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Published on: April 19, 2017
Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism
Li Wang1, Gang Li1, Ehsan Adeli1
1IDEA Lab, Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina.
Insights
Accurate infant brain MRI segmentation is vital for autism research. This new anatomy-guided method improves tissue segmentation and corrects errors in isointense images, enhancing early brain development analysis.
Area of Science:
- Neuroimaging
- Medical Image Analysis
- Developmental Neuroscience
Background:
- Infant brain MRI segmentation is crucial for understanding early brain development and identifying autism biomarkers.
- Low tissue contrast in infant MRIs, especially around 6 months, poses significant segmentation challenges.
- Existing methods often ignore anatomical prior knowledge, leading to limited accuracy and topological errors.
Purpose of the Study:
- To develop an anatomy-guided framework for joint tissue segmentation and topological correction in isointense infant brain MRIs.
- To improve the accuracy and topological correctness of brain MRI segmentation in infants at risk for autism.
- To address the limitations of current segmentation techniques in characterizing early brain development.
Main Methods:
- Proposed an anatomy-guided joint tissue segmentation and topological correction framework.
- Utilized a signed distance map of the outer cortical surface as anatomical prior knowledge.
- Incorporated anatomical priors to guide segmentation in ambiguous regions of isointense infant MRIs.
Main Results:
- The proposed framework effectively corrected topological errors in infant brain MRIs.
- Demonstrated robustness to motion artifacts.
- Achieved superior segmentation accuracy and topological correctness compared to state-of-the-art methods.
- Experimental results validated on subjects from the National Database for Autism Research.
Conclusions:
- The anatomy-guided framework significantly enhances tissue segmentation and topological correction for isointense infant MRIs.
- This method offers a more reliable approach for analyzing early brain development in infants at risk for autism.
- Improved segmentation accuracy and topological correctness facilitate more precise biomarker identification and characterization of neurodevelopmental trajectories.
Abstract:
Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy-guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state-of-the-art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness.
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