Joint Deep Neural Network Training for Radiotherapy OAR Contouring
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Solution Overview
Problem
The challenge in radiotherapy treatment planning is the time-consuming and variable nature of manual contouring of organs-at-risk (OARs), which is compounded by the need for large training datasets for accurate machine learning models, leading to high costs and inefficiencies.
Innovation Solution
Joint training of multiple machine learning models across clinical datasets to leverage commonalities and reduce the required training data per dataset, enabling more performant automatic contouring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual contouring is used to delineate target tumors and organs-at-risk, then accuracy can be maintained, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical contouring process with an automated machine learning system. Deep neural networks are trained on labeled training data to automatically delineate target tumors and organs-at-risk on medical images, eliminating the need for manual tracing while maintaining clinical accuracy. The system uses automated algorithms to identify and segment anatomical structures, transforming a labor-intensive manual task into an efficient computational process.
2Reliability
If separate machine learning models are trained for each clinical dataset, then model specificity is improved, but the amount of training data required increases significantly
Solution Approach 1:
The patent combines multiple clinical datasets into a unified training framework. Instead of training separate models for each dataset, the system merges data from multiple sources to create a comprehensive training set. This approach allows the model to learn from diverse anatomical variations and imaging protocols while reducing the per-dataset data requirement. The merged training data enables the model to generalize across different clinical scenarios with smaller individual dataset contributions.
Solution Approach 2:
The patent creates a universal machine learning model that can handle multiple clinical datasets and imaging types. The trained model is designed to be multi-functional, capable of delineating various anatomical structures across different datasets and imaging modalities. This universal model eliminates the need for separate specialized models for each dataset, reducing overall training data requirements while maintaining adaptability to different clinical scenarios through joint training on diverse data.
3Adaptability or versatility
If multiple separate machine learning models are trained for different datasets, then model versatility is improved, but the overall system complexity increases
Solution Approach 1:
The patent implements a universal model architecture that can process multiple clinical datasets through a single unified system. The model is designed with multi-functionality, capable of handling different imaging modalities and anatomical variations without requiring separate specialized models. This universal approach maintains versatility across datasets while significantly reducing system complexity compared to managing multiple separate models.
Solution Approach 2:
The patent segments the training process into distinct phases while maintaining a unified model architecture. The system divides training data into different datasets for organized processing, but integrates them through a single model framework. This segmentation of the training approach allows the system to handle diverse datasets systematically while avoiding the complexity of maintaining multiple separate models, as each dataset contributes to training the same universal model.
Data Source
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AI summary
Joint training techniques to train multiple models across clinical datasets for automatic contouring. Rather than using separate deep neural networks that are trained independently for each different dataset (e.g., a different image contrast or anatomy), joint training can be used to train multiple models simultaneously across clinical datasets for automatic contouring. By taking advantage of commonalities between two or more datasets, the techniques effectively take advantage of data that would otherwise be considered irrelevant to the task - allowing the user to train more performant models while requiring less training data per dataset.