertebral boundaries and disc locations to be determined based on actual image features rather than predefined templates
Data Source
AI summary
A method and system for automatically labeling a spine image is disclosed. The method includes receiving an input spine image and analyzing image features of the input spine image by a deep neural network. The method further includes generating a mask image corresponding to the input spine image by the deep neural network based on image characteristics of a training image dataset. A region of interest in the mask image comprises vertebral candidates of the spine. The training image dataset comprises a plurality of spine images and a plurality of corresponding mask images. The method further includes associating labels with a plurality of image components of the mask image and labeling the input spine image based on labels associated with the mask image.