3D Medical Image Alignment Using Landmark-Guided Refinement
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Solution Overview
Problem
Manual alignment of 3D medical images for standard orientations is time-consuming and prone to errors, leading to potential misinterpretation and incorrect diagnoses.
Innovation Solution
A method and system using a deep neural network to automatically align 3D medical images by detecting anatomical landmarks, estimating transformation parameters, and iteratively refining them to achieve accurate alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment of 3D medical images is performed, then alignment can be achieved, but the process is time-consuming and prone to errors
Solution Approach 1:
The system enables automatic self-alignment of 3D medical images by detecting anatomical landmarks and computing transformation parameters without human intervention. The computer automatically identifies key anatomical points, calculates rotation and translation transformations, and applies them to align images to standard orientations, eliminating the need for manual technician intervention while maintaining high alignment accuracy.
2Productivity
If manual alignment is performed by a novice technician, then alignment can be completed, but errors are more likely to occur
Solution Approach 1:
The system incorporates confidence scoring that evaluates the quality of detected anatomical landmarks and computed transformation parameters. This feedback mechanism allows the system to assess its own alignment reliability and can prompt for manual review when confidence is low, thereby maintaining high reliability while enabling rapid automatic alignment for high-confidence cases.
3Loss of time
If automatic alignment using deep neural network is implemented, then alignment time is reduced, but system complexity increases
Solution Approach 1:
The patent replaces the mechanical/manual process of image alignment with an automated computational system based on deep neural networks. The system uses machine learning models to detect anatomical landmarks and compute transformation parameters automatically, substituting human manual operations with intelligent algorithms that reduce alignment time while managing complexity through software-based solutions.
Data Source
AI summary
The current disclosure provides systems and methods for automatic image alignment of three-dimensional (3D) medical image volumes. The method includes pre-processing the 3D medical image volume by selecting a sub-volume of interest, detecting anatomical landmarks in the sub-volume using a deep neural network, estimating transformation parameters based on the anatomical landmarks to adjust rotation angles and translation of the sub-volume, adjusting the rotation angles and translation to produce a first aligned sub-volume, determining confidence in the transformation parameters based on the first aligned sub-volume, and iteratively refining the transformation parameters if the confidence is below a predetermined threshold. The disclosed approach for automated image alignment reduces the need for manual alignment and, increases a probability of the 3D image volume converging to a desired orientation compared to conventional approaches.