Probabilistic Anatomical Segmentation With Boundary Uncertainty Mapping
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Solution Overview
Problem
Existing medical image segmentation methods struggle with accurately determining the statistical confidence and uncertainty of segmentation boundaries, leading to ambiguous and potentially inaccurate anatomical structure delineations, especially due to noise and image artifacts.
Innovation Solution
A probabilistic segmentation approach is implemented, involving a training phase to generate probability density functions for segmentation boundaries, followed by a testing phase to predict patient-specific segmentation boundaries with associated uncertainty, using machine learning techniques like mixture models and neural networks to estimate and visualize confidence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learning based system is used to automate segmentation, then segmentation accuracy is improved, but the system cannot provide statistical confidence or uncertainty information for the segmentation boundaries
Solution Approach 1:
The patent transforms the segmentation output from a single deterministic boundary to a probability distribution over multiple possible boundaries. This parameter change allows the system to provide both the most likely segmentation boundary and the statistical confidence/uncertainty associated with it, resolving the contradiction between accuracy and information loss.
Solution Approach 2:
The patent introduces dynamic probability distributions that adapt to the specific image and anatomical structure being segmented. By using learned probability distributions that can vary across different regions and images, the system provides context-dependent confidence information while maintaining high segmentation accuracy through the learned model.
2Reliability
If multiple plausible boundary locations exist due to ambiguous image data, then segmentation reliability deteriorates, but providing all alternatives increases device complexity
Solution Approach 1:
The patent segments the probability distribution into discrete boundary locations with associated probabilities. Instead of presenting all possible boundaries equally, the system segments them into ranked alternatives, allowing users to see the most plausible boundaries first while understanding the uncertainty through the probability values, thus managing complexity while maintaining reliability.
Solution Approach 2:
The patent applies different levels of detail and probability information locally at different boundary locations. Regions with high confidence receive simpler treatment, while ambiguous regions provide more detailed probability distributions and alternative boundaries, optimizing the balance between reliability and complexity based on local image characteristics.
Data Source
AI summary
Systems and methods are disclosed for performing probabilistic segmentation in anatomical image analysis, using a computer system. One method includes receiving a plurality of images of an anatomical structure; receiving one or more geometric labels of the anatomical structure; generating a parametrized representation of the anatomical structure based on the one or more geometric labels and the received plurality of images; mapping a region of the parameterized representation to a geometric parameter of the anatomical structure; receiving an image of a patient's anatomy; and generating a probability distribution for a patient-specific segmentation boundary of the patient's anatomy, based on the mapping of the region of the parameterized representation of the anatomical structure to the geometric parameter of the anatomical structure.