CT Segmentation Masks for 4D Treatment Response Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional predictive models for therapeutic agent response using 3D CT scans lack anatomical context, leading to poor convergence and limited predictive performance.
Innovation Solution
A preprocessing stage generates volumetric segmentation (VS) masks from 3D CT scans, combining them with the CT scans to form 4D images for input into deep learning models, enhancing training efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional predictive models use only 3D CT scans without segmentation, then the input data is simpler and processing is faster, but the models lack anatomical context leading to poor convergence and limited predictive performance
Solution Approach 1:
The patent applies segmentation by dividing the 3D CT scan into multiple volumetric segmentation masks, each representing different anatomical structures or tissue types. This segmentation provides the deep learning model with anatomical context while maintaining structured data organization, thereby improving predictive performance without overwhelming complexity
Solution Approach 2:
The patent implements preliminary action by performing volumetric segmentation and generating anatomical masks before feeding data into the deep learning model. This preprocessing step prepares the data in advance with embedded anatomical context, enabling better model convergence and performance during the actual prediction phase
2Productivity
If volumetric segmentation masks are generated and combined with CT scans to form 4D images, then training efficiency and accuracy improve, but the preprocessing complexity and computational load increase
Solution Approach 1:
The patent merges the original 3D CT scan data with volumetric segmentation masks to create a unified 4D image structure. This combining integrates anatomical context directly into the input data format, allowing the deep learning model to process both structural and segmentation information simultaneously, thereby improving training efficiency
Solution Approach 2:
The patent transitions from 3D CT scans to 4D images by adding a temporal or channel dimension that incorporates segmentation mask information. This dimensionality change enables the model to utilize both raw imaging data and processed segmentation data in a unified framework, enhancing training efficiency and predictive accuracy
Data Source
AI summary
A system and method of automated segmentation of computed tomography (CT) imaging for predictive modeling of therapeutic agent response using deep learning analysis. The method includes acquiring a single CT scan of one or more regions of a patient. The method includes segmenting the single CT scan to generate one or more volumetric segmentation (VS) masks. The method includes combining the single CT scan and the one or more VS masks to generate a 4D image. The method includes providing the 4D image to one or more predictive models trained to predict therapeutic agent responses based on the 4D image. The method includes generating, by a processing device, a predicted treatment response score to a treatment for the patient based on the 4D image and the one or more predictive models.