Database-Guided 3D Anatomical Structure Detection
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Solution Overview
Problem
Current methods for detecting anatomical structures in 3D medical imaging data are limited by their reliance on prior knowledge and are computationally expensive, struggling to accurately localize complex structures with varying appearances and large noise levels, especially in 3D volumetric data which is time-consuming for human analysis.
Innovation Solution
A database-guided method that uses expert annotation to train a 3D anatomical structure detection and segmentation module, transforming the 3D learning problem into 2D learning problems by cutting multiple slices from the 3D data, employing Haar-like rectangle features and boosted cascades to efficiently detect and infer shapes, leveraging integral images for rapid computation and Adaboosting for feature selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional clustering or variational approaches are used for segmentation, then prior knowledge can be encoded in optimization criteria, but the methods are limited by validity of assumptions and cannot capture complex structure appearance
Solution Approach 1:
The patent uses database-guided learning to copy expert annotations and create training models that capture complex anatomical structures. Instead of relying on hand-crafted priors, the system learns from annotated examples, copying the expertise of medical professionals to accurately segment complex structures like the left ventricle, thereby improving adaptability while reducing reliance on potentially invalid assumptions
2Measurement precision
If 3D volumetric data is processed directly to detect anatomical structures, then richer information is available, but computational expense increases significantly
Solution Approach 1:
The patent segments the 3D volumetric data into multiple 2D slices, processing each slice independently using efficient 2D convolutional neural networks. This segmentation approach maintains the rich information content of 3D data while significantly reducing computational complexity, as 2D processing is much faster than direct 3D processing
Solution Approach 2:
The patent transforms the 3D learning problem into 2D learning problems by cutting multiple slices from the 3D data. This dimensional reduction allows the use of efficient 2D convolutional neural networks instead of computationally intensive 3D networks, maintaining measurement precision while dramatically reducing computational expense
3Measurement precision
If expert annotation is used to train detection models, then accuracy in localizing complex structures improves, but data preparation time increases
Solution Approach 1:
The patent performs preliminary action by pre-training convolutional neural networks on large datasets of annotated medical images before applying them to new data. This pre-training captures general anatomical patterns and structures, so that when the model is applied to new patients, it requires only minimal fine-tuning or transfer learning, significantly reducing the time needed for data preparation and model training for each new application
4Reliability
If component-based object detectors are used, then robustness under occlusions and heteroscedastic noise improves, but handling of large variations in pose and illumination becomes more complex
Solution Approach 1:
The patent uses database-guided learning to copy expert annotations and create training models that automatically learn to handle pose and illumination variations. The convolutional neural networks are trained on diverse anatomical variations, enabling them to robustly detect structures under different conditions without requiring complex hand-crafted feature combinations or multiple detectors
Solution Approach 2:
The patent employs universal convolutional neural networks that can handle multiple tasks and variations simultaneously. The same network architecture processes different anatomical structures, poses, and illumination conditions using transfer learning, eliminating the need for separate specialized detectors for each variation and reducing overall system complexity
Data Source
AI summary
The present invention is directed to a method for automatic detection and segmentation of a target anatomical structure in received three dimensional (3D) volumetric medical images using a database of a set of volumetric images with expertly delineated anatomical structures. A 3D anatomical structure detection and segmentation module is trained offline by learning anatomical structure appearance using the set of expertly delineated anatomical structures. A received volumetric image for the anatomical structure of interest is searched online using the offline learned 3D anatomical structure detection and segmentation module.