Anatomical Image Segmentation Confidence Maps from Node Variations
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Solution Overview
Problem
Existing model-based segmentation methods in medical imaging yield a single result without quantifying the confidence of the outcome, particularly in imaging modalities with low signal-to-noise ratios or image artifacts, making it difficult to assess the accuracy of anatomical structure segmentation.
Innovation Solution
A method to generate a confidence map by processing an image through multiple transformations and segmentation operations, determining confidence values for each node of an anatomical structure model, and generating a map based on these values to quantify segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single model-based segmentation result is generated, then the segmentation process is simple and fast, but the confidence or accuracy of the result cannot be assessed
Solution Approach 1:
The patent applies segmentation by dividing the evaluation process into multiple independent segmentation runs with different transformations. Each segmentation produces a separate result, and the variation among these segmented results quantifies the confidence assessment. This allows accuracy evaluation without requiring a fundamentally different or more complex segmentation algorithm.
2Reliability
If multiple segmentation results are generated through transformations and perturbations, then confidence assessment becomes possible, but the processing time and computational complexity increase
Solution Approach 1:
The patent implements partial action by generating a limited number of segmented models (e.g., 10-50 perturbations) rather than exhaustive sampling. This provides sufficient confidence assessment without requiring excessive computational resources or time. The method balances reliability with practical processing constraints by using a moderate number of perturbations.
Solution Approach 2:
The patent applies parameter changes by introducing small random transformations (scaling, rotation, translation) and perturbations to the segmentation model parameters. These parameter variations enable confidence assessment through multiple segmented models while keeping each individual transformation simple and computationally efficient.
3Adaptability or versatility
If the segmentation model is trained on all image acquisition protocols, then the model becomes more versatile, but it becomes difficult to train equally well on all protocols
Solution Approach 1:
The patent applies local quality by allowing the segmentation model to specialize in specific image acquisition protocols rather than attempting universal performance. The confidence assessment through perturbations identifies where the model is less certain, enabling protocol-specific optimization while maintaining overall versatility.
4Loss of information
If confidence values are calculated for each node and a confidence map is generated, then users can identify low-confidence areas, but additional processing steps are required
Solution Approach 1:
The patent implements self-service by having the segmentation process automatically generate confidence information through perturbation-based sampling. The confidence map is produced as a byproduct of the same segmentation framework without requiring separate manual assessment or additional complex processing steps.
Data Source
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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.