Share：
Share this content in WeChat
X
[Chinese][PDF]1022209
• Clinical Article •
Primary study of automatic segmentation and measurement of brain region volumes applicating in Alzheimer's disease diagnosis
LI Huabin  TANG Xiangqi  CHEN Yuan  XIAO Huanhui  WANG Silun 

Cite this article as: Li HB, Tang XQ, Chen Y, et al. Primary study of automatic segmentation and measurement of brain region volumes applicating in Alzheimer’s disease diagnosis[J]. Chin J Magn Reson Imaging, 2021, 12(6): 27-33. DOI:10.12015/issn.1674-8034.2021.06.006.


[Abstract] Objective To preliminarily investigate an automatic quantitative method for determining brain regional volume, and for detecting brain regional alterations in Alzheimer's disease. And then to assess the reliability and clinical value for AD diagnosis of the automatic quantitative method. Materials andMethods Structural MRI images were collected from 204 enrolled healthy volunteers. Volume of bilateral hippocampus, bilateral ventricles, bilateral caudate nucleus, bilateral putamen, and bilateral thalamus, were respectively measured by manual segmentation of 2 experienced experts and automatic measurement software. The volumetric data of the 10 regions measured by two methods were compared. Moreover, 34 AD patients, with 34 age- and sex-matched normal controls, were enrolled in the study. Volumes of 47 main brain regions (including frontal lobe, temporal lobe, parietal lobe, occipital lobe, insular lobe, posterior cingulate etc.) of AD and control groups were assessed by automatic measurement software. The difference of absolute volumes and relative volumes (absolute volume/brain parenchymal volume) of brain regions between the two groups were compared, respectively.Results The results showed that the volume of 10 selected brain regions measured by automatic measurement software had significant correlation with those by manual segmentation of experienced experts (Pearson's r2＞0.9). Bland-Altman analysis revealed that the coefficients of variation of volume difference were less than 5%. On the other hand, volume of ventricle, including lateral ventricles, third ventricle, fourth ventricle, in AD group significantly expanded compared with control (P＜0.05), 32 other brain regions in AD group had significant absolute volume atrophy compared with normal control (P＜0.05). Compared with control, 11 brain regions, including medial prefrontal lobe (P=0.0009), middle temporal gyrus (P=0.0003), inferior temporal gyrus (P=0.0012), temporal pole (P=0.0093), angular gyrus (P=0.0030), precuneus (P=0.0052), posterior cingulate gyrus (P=0.0157), amygdala (P＜0.0001), hippocampus (P=0.0016), basal area (P=0.0022), entorhinal area (P=0.0003) in AD group had significant absolute and relative volume decrease.Conclusions The automatic segmentation software can accurately measure the volume of brain regions which has good consistency with. Assessment of brain structure changes by using automatic measurement software has potential AD diagnostic value.
[Keywords] automatic measurement software；Alzheimer's disease；brain atrophy；brain region segmentation；magnetic resonance imaging

LI Huabin1   TANG Xiangqi2   CHEN Yuan3   XIAO Huanhui3   WANG Silun3*  

1 Department of Radiology of Xiangya Second Hospital of Central South University, Changsha 410011, China

2 Department of Neurology of Xiangya Second Hospital of Central South University, Changsha 410011, China

3 Shenzhen Yiwei Medical Technology Co. LTD, Shenzhen 518082, China

Wang SL, E-mail: lawrence.wang@szdrbrain.com

Conflicts of interest   None.

Received  2020-09-14
Accepted  2020-11-25
DOI: 10.12015/issn.1674-8034.2021.06.006
Cite this article as: Li HB, Tang XQ, Chen Y, et al. Primary study of automatic segmentation and measurement of brain region volumes applicating in Alzheimer’s disease diagnosis[J]. Chin J Magn Reson Imaging, 2021, 12(6): 27-33. DOI:10.12015/issn.1674-8034.2021.06.006.

