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

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.