Pelvic Lymph Lesion Classification Using 3D Atlas Alignment
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Solution Overview
Problem
Current nuclear medicine imaging techniques face challenges in accurately identifying and classifying cancerous lesions, particularly in pelvic lymph regions, due to difficulties in segmenting pelvic lymph nodes within anatomical images, which hinders precise diagnosis and treatment planning.
Innovation Solution
A method combining machine learning-based segmentation with an atlas image approach to align pelvic atlas images with anatomical images, allowing for precise identification and classification of lesions based on their spatial relationship with pelvic lymph regions, using PET/CT or SPECT/CT imaging systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional nuclear medicine imaging techniques are used to identify lesions, then the imaging process is simple, but the accuracy of identifying and classifying cancerous lesions in pelvic lymph regions is poor
Solution Approach 1:
The patent segments the pelvic region into multiple sub-regions (anterior, middle, posterior compartments) and processes each segment separately. This allows the system to focus computational resources on specific anatomical areas, improving lesion identification accuracy while managing system complexity through divide-and-conquer approach
Solution Approach 2:
The patent introduces an atlas image as an intermediary reference that bridges the gap between raw nuclear medicine images and anatomical structures. The atlas provides pre-defined pelvic lymph node regions that serve as a template for comparing and classifying lesions, enabling accurate classification without requiring the system to manually define complex anatomical boundaries
2Measurement precision
If manual segmentation of pelvic lymph nodes is performed, then classification accuracy may be improved, but the time and resource consumption increases significantly
Solution Approach 1:
The patent performs preliminary segmentation of the pelvic region into anatomical compartments before lesion classification. By pre-defining the spatial structure of pelvic lymph node regions using atlas-based approaches, the system eliminates the need for time-consuming manual segmentation during actual diagnosis, achieving both precision and efficiency
Solution Approach 2:
The patent uses an atlas image as a template or copy of typical pelvic anatomy that can be overlaid and compared against patient-specific images. This copying approach allows the system to rapidly identify pelvic lymph node regions by matching against the pre-established atlas structure, avoiding repeated manual segmentation for each patient
3Measurement precision
If detailed classification of pelvic lymph regions is implemented, then diagnostic precision is improved, but the complexity of the classification system increases
Solution Approach 1:
The patent divides the pelvic region into three main compartments (anterior, middle, posterior) based on anatomical landmarks. This segmentation simplifies the classification task by reducing the continuous anatomical space into discrete, manageable regions that can be systematically evaluated against lesion locations
Solution Approach 2:
The patent creates a multi-functional classification system that simultaneously provides anatomical localization, lesion classification, and treatment planning guidance. The atlas-based framework serves multiple purposes: defining pelvic lymph node regions, classifying lesions by location, and providing a universal reference that can be applied across different imaging modalities and patient populations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate classification of cancerous lesions by aligning pelvic lymph regions with anatomical images, improving diagnostic precision and treatment planning for cancerous lesions.
Implementation Method 1
certain radiopharmaceuticals, following administration to a patient, accumulate in regions of abnormal osteogenesis associated with malignant bone lesions
Implementation Method 2
Other radiopharmaceuticals may bind to specific receptors, enzymes, and proteins in the body that are altered during evolution of disease
Implementation Method 3
Nuclear medicine imaging techniques capture images by detecting radiation emitted from the radioactive portion of the radiopharmaceutical
Data Source
AI summary
Presented herein are systems and methods that provide automated analysis of 3D images to classify representations of lesions identified therein. In particular, in certain embodiments, approaches described herein allow hotspots representing lesions to be classified based on their spatial relationship with (e.g., whether they are in proximity to, overlap with, or are located within) one or more pelvic lymph node regions in detailed fashion.