Tracked Imaging Segmentation Using Anatomical Vectors
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Solution Overview
Problem
Existing medical image segmentation and labeling methods require significant computational effort 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 pre-registers the anatomical atlas to the imaging device coordinate system and pre-computes anatomical vectors and labels for each atlas element. This preliminary processing creates a ready-to-use mapping that eliminates the need for repeated registration during actual segmentation tasks, thereby reducing computational effort while maintaining accuracy
Solution Approach 2:
The patent creates a transformed copy of the anatomical atlas in the imaging device's coordinate system, complete with pre-computed anatomical vectors and labels. This copied and pre-processed atlas can be directly applied to patient images without requiring repeated registration computations, thus resolving the contradiction between accuracy and computational cost
2Reliability
If atlas registration is performed for each patient image, then segmentation reliability is improved, but processing time increases
Solution Approach 1:
The registration between the anatomical atlas and the imaging device is performed once in advance, and the transformation parameters are stored. During actual patient image processing, this pre-established registration is reused, eliminating repeated registration computations and significantly reducing processing time while maintaining segmentation reliability
Solution Approach 2:
The pre-computed anatomical vectors and labels in the imaging device coordinate system serve as a universal reference that can be applied to multiple patient images without re-computation. This multi-functional approach maintains consistent segmentation reliability across different patients while minimizing processing time
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.