AI Segmentation of CT Images for Radiation Planning
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Solution Overview
Problem
Current methods for segmenting normal organs and tumors in radiation treatment planning are inefficient and inaccurate due to reliance on human empirical judgment, leading to high variability and time consumption.
Innovation Solution
A system and method utilizing a deep learning algorithm to segment areas of interest in CT images, employing data augmentation and a global convolutional network structure to generate accurate segmentation maps, reducing human intervention and increasing automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human empirical judgment is used for organ segmentation, then segmentation accuracy is maintained at clinician level, but processing time increases substantially and variability increases
Solution Approach 1:
The patent replaces the mechanical system of human clinician judgment with an artificial intelligence-based automated segmentation system. The AI model processes CT images to automatically identify and segment organs and tumors, eliminating the need for manual annotation while achieving comparable or superior accuracy and significantly reducing processing time.
Solution Approach 2:
The patent creates a digital copy of the clinician's segmentation expertise through training AI models on annotated medical images. The learned models replicate the segmentation capabilities of experienced clinicians without requiring their continuous manual intervention, enabling automated reproduction of high-quality segmentation results.
2Extent of automation
If conventional rule-based segmentation methods are used, then automation is achieved, but segmentation accuracy becomes significantly low compared to clinician judgment
Solution Approach 1:
The patent replaces conventional rule-based mechanical systems with a data-driven AI learning system. Instead of relying on pre-programmed rules that fail to capture medical complexity, the system learns segmentation patterns directly from annotated medical images, achieving both automation and high accuracy simultaneously.
Solution Approach 2:
The patent changes the fundamental parameter of segmentation approach from rule-based logic to data-based learning. By training models on large datasets of annotated images, the system captures complex anatomical variations and segmentation criteria that rule-based systems cannot encode, thereby improving accuracy while maintaining automation.
3Productivity
If deep learning algorithm is used for segmentation, then processing speed increases and automation is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary training of the AI model using a comprehensive dataset of annotated medical images before actual segmentation tasks. This pre-training phase captures the complexity of anatomical structures and segmentation criteria, allowing the model to handle complex cases during deployment without requiring real-time complex computations or manual intervention.
Solution Approach 2:
The patent encapsulates the complexity of expert medical judgment in a pre-trained AI model that can be deployed as a standalone segmentation system. Once trained, the model processes new images efficiently without requiring the complex reasoning processes of human clinicians, thereby maintaining high processing speed while managing system complexity through the pre-computed knowledge embedded in the model weights.
Data Source
AI summary
The present disclosure relates to a system and method for segmenting a normal organ and/or tumor structure based on artificial intelligence for radiation treatment planning. The system may include a data collection unit configured to collect a radiotherapy structure (RT-structure) file including a computerized tomography (CT) image of a patient and contour information of an area of interest for radiation treatment, a pre-processing unit configured to extract the contour information from the RT-structure file and generate a binary image based on the extracted contour information, and a model training unit configured to learn parameters for generating a segmentation map indicative of the area of interest using a deep learning algorithm, based on the binary image, and generate a trained model based on the learnt parameter.