CT Tissue Segmentation Network for Automated Body Composition
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Solution Overview
Problem
Current imaging-based methods for quantifying body tissue composition, particularly on whole-body images, are impractical due to the need for human interaction and lack of accuracy comparable to interactive methods, with existing automated methods focusing on specific slices or tissue types and not achieving sufficient precision.
Innovation Solution
A convolutional neural network, named ABCNet, is developed to automatically segment body tissues from body-torso-wide low-dose CT images, utilizing a residual Encode-Decode architecture with Bottleneck and Dense Block designs, dynamic soft Dice loss, and efficient memory techniques to achieve accurate and efficient segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated methods are used to quantify body tissue composition on whole-body images, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces manual interactive segmentation methods with a convolutional neural network (ABCNet) that automatically segments body tissues from CT images. The neural network substitutes the mechanical interaction of manual drawing and region-of-interest selection with an automated deep learning system that processes entire body-torso-wide images to quantify adipose tissue, muscle tissue, and skeletal tissue without human intervention.
Solution Approach 2:
The patent uses a convolutional neural network to create a computational model that copies and replicates the expertise of manual segmentation. The network is trained on annotated images and learns to reproduce accurate tissue segmentation results that previously required skilled human radiologists, thereby maintaining measurement precision while achieving full automation.
2Device complexity
If existing automated methods focus on specific slices or tissue types, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent implements a universal convolutional neural network architecture that can segment multiple tissue types (adipose tissue, muscle tissue, skeletal tissue) across multiple body regions simultaneously. The ABCNet model is designed to process entire body-torso-wide CT images and automatically identify and quantify all relevant tissues in one operation, eliminating the need for separate methods for different tissues or slices.
Solution Approach 2:
The patent divides the complex task of whole-body tissue quantification into manageable segments through the convolutional neural network's layered architecture. The network processes images through multiple convolutional layers that progressively segment and identify different tissue types, allowing comprehensive analysis while maintaining computational efficiency through hierarchical processing.
3Measurement precision
If interactive manual methods are used for tissue quantification, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The patent replaces the time-consuming manual interactive segmentation process with an automated convolutional neural network that processes CT images rapidly. Instead of requiring radiologists to manually draw contours and define regions of interest for each tissue type, the ABCNet automatically segments all tissues in seconds, dramatically increasing throughput while maintaining precision through deep learning.
Solution Approach 2:
The convolutional neural network performs self-service by automatically segmenting and quantifying all body tissues without requiring human intervention. The system processes entire body-torso-wide images independently, making it suitable for high-volume screening and research applications where rapid analysis of numerous subjects is required.
Data Source
AI summary
Methods and systems are described for determining body composition information. An example method can comprise receiving imaging data associated with a patient, causing the imaging data to be input into a convolutional neural network stored on one or more computing devices, determining, based on output data resulting from inputting the imaging data into the convolutional neural network, body composition information, and causing output of the body composition information.