Neural Network Classifier for Automated Organ Contour Evaluation
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Solution Overview
Problem
Current clinical practices for radiotherapy require manual and time-consuming evaluation of organ segmentations, which are prone to intra-observer and inter-observer variations, and lack automated guidance for quality control.
Innovation Solution
A dual-input machine learning neural network classifier is trained on image pairs of medical images and segmentation masks to automatically evaluate organ contours, providing visual indications of confidence levels and highlighting regions that require correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated segmentation methods are deployed to reduce delineation workload, then productivity is improved, but reliability deteriorates due to quality control issues and inability to detect critical inaccuracies
Solution Approach 1:
The system implements feedback by having the trained neural network classifier evaluate generated segmentations and provide confidence scores. The network feedbacks information about which regions are likely correct versus incorrect, enabling iterative refinement and quality control without manual review of entire segmentations.
Solution Approach 2:
The neural network classifier acts as an intermediary between the automated segmentation algorithm and the clinician. It receives generated segmentations, evaluates them, and provides localized feedback about problematic regions, mediating the quality control process and reducing the burden on clinicians while maintaining reliability.
2Reliability
If clinicians perform manual visual inspection of every region of every contour for quality control, then reliability is improved, but productivity deteriorates due to resource intensity
Solution Approach 1:
The system extracts only the problematic regions from complete segmentations using the trained neural network classifier. Instead of requiring clinicians to review every region, the system identifies and extracts only those regions with low confidence scores, allowing focused manual inspection where it is most needed.
Solution Approach 2:
The system applies partial action by having clinicians evaluate only specific problematic regions identified by the neural network rather than performing complete manual inspection of all contours. This partial evaluation approach maintains reliability for critical regions while significantly improving productivity.
3Productivity
If automated segmentation algorithms are used without localized evaluation, then productivity is improved, but measurement precision deteriorates due to lack of guidance on which regions need evaluation
Solution Approach 1:
The system applies local quality by providing different evaluation treatments to different regions of the segmentation. High-confidence regions are accepted with minimal review, while low-confidence regions receive detailed scrutiny. The neural network classifier identifies specific localized regions that require precise evaluation, maintaining both productivity and measurement precision.
Data Source
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AI summary
A localized evaluation network incorporates a discriminator (111a) acting as classifier, which may be included within a generative adversarial network, GAN (111). GAN (111) may include a generative network such as U-NET for creating segmentations. The localized evaluation network is trained on image pairs (220) including medical images of organs of interest and segmentation (mask) images. The network is trained to distinguish whether an image pair (220) does or does not represent the ground truth. GAN (111) examines interior layers of the discriminator (111a) and evaluates how much each localized image region contributes to the final classification. The discriminator (111a) may analyze regions of the image pair (220) that contribute to a classification by analyzing layer weights of the machine learning model.