Uncertainty Quantification for Medical Image Segmentation Masks
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Solution Overview
Problem
Conventional medical image processing methods struggle to distinguish between confident and uncertain segmentation masks, leading to inefficiencies in anatomical landmark positioning and measurement workflows, as users must manually verify automated placements.
Innovation Solution
An image processing system that generates uncertainty maps by producing multiple augmented versions of medical images, determining segmentation mask uncertainty, and visually indicating uncertain caliper positions to prompt user confirmation or adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine learning models are used to segment medical images, then segmentation automation is improved, but uncertainty quantification capability deteriorates
Solution Approach 1:
The patent generates multiple synthetic copies of the input medical image by applying various augmentations (rotation, flipping, scaling, etc.) and feeds them to the trained segmentation model to produce multiple segmentation masks. By analyzing the variations between these synthetic copies, the system quantifies uncertainty without requiring additional real data, thus maintaining automation while improving reliability assessment.
Solution Approach 2:
The patent introduces an uncertainty map as an intermediary component that visualizes and quantifies segmentation uncertainty pixel-by-pixel. This intermediary layer bridges the gap between the automated segmentation output and the need for uncertainty assessment, allowing users to evaluate confidence levels without manual re-segmentation.
2Speed
If conventional segmentation methods are used, then processing speed is maintained, but manual verification requirement increases
Solution Approach 1:
The patent implements a feedback mechanism where the uncertainty map is generated and displayed alongside the segmentation result, providing real-time information about the confidence levels of different regions. This feedback allows users to quickly identify which areas require manual verification, reducing unnecessary manual checking of high-confidence regions and saving time.
Solution Approach 2:
The patent applies local quality analysis by generating uncertainty maps that vary spatially across the image, with different uncertainty levels in different regions. This allows selective manual verification only in high-uncertainty areas rather than uniformly reviewing the entire segmentation, optimizing the balance between processing speed and verification thoroughness.
3Reliability
If uncertainty maps are generated from multiple augmented images, then segmentation reliability is improved, but computational complexity increases
Solution Approach 1:
The patent applies a limited set of augmentations (rotation, flipping, scaling) rather than exhaustively trying all possible transformations. This partial action approach generates sufficient synthetic images to quantify uncertainty effectively while keeping computational resources manageable, avoiding the excessive complexity of complete transformation enumeration.
Solution Approach 2:
Instead of processing multiple different real images, the patent creates synthetic copies of a single input image through augmentations. This copying approach reduces the need to store and process multiple original images, lowering computational and storage requirements while still achieving reliable uncertainty quantification through variation analysis of the synthetic copies.
Data Source
AI summary
Systems and methods are provided for quantifying uncertainty of segmentation mask predictions made by machine learning models, where the uncertainty may be used to streamline an anatomical measurement workflow by automatically identifying less certain caliper placements. In one example, the current disclosure teaches receiving an image including a region of interest, determining a segmentation mask for the region of interest using a trained machine learning model, placing a caliper at a position within the image based on the segmentation mask, determining an uncertainty of the position of the caliper, and responding to the uncertainty of the position of the caliper being greater than a pre-determined threshold by displaying a visual indication of the position of the caliper via a display device and prompting a user to confirm or edit the position of the caliper.