LOGISMOS-B for Primates: Primate Cortical Surface Reconstruction and Thickness Measurement

• Authors: Ipek Oguz, University of Iowa; Martin Styner, University of North Carolina; Maria Sanchez, Emory University; Yundi Shi, University of North Carolina; Milan Sonka, University of Iowa
• Journal Title: Proceedings of SPIE
• Volume: Volume 9413
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE) | 2015-01-01
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1117/12.2082327
• Subjects: Engineering, Biomedical; Computer Science
• Keywords: Science & Technology; Physical Sciences; Life Sciences & Biomedicine; Optics; Radiology, Nuclear Medicine & Medical Imaging; Cortical thickness; brain; cortex; MRI; primate; macaque; animal imaging; segmentation; GRAPH IMAGE SEGMENTATION; MULTIPLE OBJECTS; BRAIN; CLASSIFICATION; VOLUME

Abstract

Cortical thickness and surface area are important morphological measures with implications for many psychiatric and neurological conditions. Automated segmentation and reconstruction of the cortical surface from 3D MRI scans is challenging due to the variable anatomy of the cortex and its highly complex geometry. While many methods exist for this task in the context of the human brain, these methods are typically not readily applicable to the primate brain. We propose an innovative approach based on our recently proposed human cortical reconstruction algorithm, LOGISMOS-B, and the Laplace-based thickness measurement method. Quantitative evaluation of our approach was performed based on a dataset of T1-and T2-weighted MRI scans from 12-month-old macaques where labeling by our anatomical experts was used as independent standard. In this dataset, LOGISMOS-B has an average signed surface error of 0.01 ± 0.03mm and an unsigned surface error of 0.42 ± 0.03mm over the whole brain. Excluding the rather problematic temporal pole region further improves unsigned surface distance to 0.34 ± 0.03mm. This high level of accuracy reached by our algorithm even in this challenging developmental dataset illustrates its robustness and its potential for primate brain studies.