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Author Notes:

The authors would like to thank Marc Niethammer and Joohwi Lee for the Laplacian-based thickness implementation.

Subjects:

Research Funding:

This research was funded, in part, by the following NIH grants: R01-EB004640, R01 MH091645-02, U54 EB005149, P50 MH078105- 01A2S1, P50 MH078105-01, P50 MH100029, P30 HD003110, U54 HD079124.

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

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

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Journal Title:

Proceedings of SPIE

Volume:

Volume 9413

Publisher:

Type of Work:

Article | Post-print: After Peer Review

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.

Copyright information:

Copyright 2015 Society of Photo Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited.

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