3D CNN Phase Detection for Medical Image Registration
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Solution Overview
Problem
Radiologists face challenges in accurately diagnosing diseases using enhanced medical images due to variability in phase identification and spatial alignment, especially when different phases of enhanced medical images are scanned at different time points with varying positions.
Innovation Solution
An enhanced medical images processing method utilizing a pre-trained 3D convolutional neural network to detect and mark phases, segment interest regions, and register images to a common coordinate system, ensuring accurate phase identification and spatial alignment for improved diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If enhanced medical images are scanned at different time points for different phases, then the diagnostic information coverage is improved, but the spatial alignment accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by performing image registration before diagnostic analysis. The system pre-aligns enhanced medical images from different phases and time points using spatial transformation algorithms, establishing a common coordinate system in advance. This preliminary spatial alignment ensures that subsequent diagnostic operations work with properly registered images, resolving the contradiction between comprehensive phase coverage and spatial accuracy.
2Extent of automation
If phase identification is performed manually by radiologists, then the diagnostic expertise is utilized, but the identification consistency deteriorates
Solution Approach 1:
The patent replaces the manual mechanical process of radiologist phase identification with an automated computational system. The system uses image analysis algorithms to automatically detect and identify contrast enhancement phases, replacing the subjective visual assessment with objective, consistent computational evaluation. This substitution maintains diagnostic expertise while eliminating inter-observer variability, achieving both automation and reliability.
3Loss of information
If text descriptions are provided with enhanced medical images, then the image documentation is improved, but the phase distinction clarity deteriorates
Solution Approach 1:
The patent applies color changes by visually encoding different contrast enhancement phases with distinct color representations. The system assigns specific colors to different phases (e.g., arterial phase, venous phase, delayed phase), allowing radiologists to quickly distinguish phases through color-coding rather than relying on text descriptions. This visual differentiation dramatically improves phase distinction clarity while maintaining comprehensive documentation.
Data Source
AI summary
An enhanced medical images processing method and a computing device includes: acquiring series of enhanced medical images and detecting a phase of each enhanced medical image in the series of enhanced medical images using a pre-trained 3D convolutional neural network model. A plurality of target enhanced medical images from the enhanced medical image are selected according to the phases. A plurality of interest images is obtained by identifying and segmenting an interest region in each of the plurality of target enhanced medical images, and finally registering the plurality of interest images. The registered images have clear phase markers and are all spatially aligned, allowing a subsequent doctor or clinician to directly use the registered interest images for diagnosis without the need to rescan the patient.