Brain Landmark Localization Using Neural Coordinate Mapping
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Solution Overview
Problem
Existing methods for localizing brain structure identifiers such as the anterior commissure (AC), posterior commissure (PC), and midsagittal plane (MSP) in neurosurgical analysis are time-consuming, subjective, and lack repeatability, with existing automatic solutions being inefficient and of low practical value.
Innovation Solution
A system utilizing a neural network model to generate probability maps for brain region, landmark, and plane identifiers, followed by segmentation and coordinate system construction to accurately determine these landmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual localization of brain identifiers (AC, PC, MSP, cortical landmarks) is performed by doctors, then the localization can be performed with human expertise and judgment, but the process is time-consuming and has low repeatability due to subjective operator influence
Solution Approach 1:
The patent replaces the manual mechanical localization process with an automated computer-based system. The system uses image processing algorithms to automatically detect and localize brain identifiers (AC, PC, MSP, and cortical landmarks) from medical images, eliminating the need for manual doctor intervention while maintaining high accuracy through automated coordinate system construction and landmark detection algorithms.
Solution Approach 2:
The system enables self-service localization by automatically performing all localization tasks without human intervention. The automated algorithm independently processes medical images, identifies brain structures, constructs coordinate systems, and determines landmark positions, making the system self-sufficient and eliminating time loss associated with manual operations.
2Loss of time
If existing automatic determination solutions are used to locate brain identifiers, then the time consumption is reduced, but the processing process becomes complicated with poor robustness and low efficiency
Solution Approach 1:
The patent segments the localization task into distinct modules: image acquisition, coordinate system construction (localizing AC, PC, and MSP), and cortical landmark detection. Each module handles a specific aspect of the localization process, making the overall system more manageable and robust while reducing processing complexity through modular design.
Solution Approach 2:
The system implements a universal automated localization framework that can handle multiple brain identifiers (AC, PC, MSP, and various cortical landmarks) using a unified approach. This multi-functional system eliminates the need for separate specialized procedures for each identifier, reducing overall processing complexity while maintaining efficiency across different localization tasks.
Data Source
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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).