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

1Iowa Institute for Biomedical Imaging, Department of Electrical & Computer Engineering, and Ophthalmology & Visual Sciences, The University of Iowa, Iowa City, IA, USA.
2Department of Psychiatry and Computer Science, University of North Carolina, Chapel Hill, NC, USA.
3Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA.
Publication Date:
Volume Number:
Proc SPIE Int Soc Opt Eng. 2015 Mar; 9413:941313.
PubMed ID:
Cortical thickness, brain, cortex, MRI, primate, macaque, animal imaging, segmentation
Appears in Collections:
R01 EB004640/EB/NIBIB NIH HHS/United States
R01 MH091645/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
P50 MH078105/MH/NIMH NIH HHS/United States
P50 MH100029/MH/NIMH NIH HHS/United States
P30 HD003110/HD/NICHD NIH HHS/United States
U54 HD079124/HD/NICHD NIH HHS/United States
Generated Citation:
Oguz I., Styner M., Sanchez M., Shi Y., Sonka M. LOGISMOS-B for Primates: Primate Cortical Surface Reconstruction and Thickness Measurement. Proc SPIE Int Soc Opt Eng. 2015 Mar; 9413:941313. PMID: 26028802. PMCID: PMC4449148.
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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.

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