Graph Surface Segmentation With Topology-Constrained Deep Learning
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Solution Overview
Problem
Existing deep learning-based methods for medical image analysis lack topology guarantees for segmented objects, requiring post-processing to infer boundary surfaces and are inefficient with scarce training data.
Innovation Solution
A surface-based U-net backbone with a learnable surface smoothing block for end-to-end training, incorporating surface cost functions and deep learning to enforce geometric constraints and mutual surface interactions, reducing the need for post-processing and training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If classification or region based deep learning methods are used to predict probability for each voxel, then segmentation can be performed, but topology guarantee for segmented objects is lacking and post processing is needed
Solution Approach 1:
The patent introduces a level set function as an intermediary between the deep learning probability predictions and the final segmented objects. This level set function serves as a mediator that enforces topological constraints and guarantees while maintaining the automation benefits of deep learning. The level set function transforms the discrete voxel probabilities into continuous surface representations with guaranteed topological properties.
2Measurement precision
If more training data is provided to deep learning methods, then segmentation accuracy may improve, but the requirement for training data increases
Solution Approach 1:
The patent changes the fundamental parameters of the segmentation approach by transitioning from direct voxel-wise classification to level set function optimization. This parameter change allows the system to achieve high segmentation accuracy with less training data by leveraging the mathematical properties of level set functions and their ability to enforce topological constraints, reducing reliance on large annotated datasets.
3Reliability
If post processing is added to infer boundary surfaces, then topology can be guaranteed, but the complexity of the workflow increases
Solution Approach 1:
The patent merges the deep learning-based probability prediction with the level set function optimization into a unified framework. By combining these two approaches, the system simultaneously achieves automated segmentation with topology guarantees without requiring separate post-processing steps. The level set function is optimized directly from the deep learning predictions, integrating multiple functions into a single coherent workflow.
4Measurement precision
If sub-pixel segmentation accuracy is achieved, then segmentation precision improves, but the computational complexity increases
Solution Approach 1:
The patent transitions from discrete voxel-based segmentation to continuous level set function optimization, effectively moving from a discrete dimension to a continuous dimension. This dimensionality change enables sub-pixel segmentation accuracy by allowing surface positions to be defined continuously rather than at discrete voxel boundaries, achieving higher precision without proportionally increasing computational complexity.
Data Source
AI summary
An automated method for segmentation includes steps of receiving at a computing device an input image representing at least one surface and performing by the computing device image segmentation on the input image based on a graph surface segmentation model with deep learning. The deep learning may be used to parameterize the graph surface segmentation model.