Literature DB >> 26140584

Semi-automated hippocampal segmentation in people with cognitive impairment using an age appropriate template for registration.

Bernd Merkel1,2, Christopher Steward1,2, Lucy Vivash3, Charles B Malpas4, Pramit Phal1,2, Bradford A Moffat5, Kay L Cox6, Kathryn A Ellis7, David J Ames7,8, Elizabeth V Cyarto8, Michelle M Y Lai8, Matthew J Sharman9, Cassandra Szoeke3, Colin L Masters10, Nicola T Lautenschlager7,11, Patricia Desmond1,2.   

Abstract

BACKGROUND: To evaluate a new semi-automated segmentation method for calculating hippocampal volumes and to compare results with standard software tools in a cohort of people with subjective memory complaints (SMC) and mild cognitive impairment (MCI).
METHODS: Data from 58 participants, 39 with SMC (17 male, 22 female, mean age 72.6) and 19 with MCI (6 male, 13 female, mean age 74.3), were analyzed. For each participant, T1-weighted images were acquired using an MPRAGE sequence on a 3 Tesla MRI system. Hippocampal volumes (left, right, and total) were calculated with a new, age appropriate registration template, based on older people and using the advanced software tool ANTs (Advanced Normalization Tools). The results were compared with manual tracing (seen as the reference standard) and two widely accepted automated software tools (FSL, FreeSurfer).
RESULTS: The hippocampal volumes, calculated by using the age appropriate registration template were significantly (P < 0.05) more accurate (mean volume accuracy more than 90%) than those obtained with FreeSurfer and FSL (both less than 70%). Dice coefficients for the hippocampal segmentations with the new template method (75.3%) were slightly, but significantly (P < 0.05) higher than those from FreeSurfer (72.4%).
CONCLUSION: These results suggest that an age appropriate registration template might be a more accurate alternative to calculate hippocampal volumes when manual segmentation is not feasible.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  Alzheimer's disease; hippocampus; magnetic resonance imaging; mild cognitive impairment; segmentation; subjective memory complaints

Mesh:

Year:  2015        PMID: 26140584     DOI: 10.1002/jmri.24966

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  7 in total

1.  Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients.

Authors:  Mohammad-Parsa Hosseini; Mohammad-Reza Nazem-Zadeh; Dario Pompili; Kourosh Jafari-Khouzani; Kost Elisevich; Hamid Soltanian-Zadeh
Journal:  Med Phys       Date:  2016-01       Impact factor: 4.071

2.  Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans.

Authors:  Shan Cong; Shannon L Risacher; John D West; Yu-Chien Wu; Liana G Apostolova; Eileen Tallman; Maher Rizkalla; Paul Salama; Andrew J Saykin; Li Shen
Journal:  Brain Imaging Behav       Date:  2018-12       Impact factor: 3.978

3.  Translational MRI Volumetry with NeuroQuant: Effects of Version and Normative Data on Relationships with Memory Performance in Healthy Older Adults and Patients with Mild Cognitive Impairment.

Authors:  Julija Stelmokas; Lance Yassay; Bruno Giordani; Hiroko H Dodge; Ivo D Dinov; Arijit Bhaumik; K Sathian; Benjamin M Hampstead
Journal:  J Alzheimers Dis       Date:  2017       Impact factor: 4.472

4.  Building a Surface Atlas of Hippocampal Subfields From High Resolution T2-weighted MRI Scans Using Landmark-free Surface Registration.

Authors:  Shan Cong; Maher Rizkalla; Paul Salama; Shannon L Risacher; John D West; Yu-Chien Wu; Liana Apostolova; Eileen Tallman; Andrew J Saykin; Li Shen
Journal:  Conf Proc (Midwest Symp Circuits Syst)       Date:  2017-03-06

5.  Comparison of accuracy between FSL's FIRST and Freesurfer for caudate nucleus and putamen segmentation.

Authors:  Gabor Perlaki; Reka Horvath; Szilvia Anett Nagy; Peter Bogner; Tamas Doczi; Jozsef Janszky; Gergely Orsi
Journal:  Sci Rep       Date:  2017-05-25       Impact factor: 4.379

6.  Accuracy of automated amygdala MRI segmentation approaches in Huntington's disease in the IMAGE-HD cohort.

Authors:  Bonnie Alexander; Nellie Georgiou-Karistianis; Richard Beare; Lotta M Ahveninen; Valentina Lorenzetti; Julie C Stout; Yifat Glikmann-Johnston
Journal:  Hum Brain Mapp       Date:  2020-02-07       Impact factor: 5.038

7.  Robustness of radiomics to variations in segmentation methods in multimodal brain MRI.

Authors:  M G Poirot; M W A Caan; H G Ruhe; A Bjørnerud; I Groote; L Reneman; H A Marquering
Journal:  Sci Rep       Date:  2022-10-06       Impact factor: 4.996

  7 in total

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