Dual-Resolution ROI Segmentation for Medical Image Processing
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Solution Overview
Problem
Current medical image segmentation methods, such as those used in MRI and CT imaging, are inefficient and resource-intensive, particularly when segmenting regions of interest like polycystic kidneys, requiring significant time and computational resources.
Innovation Solution
A system and method for image segmentation using a dual ROI segmentation model with convolutional layers, where the first model segments at a lower image resolution and the second model refines at a higher resolution, reducing processing time and resources by employing a Bottleneck structure and efficient memory utilization strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single high-resolution ROI segmentation model is used, then segmentation accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the image processing into two stages: first segmenting a preliminary region at low resolution, then segmenting the target region at high resolution. This multi-stage segmentation approach reduces the computational burden on the high-resolution model while maintaining accuracy.
Solution Approach 2:
The patent introduces a resolution dimension by using two different image resolutions (first resolution and second resolution) for different processing stages. The low-resolution model handles coarse segmentation, while the high-resolution model refines specific regions, effectively adding a dimensional strategy to balance speed and accuracy.
2Measurement precision
If a single high-resolution ROI segmentation model is used, then segmentation accuracy is improved, but computational complexity and memory requirements increase
Solution Approach 1:
The patent segments the computational task into two parts: a first ROI segmentation model for preliminary region identification and a second ROI segmentation model for precise target region segmentation. This division reduces the computational complexity of each individual model while maintaining overall accuracy.
Solution Approach 2:
The patent applies different processing qualities to different regions: low-resolution processing for the entire image to identify preliminary regions, and high-resolution processing only for the specific target region. This local quality approach reduces overall computational complexity while preserving necessary accuracy where needed.
3Measurement precision
If manual annotation by doctors is used, then segmentation accuracy is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary region segmentation automatically using the first ROI segmentation model before final target region segmentation. This preliminary action eliminates the need for manual annotation in most cases, significantly improving productivity while maintaining accuracy through the two-stage automated process.
Solution Approach 2:
The patent enables the system to perform segmentation automatically without requiring manual doctor annotation. The dual-model system serves itself by identifying preliminary regions and then refining them into accurate target regions, replacing manual labor with automated intelligent processing.
Data Source
AI summary
A system for image segmentation is provided. The system may obtain a target image including an ROI, and segment a preliminary region representative of the ROI from the target image using a first ROI segmentation model corresponding to a first image resolution. The system may segment a target region representative of the ROI from the preliminary region using a second ROI segmentation model corresponding to a second image resolution. At least one model of the first and second ROI segmentation models may at least include a first convolutional layer and a second convolutional layer downstream to the first convolutional layer. A count of input channels of the first convolutional layer may be greater than a count of output channels of the first convolutional layer, and a count of input channels of the second convolutional layer may be smaller than a count of output channels of the second convolutional layer.