Anatomical Vector Segmentation Without Repeated Atlas Registration
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Solution Overview
Problem
Existing medical image segmentation and labeling methods require significant computational effort due to the need for registering patient images with anatomical atlases, which is inefficient and resource-intensive.
Innovation Solution
A method involving a tracked imaging device and an anatomical atlas is used to establish a transformation between the atlas space and patient space, training a learning algorithm with anatomical vectors and labels to enable efficient segmentation and labeling of patient images without repeated atlas registration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patient images are segmented and labeled using an anatomical atlas with registration, then segmentation accuracy is improved, but computational effort increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-training a learning algorithm using atlas data and anatomical vectors before actual patient image segmentation. The learning algorithm is trained offline with labeled atlas images and anatomical vectors, creating a model that can be rapidly applied to patient images without requiring repeated registration computations. This shifts the computational burden from the segmentation phase to the preliminary training phase.
Solution Approach 2:
The patent uses copying by creating anatomical vectors that represent the atlas data in a compressed, parameterized form. Instead of repeatedly processing and registering full atlas images, the system copies essential anatomical information into vector representations that can be efficiently compared and matched against patient images, reducing computational requirements while maintaining segmentation accuracy.
2Measurement precision
If atlas registration is performed for each patient image, then segmentation precision is improved, but processing time increases
Solution Approach 1:
The learning algorithm is pre-trained offline using atlas images and their corresponding anatomical vectors, establishing the relationship between anatomical structures and their vector representations before actual patient image processing. This preliminary training creates a model that can rapidly segment patient images without requiring repeated registration computations for each image.
Solution Approach 2:
The patent replaces the traditional mechanical registration process with a learning-based approach. Instead of performing iterative image registration algorithms that compare and align full atlas images with patient images, the system uses a trained learning algorithm that processes anatomical vectors and image features, substituting computationally intensive mechanical registration with a more efficient machine learning inference process.
3Measurement precision
If a tracked imaging device is used to establish transformation between atlas space and patient space, then anatomical accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts the essential transformation information from the complex tracked imaging device system into anatomical vectors that represent the relationship between atlas space and patient space. By separating the tracking function from the segmentation function and representing the transformation in a simplified vector form, the system reduces overall complexity while maintaining anatomical accuracy.
Solution Approach 2:
The learning algorithm serves multiple functions: it processes anatomical vectors from the tracked imaging device, performs image segmentation, and generates labeled outputs. This multi-functional approach consolidates several operations into a single unified system, reducing the need for separate complex components while maintaining anatomical accuracy through the tracked imaging device.
Data Source
AI summary
Disclosed is a computer-implemented method which encompasses registering a tracked imaging device such as a microscope having a known viewing direction and an atlas to a patient space so that a transformation can be established between the atlas space and the reference system for defining positions in images of an anatomical structure of the patient. Labels are associated with certain constituents of the images and are input into a learning algorithm such as a machine learning algorithm, for example a convolutional neural network, together with the medical images and an anatomical vector and for example also the atlas to train the learning algorithm for automatic segmentation of patient images generated with the tracked imaging device. The trained learning algorithm then allows for efficient segmentation and/or labelling of patient images without having to register the patient images to the atlas each time, thereby saving on computational effort.