Whole-Body 3D Image Segmentation for Platform-Agnostic Uptake Analysis
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Solution Overview
Problem
Current medical imaging analysis processes rely heavily on radiologist interpretation, which can be subjective and time-consuming, and patients struggle to understand complex imaging results, leading to misinformed decisions about disease diagnosis and treatment.
Innovation Solution
Developed systems and methods for automated analysis of 3D medical images using machine learning techniques, particularly Convolutional Neural Networks (CNNs), to accurately identify specific anatomical regions and lesions across the entire body, enabling standardized evaluation of radiopharmaceutical uptake and disease severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning analysis is implemented, then productivity and consistency improve, but device complexity increases
Solution Approach 1:
The patent replaces manual radiologist interpretation (mechanical human process) with automated machine learning algorithms. CNN-based models automatically segment anatomical structures and quantify radiopharmaceutical uptake, eliminating the need for subjective human visual analysis while maintaining or improving diagnostic accuracy.
Solution Approach 2:
The patent introduces a multi-stage processing pipeline with intermediate steps including image pre-processing, anatomical segmentation, uptake quantification, and quality control. These intermediary computational stages break down the complex analysis task into manageable components, improving both productivity and system transparency.
2Measurement precision
If standardized automated analysis is used, then measurement precision improves, but adaptability to different imaging platforms decreases
Solution Approach 1:
The patent develops a universal analysis platform that can process images from multiple imaging modalities (PET, SPECT, planar imaging) and different radiopharmaceuticals. The system uses standardized protocols and calibration methods that adapt to various platforms while maintaining consistent measurement precision across all systems.
Solution Approach 2:
The patent employs adjustable parameters and platform-specific calibration factors that allow the standardized analysis algorithm to adapt to different imaging systems. By modifying certain parameters (e.g., resolution corrections, attenuation coefficients) while maintaining the core analysis framework, the system achieves both precision and versatility.
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.