Anatomically Consistent Embeddings for Multi-Scale Medical Imaging
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Solution Overview
Problem
Existing self-supervised learning (SSL) methods for medical images fail to effectively capture hierarchical and multi-scale consistency in anatomical structures, missing nuanced spatial relationships and local details crucial for accurate medical analysis.
Innovation Solution
The Anatomically Consistent Embeddings (ACE) framework employs grid-wise multi-scale cropping and learns global and local consistencies through composition and decomposition, using contrastive learning and matrix matching to optimize feature extraction across varying scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If existing SSL methods are used for medical images, then training without expert annotations is achieved, but hierarchical and multi-scale consistency in anatomical structures is not captured
Solution Approach 1:
The patent divides medical images into multiple scales and hierarchies of anatomical structures. The SSL framework processes images at different resolution levels and extracts features from various anatomical hierarchies (organ level, lobe level, segment level), ensuring that spatial relationships are preserved across all scales rather than lost in a single-scale processing approach.
Solution Approach 2:
The patent adds a hierarchical dimension to the traditional 2D image processing by introducing multi-scale analysis. The framework processes images at multiple resolution levels and integrates information across these dimensional layers, capturing spatial relationships that would be invisible at any single scale while maintaining training efficiency through self-supervised learning.
2Ease of manufacture
If photographic image SSL techniques are applied to medical images, then reduction of annotation dependence is achieved, but anatomical characteristics are not fully exploited
Solution Approach 1:
The patent applies different processing strategies to different anatomical regions and scales. The SSL framework extracts features specific to local anatomical structures (lung lobes, liver segments, bronchial trees) while maintaining global contextual understanding. This local quality approach allows the model to adapt to the specific hierarchical organization of medical anatomy rather than treating all regions uniformly.
Solution Approach 2:
The patent changes the processing parameters to match anatomical characteristics by implementing multi-scale analysis with different resolution levels and hierarchical feature extraction. The framework adjusts processing granularity to match anatomical hierarchies, enabling the model to capture both fine-grained local anatomical details and coarse-grained global relationships without requiring expert annotations.
3Stability of the object's composition
If global anatomical patterns are learned, then consistency across patients is achieved, but local anatomical details and spatial relationships are lost
Solution Approach 1:
The patent segments the anatomical analysis into multiple hierarchical levels: global organ-level patterns, intermediate lobe/segment-level structures, and local fine-grained anatomical details. The SSL framework processes each level separately and integrates them, ensuring that global consistency is maintained while local measurement precision is preserved through dedicated feature extraction at each hierarchical level.
Solution Approach 2:
The patent implements a nested hierarchical structure where local anatomical details are nested within intermediate structures, which are in turn nested within global organ patterns. The SSL framework extracts features at each nested level and combines them, allowing the model to simultaneously capture global anatomical consistency and local detail precision by processing information from the innermost to outermost hierarchical levels.
4Reliability
If multi-scale cropping is implemented, then hierarchical consistency is learned, but computational complexity increases
Solution Approach 1:
The patent segments the computational process into distinct hierarchical levels with dedicated feature extraction for each scale. Rather than processing all scales simultaneously with equal complexity, the framework divides computation into manageable segments corresponding to different anatomical hierarchies, reducing overall processing complexity while maintaining reliable anatomical representation at each level.
Solution Approach 2:
The patent applies partial processing at each hierarchical level, focusing computational resources on the most diagnostically relevant features at each scale. The SSL framework extracts only the essential features needed at each hierarchical level rather than processing all possible features, achieving reliable anatomical representation with reduced computational complexity by avoiding excessive processing.
Data Source
AI summary
A method performed by a system having at least a processor and a memory therein to execute instructions for a self-supervised learning framework to learn anatomically consistent embeddings of anatomical structures in medical images of a plurality of patients across varying scales of anatomical structures in the plurality of patients receives a medical image as an input, and obtains a first cropped image and a second overlapping cropped image of the received medical image, each having respective representative global embeddings and corresponding overlapped patch embeddings and non-overlapped patch embeddings. The system calculates a global consistency loss using the respective representative global embeddings and calculates a local consistency loss using the corresponding overlapped patch embeddings and non-overlapped patch embeddings.