Abstract #1029

A Rapid Deep Learning Approach to Parcellate 280 Anatomical Regions to Cover the Whole Brain

Kei Nishimaki^1,2, Kengo Onda¹, Kumpei Ikuta², Jill Chotiyanonta¹, Yuto Uchida¹, Susumu Mori¹, Hitoshi Iyatomi², and Kenichi Oishi^1,3

¹The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²Department of Applied Informatics, Hosei University Graduate School of Science and Engineering, Tokyo, Japan, ³The Richman Family Precision Medicine Center of Excellence in Alzheimer’s Disease, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Synopsis

Keywords: Segmentation, Segmentation

Motivation: Whole-brain MRI parcellation serves as a feature extraction technique, allowing for the condensation of over a million pixels of information into a few hundred neuroanatomically defined elements.

Goal(s): The multi-atlas label-fusion (MALF) method is known for accurate parcellation but typically necessitates several hours to process a single image. Our goal was to develop a faster parcellation tool with an accuracy comparable to that of MALF.

Approach: We introduce open-source multiple anatomical parcellation T1 (OpenMAP-T1), based on deep learning and multi-processing.

Results: The OpenMAP achieves an equivalent parcellation performance to MALF and is 40 times faster.

Impact: OpenMAP significantly accelerates processing speed, allowing for large-scale data analysis using volumetric information derived from detailed parcellation of the whole brain, including both gray and white matter regions.

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