Brain Tumor Image Registration and Segmentation for Radiotherapy Response
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Solution Overview
Problem
Current methods for tracking brain metastases after radiotherapy are manual, time-consuming, and prone to human error, lacking a comprehensive solution for automatically tracking and quantifying tumor responses in brain metastases across multiple imaging sessions.
Innovation Solution
A deep learning-based system, METRO, performs image registration, segmentation, and tracking of brain metastases using convolutional neural networks to generate longitudinal maps of tumor volumes, aligning images with treatment plans, and providing dose metrics, thereby automating the process of monitoring tumor responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual methods are used to track brain metastases after radiotherapy, then clinical expertise and judgment can be applied, but the process is time-consuming and prone to human error
Solution Approach 1:
The system enables automated self-tracking of brain metastases by using machine learning models to automatically segment tumors, register images, and calculate volumetric changes without requiring manual clinical intervention for each measurement step
Solution Approach 2:
The patent replaces manual mechanical measurement processes with automated computational systems, using deep learning algorithms to perform image segmentation, registration, and volumetric calculation that were previously done manually by clinicians
2Measurement precision
If manual tracking methods are used, then flexibility in clinical judgment is maintained, but measurement precision and consistency are reduced
Solution Approach 1:
The patent replaces manual measurement processes with automated computational systems, using deep learning algorithms to perform image segmentation, registration, and volumetric calculation that were previously done manually by clinicians
Solution Approach 2:
The system transforms complex medical imaging data into standardized volumetric parameters through automated processing, changing the state of raw image data into quantifiable tumor volume metrics that can be consistently compared over time
3Productivity
If comprehensive automated tracking is implemented, then time efficiency and measurement consistency are improved, but system complexity increases
Solution Approach 1:
The system divides the complex task of tumor tracking into distinct modular components: image registration module, tumor segmentation module, volumetric calculation module, and reporting module, allowing each to be optimized independently
Solution Approach 2:
The automated tracking system is designed to handle multiple imaging modalities and tumor types through a unified platform, making the complex system broadly applicable across different clinical scenarios
Data Source
AI summary
Presented herein are systems and methods of determining tumor responses in brains from administering radiotherapy. A computing system can: identify a plurality of biomedical images of a brain of a subject; perform an image registration on a first biomedical image with a second biomedical image to determine a plurality of translation parameters; generate a third biomedical image using the second biomedical image in accordance with the plurality of translation parameters, detect using an image segmentation model, (i) a first segment identifying a first region within the third biomedical image and (ii) a second segment identifying a second region within the second biomedical image; and determine a metric indicating a degree of responsiveness of a tumor in the subject to the administration of the radiotherapy to the brain, based on the first segment and the second segment.