Adaptive Anatomical Region Prediction via Landmark Similarity
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Solution Overview
Problem
Current medical imaging technologies face challenges in automating anatomical region detection due to varying imaging protocols across clinical sites, which limits the adaptability and feasibility of learning-based approaches for consistent scan quality.
Innovation Solution
A framework for adaptive anatomical region prediction that uses exemplar images with annotated landmarks to compute anatomical similarity scores and predict anatomical regions, allowing for adaptive combination of anatomical regions based on these scores, enabling automatic detection and handling of outliers and missing landmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning-based approaches are used to detect anatomical regions, then detection accuracy is improved, but the system requires re-training for each clinical site which increases complexity and reduces adaptability
Solution Approach 1:
The system performs preliminary actions by collecting and storing multiple exemplar images with manually annotated anatomical regions before actual detection. These pre-prepared exemplars serve as a reusable knowledge base that eliminates the need for re-training at each clinical site, while still achieving high detection accuracy through adaptive combination of these pre-processed examples.
Solution Approach 2:
Instead of re-training models at each clinical site, the system creates copies of annotated exemplar images from different clinical sites and adapts them to new sites through landmark-based similarity comparison. This copying approach allows the system to leverage knowledge from multiple sites without requiring expensive and time-consuming re-training processes.
2Adaptability or versatility
If multiple imaging protocols from different clinical sites are accommodated, then adaptability is improved, but the system complexity increases due to need for multiple trained models
Solution Approach 1:
The system achieves universality by designing a single detection framework that can handle multiple imaging protocols from different clinical sites. Instead of creating separate specialized models for each protocol, the system uses a unified approach with landmark-based similarity comparison that works across diverse protocols, making the system multi-functional without increasing complexity.
Solution Approach 2:
The system adapts to different imaging protocols by changing parameters such as landmark weights and combination coefficients based on anatomical similarity scores, rather than changing the fundamental detection model. This parameter adjustment approach allows the system to accommodate various protocols while maintaining a single, simple detection framework.
3Reliability
If manual annotation of anatomical regions is performed for each exemplar image, then training data quality is improved, but the time and resources required increase significantly
Solution Approach 1:
Instead of requiring complete manual annotation of entire anatomical regions, the system uses partial action by annotating only key landmark points on exemplar images. This partial annotation approach significantly reduces the time and resources required while still providing sufficient information for the landmark-based similarity comparison and region prediction to achieve high reliability.
Data Source
AI summary
Disclosed herein is a framework for facilitating adaptive anatomical region prediction. In accordance with one aspect, a set of exemplar images including annotated first landmarks is received. User definitions of first anatomical regions in the exemplar images are obtained. The framework may detect second landmarks in a subject image. It may further compute anatomical similarity scores between the subject image and the exemplar images based on the first and second landmarks, and predict a second anatomical region in the subject image by adaptively combining the first anatomical regions based on the anatomical similarity scores.