Brain Tumor MRI Segmentation Using White Matter Template and Diffusion Weighted Imaging
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Solution Overview
Problem
Current brain tumor segmentation methods using magnetic resonance imaging (MRI) cannot accurately distinguish between the tumor body and edema regions, which is crucial for complete tumor resection and prognosis.
Innovation Solution
A method involving the acquisition of a basic white matter template from healthy samples, registration with patient images, and the use of high b-value diffusion weighted images and apparent diffusion coefficient images to segment the tumor body and edema regions by removing normal white matter regions, allowing for precise separation of these areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current MRI segmentation methods are used to separate tumor region from normal brain tissues, then the tumor region can be identified, but the boundary between tumor body and edema cannot be clearly determined
Solution Approach 1:
The patent divides the tumor region into multiple sub-regions (tumor body, edema, necrosis) by analyzing signal intensity distributions and using clustering algorithms. This segmentation of the segmentation process enables clear differentiation between tumor body and edema boundaries that were previously indistinguishable.
Solution Approach 2:
The patent applies different analysis methods to different regions within the tumor. By examining local signal characteristics and using region-specific clustering, the method preserves local boundary information while achieving global segmentation accuracy.
2Measurement precision
If multiple image sequences and processing steps are used to improve segmentation accuracy, then tumor body and edema can be separated, but the device complexity increases
Solution Approach 1:
The patent uses a unified clustering algorithm framework that can process multiple MRI sequences (T1, T2, FLAIR, DWI) simultaneously. This multi-functional approach achieves accurate segmentation without requiring separate processing pipelines for each sequence, thereby controlling system complexity.
Solution Approach 2:
The patent combines multiple MRI sequences and integrates them into a single segmentation process using clustering algorithms. By merging the information from different sequences rather than processing them separately, the method achieves high accuracy while avoiding the complexity of multiple independent processing systems.
Data Source
AI summary
A brain tumor image segmentation method and device are disclosed. The disclosed method includes acquiring a basic white matter template generated based on brain magnetic resonance images of a plurality of healthy samples, collecting corresponding low, mid and high b-value diffusion weighted images of the brain of a patient, segmenting out a tumor region including the tumor body and the edema on each image based on the signal distribution of each image in a first set image group of the patient, removing the normal white matter region from the tumor region according to the basic white matter template and the high b-value diffusion weighted image, and classifying the value of the voxel in each image in a second set image group and a second apparent diffusion coefficient image obtained through calculations to obtain a tumor body region and an edema region.