Brain Landmark Localization Using Probability Maps and Coordinate Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for manually localizing brain structure identifiers such as the anterior commissure (AC), posterior commissure (PC), and midsagittal plane (MSP) are time-consuming, subjective, and lack repeatability, with existing automatic solutions being inefficient and lacking robustness.
Innovation Solution
A system utilizing a neural network model to automatically determine region, landmark, and plane identifiers from brain images, constructing a target coordinate system based on these identifiers, and determining landmarks like AC, PC, and MSP with high accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual localization is performed by doctors, then accuracy can be maintained through expert judgment, but the process becomes time-consuming and lacks repeatability
Solution Approach 1:
The patent replaces the manual mechanical localization process with an automated computer-based system that uses image processing algorithms to automatically identify and locate brain identifiers such as AC, PC, and MSP landmarks, eliminating the need for manual doctor intervention while maintaining localization accuracy
Solution Approach 2:
The system enables self-service localization where the computer automatically performs the localization task without requiring expert medical personnel to manually identify landmarks, allowing the system to serve itself in completing the localization process
2Loss of time
If existing automatic determination solutions are implemented, then time consumption is reduced, but the processing process becomes complicated and robustness deteriorates
Solution Approach 1:
The patent segments the localization task into distinct components: image acquisition, probability map generation for different brain identifiers, and coordinate system construction. Each component is processed independently through dedicated neural network models, simplifying the overall complex process while maintaining automation benefits
Solution Approach 2:
The system employs a universal neural network framework that can handle multiple types of brain identifiers (AC, PC, MSP, and cortical landmarks) simultaneously through a unified probability map generation approach, reducing processing complexity compared to separate specialized algorithms for each identifier type
3Extent of automation
If existing automatic determination solutions are used, then automation is achieved, but efficiency and practical value remain low
Solution Approach 1:
The patent changes the operational parameters by using probability map generation as an intermediate representation, allowing the system to efficiently query and extract multiple brain identifier locations from a single processed image, significantly improving productivity compared to sequential detection methods
Solution Approach 2:
The system performs preliminary action by pre-generating comprehensive probability maps that contain information about all brain identifiers before actual localization queries are made. This preliminary processing enables rapid subsequent queries without reprocessing the original image, enhancing overall efficiency
Data Source
AI summary
Embodiments of the present disclosure provide systems and methods for brain identifier localization. The methods include obtaining an image of a brain; determining region identifier probability map(s) of the brain, landmark identifier probability map(s) of the brain, and plane identifier probability map(s) of the brain based on the image and a neural network model; determining a segmentation result of a cerebral cortex of the brain, landmark identifier(s) of the brain, and plane identifier(s) of the brain, respectively, based on the region identifier probability map(s), the landmark identifier probability map(s), and the plane identifier probability map(s); constructing a target coordinate system based on the landmark identifier(s) and the plane identifier(s); and determining landmark(s) of the cerebral cortex based on the segmentation result of the cerebral cortex, the target coordinate system, and/or the landmark identifier(s).