Anatomical Primitive Detection via Invariant Points
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Solution Overview
Problem
Conventional anatomical primitive detection methods in medical imaging are not generic, adaptable, or automatic, struggling with variability across patients, diseases, and partial images, leading to inefficiencies and inconsistencies in analysis.
Innovation Solution
A method for detecting transformationally invariant points and landmark points in image volumes, followed by fitting a geometric primitive using iteratively re-weighted least squares and consensus voting to discard erroneous points, ensuring robust and consistent detection across different anatomical variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional manual detection methods are used for anatomical primitives, then the detection process can be performed with simple algorithms, but the method lacks generality and cannot adapt to different patient variations, diseases, or partial images
Solution Approach 1:
The patent applies preliminary action by pre-training a deep neural network model on a large dataset of annotated medical images before deployment. This pre-training phase prepares the model to handle various patient variations, diseases, and imaging conditions, enabling it to adapt to different scenarios without requiring complex runtime adjustments or manual reconfiguration.
Solution Approach 2:
The patent utilizes parameter changes by employing a deep neural network with learnable parameters that automatically adjust to different input characteristics. The model's internal parameters are optimized through training to capture variations across patients, anatomical structures, and imaging modalities, providing adaptability without requiring complex external control mechanisms.
2Extent of automation
If manual detection and labeling of anatomical primitives is performed, then the detection approach can be simple and focused on particular planes, but it is not automatic and requires significant human time and effort
Solution Approach 1:
The patent replaces the mechanical manual detection process with an automated deep learning system. The deep neural network automatically performs detection, classification, and localization of anatomical primitives without human intervention, substituting the manual mechanical labeling process with an intelligent automated system that processes images efficiently and consistently.
Solution Approach 2:
The patent implements self-service by enabling the detection system to automatically perform all detection tasks without requiring manual intervention. The deep learning model independently identifies anatomical structures, handles variations, and produces results, making the system self-sufficient and eliminating the need for human operators to perform time-consuming manual detection.
3Reliability
If conventional detection methods focus on particular planes with simple algorithms, then the implementation can be straightforward, but they fail to handle abnormal, irregular, and partial images effectively
Solution Approach 1:
The patent applies preliminary action by pre-training the deep neural network on a diverse dataset that includes abnormal, irregular, and partial images. This pre-training exposes the model to various edge cases and variations before deployment, enabling it to handle such images reliably without requiring complex post-processing or manual intervention during actual detection.
Solution Approach 2:
The patent achieves universality by designing a deep learning model that can handle multiple types of images and anatomical variations through a single unified framework. The model is trained to recognize anatomical primitives across different imaging conditions, pathologies, and anatomical variations, providing reliable detection without requiring separate specialized algorithms for each scenario.
4Measurement precision
If manual detection approaches are used for anatomical primitives, then the method can be simple to implement, but it lacks consistency and repeatability across different studies and patients
Solution Approach 1:
The patent replaces manual detection with an automated deep learning system that ensures consistent and repeatable measurements. The model applies the same detection criteria uniformly across all images, eliminating human variability and ensuring that detection results are consistent regardless of which operator performs the detection or when the detection is performed.
Solution Approach 2:
The patent utilizes parameter changes by employing a trained deep neural network with fixed parameters that ensure consistent detection behavior. Once trained, the model's parameters remain constant, guaranteeing that the same anatomical structures are detected with the same precision across different studies, patients, and operators, thereby achieving high measurement precision and repeatability.
Data Source
AI summary
A method of detecting an anatomical primitive in an image volume includes detecting a plurality of transformationally invariant points (TIPS) in the volume, aligning the volume using the TIPs, detecting a plurality landmark points in the aligned volume that are indicative of a given anatomical object, and fitting a target geometric primitive as the anatomical primitive based using the detected landmark points.