Organ Segmentation Quality Assurance With Multi-Channel Reconstruction
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Solution Overview
Problem
Deep learning-based medical image segmentation models often produce inaccurate results due to inter-patient variability, improper imaging protocols, and anatomical structure complexity, necessitating a method to assess segmentation quality before clinical application.
Innovation Solution
A deep learning framework that uses a multi-channel reconstruction model to generate reconstructed versions of segmentation masks, comparing them to ground truth masks to determine quality and identify errors, with a system to regulate the use of auto-segmentation masks based on quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning-based segmentation models are used to automatically segment anatomical structures, then segmentation speed and productivity are improved, but segmentation accuracy and reliability deteriorate due to inter-patient variability and anatomical complexity
Solution Approach 1:
The patent implements a quality assessment component that provides feedback on segmentation mask quality by comparing reconstructed versions against ground truth masks. This feedback mechanism identifies inaccurate segmentations and prevents their use in clinical applications, thereby maintaining reliability while preserving the speed benefits of automated deep learning segmentation.
2Productivity
If automated segmentation models are applied to clinical applications, then workflow efficiency is improved, but the risk of using inaccurate segmentation results increases
Solution Approach 1:
The patent performs preliminary quality assessment of segmentation masks before they are used in clinical workflows. The quality assessment component evaluates each mask against ground truth and generates quality scores that determine whether masks are suitable for clinical use, preventing harmful inaccurate segmentations from entering the clinical workflow while maintaining efficient automated processing.
3Reliability
If a quality assessment system is added to evaluate segmentation masks, then segmentation quality and reliability are improved, but system complexity increases
Solution Approach 1:
The patent uses a reconstruction model that creates a simplified copy or representation of the segmentation mask quality assessment process. Instead of requiring complex manual verification systems, the reconstruction model generates synthesized quality assessments by transforming segmentation masks through a learned representation, reducing system complexity while maintaining high reliability.
4Loss of time
If segmentation masks are used for clinical applications without quality verification, then processing time is reduced, but the number of errors increases
Solution Approach 1:
The patent implements a feedback mechanism where the quality assessment component continuously monitors segmentation mask quality and provides real-time feedback on errors. This allows the system to quickly identify and correct inaccurate segmentations without significant time loss, maintaining high reliability while minimizing processing time through automated feedback-driven error detection.
Data Source
AI summary
A deep-learning based framework to assess the quality of medical image auto-segmentation is described. According to an example, a computer-implemented method comprises receiving segmentation masks generated, via one or more segmentation models, from medical image data depicting an anatomical region of a subject, wherein each of the segmentation masks depicts a different anatomical structure of a set of different anatomical structures included in the anatomical region. The method further comprises generating reconstructed versions of the segmentation masks based on application of a multi-channel reconstruction model to the segmentation masks, wherein the reconstructed versions correspond to optimized versions of the segmentation masks. The method further comprises determining an assessment of quality of the segmentation masks based on comparison of the segmentation masks to the reconstructed versions, generating output data regarding the assessment of quality, and rendering the output data via an electronic output device.