Whole-Body 3D Image Segmentation Across Imaging Platforms
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Solution Overview
Problem
Existing medical imaging analysis processes rely heavily on radiologist interpretation, which can be subjective and time-consuming, and patients struggle to understand complex imaging results, necessitating improved automated analysis and communication of diagnoses and treatment recommendations.
Innovation Solution
Developed systems and methods for automated, machine learning-based 3D image analysis that identify specific anatomical regions and quantify radiopharmaceutical uptake across the entire body, using Convolutional Neural Networks (CNNs) to segment and map anatomical and functional images, enabling accurate detection and staging of cancerous lesions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radiologist interpretation is used for medical image analysis, then diagnostic accuracy can be maintained, but analysis time and subjectivity increase
Solution Approach 1:
The patent introduces an automated image analysis system as an intermediary between the medical images and the radiologist. This system pre-processes and segments anatomical regions automatically, providing structured results that assist rather than replace radiologist interpretation. The automated segmentation serves as a mediator that reduces the radiologist's workload while maintaining diagnostic accuracy through human-in-the-loop validation.
Solution Approach 2:
The patent implements self-service through automated segmentation algorithms that can independently identify and delineate anatomical regions without human intervention. The system performs initial image processing, region detection, and measurement calculations automatically, enabling the imaging system to serve itself for routine analysis tasks while freeing radiologists for complex diagnostic decisions.
2Productivity
If automated analysis systems are implemented, then analysis efficiency improves, but adaptability to different imaging platforms and protocols decreases
Solution Approach 1:
The patent achieves universality by designing the automated analysis system to handle multiple imaging modalities (CT, MRI, PET) and various anatomical regions through a unified platform. The system incorporates configurable parameters and adaptive algorithms that can be adjusted to accommodate different imaging protocols, scanners, and clinical applications, allowing one system to perform multiple functions across diverse platforms.
Solution Approach 2:
The patent applies parameter changes by implementing adjustable segmentation thresholds, region definitions, and analysis criteria that can be modified based on the specific imaging platform and protocol being used. The system dynamically adapts its parameters to match different scan types, contrast agents, and clinical indications, maintaining high efficiency across varying conditions without requiring separate specialized systems.
3Reliability
If comprehensive whole-body segmentation is performed, then detection completeness improves, but computational complexity and processing time increase
Solution Approach 1:
The patent applies segmentation by dividing the complex whole-body analysis task into smaller, manageable anatomical regions (head, thorax, abdomen, pelvis, extremities). Each region is processed independently using region-specific algorithms and parameters, reducing the overall computational burden while maintaining comprehensive detection. This hierarchical segmentation allows the system to handle large volumes of data efficiently by breaking down the problem into modular components.
Solution Approach 2:
The patent implements partial action by focusing computational resources on clinically relevant regions and structures rather than attempting to segment every anatomical feature in the body. The system prioritizes segmentation of regions with higher diagnostic value or those most likely to contain pathology, performing detailed analysis where needed and simplified analysis elsewhere, thus reducing overall complexity while maintaining detection completeness for critical areas.
Data Source
AI summary
Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and/or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and/or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and/or determine efficacy of a treatment modality.