Medical Image Segmentation Confidence Mapping for Anatomical Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing model-based segmentation methods in medical imaging, particularly for modalities with low signal-to-noise ratios or image artifacts, provide a single result with unknown confidence, making accurate anatomical structure delineation challenging.
Innovation Solution
A method to determine confidence values for image segmentation by generating multiple segmentation outputs and calculating confidence maps based on variance analysis, incorporating transformations and machine learning algorithms to predict confidence values for each node of the anatomical structure model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based segmentation is used to delineate anatomical structures, then the anatomical structures can be segmented from medical images, but the confidence of the segmentation result remains unknown
Solution Approach 1:
The patent segments the segmentation process into multiple independent trials, where each trial produces a separate segmentation result. By analyzing the variability across these segmented results, the system quantifies confidence without requiring additional imaging data
Solution Approach 2:
The system implements feedback by using the variability information from multiple segmentation trials to adjust and refine the confidence assessment. The confidence map is generated based on the consistency of results across trials, providing continuous feedback on segmentation reliability
2Shape
If segmentation is performed on images with low signal-to-noise ratios or artifacts, then anatomical structures can be visualized, but the reliability of segmentation becomes uncertain
Solution Approach 1:
The patent performs preliminary actions by conducting multiple segmentation trials before finalizing the result. This preliminary multi-trial approach allows the system to assess reliability under challenging imaging conditions before presenting the final segmentation to the user
Solution Approach 2:
The system changes parameters by varying segmentation conditions across multiple trials, potentially adjusting model parameters, initialization conditions, or processing parameters to observe how segmentation results vary under different conditions, thereby assessing reliability
3Reliability
If multiple segmentation outputs are generated to assess confidence, then confidence values can be determined, but computational complexity increases
Solution Approach 1:
The patent applies partial action by generating a limited number of segmentation trials (e.g., 10-100 trials) rather than exhaustive sampling. This partial sampling provides sufficient confidence assessment while avoiding excessive computational burden that would result from generating too many trials
Data Source
AI summary
The invention provides a method for determining a confidence value for an image segmentation. The method includes obtaining an image, wherein the image comprises a view of an anatomical structure and a model of the anatomical structure is obtained, wherein the model comprises a plurality of nodes. The image is processed to generate a plurality of image segmentation outputs, wherein each image segmentation output comprises a set of values for the view, wherein each value of the set of values is associated with a node of the plurality of nodes of the model. For each node of the model, a confidence value is determined based on the plurality of values corresponding to the node. A confidence map of the anatomical structure is generated based on the confidence value of each node.