Neural Image Segmentation for Accurate Implant Placement Identification
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Solution Overview
Problem
Existing imaging technologies face challenges in accurately identifying and confirming the placement of implants within subjects due to distortion and overlap in image data, especially when direct visual access is impractical or impossible.
Innovation Solution
A system and method utilizing a neural network for automatic segmentation of implants from image data, trained using simulated images generated from CAD models to enhance the identification and confirmation of implant placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is acquired to analyze implant placement, then the ability to confirm implant position is improved, but image distortion and overlap occur reducing identification accuracy
Solution Approach 1:
The patent applies segmentation by dividing the image data into distinct regions: implant portions and subject portions. The neural network automatically segments the image to separate the implant from surrounding anatomical structures, eliminating the distortion and overlap problems that plague traditional imaging methods. This allows for clear identification of implant placement without the confounding factors of image distortion.
Solution Approach 2:
The patent introduces a neural network as an intermediary between the raw image data and the final implant position analysis. This neural network intermediary processes the distorted and overlapping image data, transforming it into clear, segmented information that accurately reveals implant placement. The neural network acts as a mediator that converts problematic image data into useful diagnostic information.
2Ease of operation
If direct visual access is used to view implant placement, then real-time confirmation is improved, but access to internal portions becomes impossible due to subject anatomy
Solution Approach 1:
The patent replaces the mechanical approach of direct visual inspection with an automated neural network-based image analysis system. Instead of requiring physical access or direct viewing of the implant through incisions or body cavities, the system uses computational methods to automatically segment and identify implant positions from external imaging data, making the process both non-invasive and highly accurate.
Solution Approach 2:
The patent creates a processed copy or representation of the internal implant position through neural network segmentation of external image data. Rather than requiring direct viewing of the actual implant, the system generates a segmented image copy that clearly displays implant placement, effectively allowing visualization without physical access to the internal subject portions.
3Adaptability or versatility
If manual analysis of image data is performed, then flexibility in analysis is improved, but time consumption and productivity decrease
Solution Approach 1:
The patent implements self-service by enabling the neural network to automatically perform the entire implant verification process without requiring manual intervention. The system autonomously segments images, identifies implant portions, determines placement accuracy, and generates verification results. This automated self-service approach maintains the flexibility of comprehensive analysis while dramatically increasing productivity by eliminating time-consuming manual processes.
Solution Approach 2:
The patent applies preliminary action by pre-training the neural network with extensive training data before deployment. This preliminary training phase prepares the system to automatically perform accurate implant verification without requiring manual analysis during actual use. The preliminary action of training creates a ready-to-use system that combines analytical flexibility with high-speed automated operation.
Data Source
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AI summary
An image segmentation system and display is disclosed. The system may be operated or configured to generate a segmentation of a member from an image. The image and/or the segmentation may be displayed for viewing by a user.