Neural Network Bone Segmentation for Multi-Density Imaging
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Solution Overview
Problem
Current methods for patient-specific treatment planning struggle to accurately identify and depict bones of different density values, and lack a system for multi-thresholding and kinematics associated with the region of interest, leading to challenges in providing precise alignment and virtual measurements during surgical procedures.
Innovation Solution
A method involving the use of neural networks to analyze image files, extract density information, and create virtual versions of anatomical regions, allowing for segmentation, landmarking, and treatment planning, with the ability to generate heat maps and virtual surgery guides for precise surgical navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single predefined threshold value is used for bone segmentation, then the segmentation process is simple and fast, but bones of different density values cannot be separated
Solution Approach 1:
The patent applies segmentation by dividing the bone segmentation process into multiple stages using different threshold values. The first threshold separates metal structures, the second threshold separates cortical bone, and the third threshold separates cancellous bone. This multi-level segmentation approach enables differentiation of bones with different density values while maintaining systematic efficiency.
Solution Approach 2:
The patent applies local quality by using different threshold values for different regions and density levels within the bone structure. Each threshold is optimized for specific density ranges: the first threshold for metal structures, the second for cortical bone regions, and the third for cancellous bone regions. This allows precise differentiation of local bone properties.
2Productivity
If low resolution imaging is used, then the imaging process is faster and requires less computational resources, but the system cannot separate two fused bones
Solution Approach 1:
The patent applies preliminary action by performing multi-threshold segmentation and heat map generation before the actual surgical procedure. The system pre-processes the imaging data to create detailed density maps and separation visualizations, enabling fused bones to be distinguished and analyzed in advance, thus avoiding the need for high-resolution imaging during the surgical procedure itself.
3Measurement precision
If manual segmentation methods are used, then the system can handle complex bone structures, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent applies self-service by implementing an automated multi-threshold segmentation system that performs bone structure analysis without manual intervention. The system automatically applies multiple thresholds, generates heat maps, and creates 3D visualizations of bone densities, eliminating the need for time-consuming manual segmentation while maintaining high accuracy in handling complex bone structures.
Solution Approach 2:
The patent replaces manual mechanical segmentation processes with automated computational algorithms. The multi-threshold segmentation method uses computer-based image processing to automatically differentiate bone densities and structures, substituting the manual mechanical approach with an efficient digital system that maintains precision while dramatically reducing time requirements.
4Ease of operation
If no multi-thresholding system is implemented, then the system is simpler to operate, but precise alignment and virtual measurements cannot be provided during surgery
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing multiple threshold values and their corresponding heat map representations before the surgical procedure. The system prepares the multi-thresholding data structures and alignment parameters in advance, allowing surgeons to access pre-computed precision data during surgery without requiring complex real-time calculations, thus maintaining ease of operation while providing precise alignment capabilities.
Data Source
AI summary
Embodiments related to a method is described. The method comprises receiving an image file of a region of interest of an anatomy of a living organism, analyzing the image file and extracting image coordinates and density information of a plurality of points in the image file, training a neural network using collective information available in a database, registering the region of interest of the anatomy as a virtual version using an input from the neural network, and subsequently training the neural network using a user input from a user and the collective information available in the database. The collective information is recorded in the database with respect to a plurality of clusters of different physiological states of the living organism. The method further comprises performing a treatment on the region of interest. In an embodiment, the treatment is performed using the input from the neural network.