Anatomical Structure Segmentation Using Weighted Volumetric Patches
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Solution Overview
Problem
Existing image-guided surgery systems face challenges in efficiently and cost-effectively segmenting anatomical structures due to the time-consuming nature of manual segmentation methods, which hinders widespread adoption.
Innovation Solution
A method involving the use of a convolutional neural network for image segmentation, utilizing weighted volumetric patches and down-sampling and up-sampling filter processes to enhance the accuracy of anatomical structure segmentation, particularly focusing on balancing foreground and background classifications and emphasizing structural edges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation methods are used, then segmentation accuracy can be achieved, but the process becomes very time consuming
Solution Approach 1:
The patent segments the volumetric image data into multiple 2D slices for parallel processing. Each slice is processed independently through the convolutional neural network, enabling efficient computation while maintaining segmentation accuracy. This segmentation approach transforms a computationally intensive 3D problem into multiple manageable 2D problems that can be solved faster.
Solution Approach 2:
The patent replaces manual mechanical segmentation operations with an automated convolutional neural network system. The CNN automatically learns segmentation patterns from training data and applies them to new images, eliminating the need for time-consuming manual tracing while maintaining or improving segmentation accuracy through consistent application of learned features.
2Productivity
If automated segmentation methods are used, then processing speed is improved, but segmentation accuracy may deteriorate
Solution Approach 1:
The patent performs preliminary training of the convolutional neural network on labeled training data before actual segmentation. This preliminary learning phase enables the network to automatically capture anatomical features and segmentation patterns, so that during actual use, accurate segmentation is achieved rapidly without manual intervention. The preliminary action of training transfers knowledge that ensures both speed and accuracy.
Solution Approach 2:
The patent employs a feedback mechanism where the CNN processes images and generates segmentation outputs that can be evaluated and used to refine the model. The system learns from training data with known ground truth labels, adjusting its internal parameters to minimize errors. This feedback loop ensures that automated segmentation maintains high accuracy while providing rapid processing.
3Manufacturing precision
If detailed anatomical structures are segmented, then surgical guidance precision is improved, but computational complexity increases
Solution Approach 1:
The patent processes volumetric 3D medical images by slicing them into multiple 2D cross-sectional images. This dimensionality reduction transforms the complex 3D segmentation problem into multiple simpler 2D problems that are computationally more manageable. The CNN processes each 2D slice independently, reducing computational complexity while still capturing detailed anatomical structures when the 2D results are reconstructed into 3D guidance information.
Data Source
AI summary
A method for image segmentation comprises receiving volumetric image data for an anatomical region and generating a first volumetric patch from the volumetric image data. The method also comprises generating a second volumetric patch from the first volumetric patch by weighting a plurality of volumetric units in the first volumetric patch and receiving the second volumetric patch as an input to a convolutional neural network. The method also comprises conducting a down-sampling filter process and conducting an up-sampling filter process within the convolutional neural network.