Cloud Segmentation Model Training for Clinic-Specific Generalization
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Solution Overview
Problem
Existing deep learning segmentation models for medical imaging in radiotherapy are not guaranteed to generalize across different clinics due to varying contouring practices and patient data, leading to suboptimal contouring and inaccurate treatment plans.
Innovation Solution
A cloud-based scalable training system that allows clients to train and apply segmentation models using their own datasets, utilizing a computing infrastructure with scalable computing resources and a web-based interface for data upload, processing, model deployment, and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a segmentation DNN model is trained on a dataset generated at one clinic, then the model can be automatically applied to patient data, but the model does not guarantee to generalize to other datasets from different clinics or geographic regions
Solution Approach 1:
The system allows retraining of the segmentation model with different training datasets from various clinics, changing the data parameters and distributions the model learns from. This enables the model to adapt to different contouring practices and anatomical variations across geographic regions, improving generalization while maintaining automatic segmentation capabilities
Solution Approach 2:
The system provides dynamic model updating capabilities where the segmentation model can be retrained and updated with new clinic-specific datasets. This creates a flexible system that can adapt to different clinical environments rather than being static, allowing the model to evolve and improve its performance across diverse patient populations and contouring practices
2Measurement precision
If manual contouring is performed to ensure accurate anatomical structure delineation, then the segmentation accuracy is improved, but the radiotherapy planning process becomes time-consuming
Solution Approach 1:
The system implements feedback mechanisms where automated segmentation results can be reviewed and corrected by clinicians, and these corrections are used to refine and retrain the model. This feedback loop allows the system to maintain high accuracy while reducing the time required for manual contouring, as the model learns from actual clinical corrections and improves over time
Solution Approach 2:
The segmentation model performs self-improvement by automatically learning from training datasets and clinical feedback without requiring complete manual re-contouring. The system serves itself by using accumulated data to enhance its own performance, reducing the need for time-consuming manual interventions while maintaining or improving contouring accuracy
Data Source
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AI summary
A system for training a segmentation model, comprising: an interface configured to allow a user to: upload and store training data in a storage device of a cloud-based network; provide access to the training data stored in the storage device; initiate a request for training a segmentation model; monitor the training of the segmentation model; and download the trained segmentation model; and a computing infrastructure configured to: pre-process the training data using a first set of computing resources of the cloud-based network to obtain processed training data, and store the processed training data in the storage device; deploy a training application on a second set of computing resources of the cloud-based network to train the segmentation model based on the processed training data; provide access to monitor the training; and provide access to the trained segmentation model.