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Abstract #2702

Robust, Atlas-Free, Automatic Segmentation of Brain MRI in Health and Disease

Govind Nair1, Kartiga Selvaganesan1, Emily Whitehead1, Paba M DeAlwis1, Mathew K Schindler1, Bryan Smith1, Avindra Nath1, Steven Jacobson1, Daniel S Reich1, Ziad Saad2, Souheil Inati3, and Sara Inati1

1NINDS, National Institutes of Health, Bethesda, MD, United States, 2NIMH, National Institutes of Health, Bethesda, MD, United States, 3Inati Analytics, Potomac, MD, United States

An atlas-free, brain-segmentation algorithm that uses derivative-based features and logistic regression classifier was optimized and tested on images of healthy volunteers and individuals clinically diagnosed with a variety of neuroimmunological diseases.The algorithm was trained to classify gray and white matter, CSF, globus pallidus, white matter lesions, and “other” tissue classes from all the images routinely acquired at our center. The algorithm achieved highly accurate brain segmentations and outperformed widely used techniques for brain segmentation and lesion detection. The algorithm has been found to be versatile in brain segmentation using images acquired at other collaborator sites.

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