1
Prince M, Albanese E, Guerchet M, et al. World alzheimer report 2014: Dementia and risk reduction an analysis of protective and modifiable factors[J]. Technical Report, September 2014. DOI: 10.1084/jem.46.6.1007.
2
Schröder J, Pantel J. Neuroimaging of hippocampal atrophy in early recognition of Alzheimer's disease--a critical appraisal after two decades of research[J]. Psychiatry Res Neuroimaging, 2016, 247: 71-78. DOI: 10.1016/j.pscychresns.2015.08.014.
3
Li XS, Wang HB, Yu YQ, et al. Emotional memory related grey matter volum changes of Alzheimer's disease[J]. Chin J Med Imaging Technol, 2016, 32(7): 1015-1019. DOI: 10.13929/j.1003-3289.2016.07.006.
4
Xu YY, Qian XH, Deng L, et al. MRI analysis of hippocampus head, body and tail volume changes in progression of Alzheimer's disease[J]. Chin J Med Imaging Technol, 2017, 33(6): 853-858. DOI: 10.13929/j.1003-3289.201610043.
5
Cheng JM, Zhao XH, Men WW, et al. Voxel-based MRI study on brain gray matter of patients with mild congnitive impairment converting to Alzheimer's disease[J]. Chin J Med Imaging Technol, 2015, 31(1): 19-23. DOI: 10.13929/j.1003-3289.2015.01.005.
6
Jack CR, Shiung MM, Gunter JL, et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD[J]. Neurology, 2004, 62(4): 591-600. DOI: 10.1212/01.wnl.0000110315.26026.ef.
7
Huang R, Wang HB, Ge WW, et al. Quantitative MR evaluation of hippocampal volumes: comparison with Alzheimer's disease and mild cognitive impairment[J]. Chin J CT & MRI, 2018, 16(10): 35-38. DOI: 10.3969/j.issn.1672-5131.2018.10.011.
8
Bin Zahid A, Mikheev A, Srivatsa N, et al. Accelerated brain atrophy on serial computed tomography: Potential marker of the progression of Alzheimer disease[J]. J Comput Assist Tomogr, 2016, 40(5): 827-832. DOI: 10.1097/RCT.0000000000000435.
9
Godbolt AK, Cipolotti L, Anderson VM, et al. A decade of pre-diagnostic assessment in a case of familial Alzheimer's disease: tracking progression from asymptomatic to MCI and dementia[J]. Neurocase, 2005, 11(1): 56-64. DOI: 10.1080/13554790490896866.
10
Liang FJ, Xiao X, Xiao T, et al. Amygdala volume in 2-to 3-year-old children with autism spectrum disorder[J]. Chin J Psychiatry, 2014, 47(6): 359-363. DOI: 10.3760/cma.j.issn.1671-7368.2014.06.010.
11
Qin H, Chen N, Li KC. MRI measurement of occipital lobe volume in different genders of healthy Han Chinese adults[J]. Chin J Med Imaging Technol, 2020, 36(4): 514-518. DOI: 10.13929/j.issn.1003-3289.2020.04.008.
12
Agüera-Ortiz L, Hernandez-Tamames JA, Martinez-Martin P, et al. Structural correlates of apathy in Alzheimer's disease: a multimodal MRI study[J]. Int J Geriatr Psychiatry, 2017, 32(8): 922-930. DOI: 10.1002/gps.4548.
13
Du AT, Schuff N, Amend D, et al. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease[J]. J Neurol Neurosurg Psychiatry, 2001, 71(4): 441-447. DOI: 10.1136/jnnp.71.4.441.
14
Peng GP, Liu XY, Luo BY. Neuroimaging in the diagnosis and differentiation of Alzheimer's disease[J]. Chin J Neurology, 2020, 53(5): 321-327. DOI: 10.3760/cma.j.cn113694-20191107-00692.
15
Bartos A, Gregus D, Ibrahim I, Tintěra J. Brain volumes and their ratios in Alzheimer´s disease on magnetic resonance imaging segmented using Freesurfer 6.0[J]. Psychiatry Res Neuroimaging, 2019, 287: 70-74. DOI: 10.1016/j.pscychresns.2019.01.014.
16
Liu M, Li F, Yan H, et al. A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease[J]. Neuroimage, 2020, 208: 116459. DOI: 10.1016/j.neuroimage.2019.116459.
17
Shaikh TA, Ali R. Automated atrophy assessment for Alzheimer's disease diagnosis from brain MRI images[J]. Magn Reson Imaging, 2019, 62: 167-173. DOI: 10.1016/j.mri.2019.06.019.
18
Zeng XZ, Yuan HS, Liu Y, et al. Characteristics of cerebral blood flow and cerebral gray matter in patients with mild Alzheimer's disease using voxel-based method[J]. Chin J Med Imaging Technol, 2017, 25(2): 81-85. DOI: 10.3969/j.issn.1005-5185.2017.02.001.
19
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease[J]. Alzheimers Dement, 2011, 7(3): 263-269. DOI: 10.1016/j.jalz.2011.03.005. DOI: .
20
Tondelli M, Wilcock GK, Nichelli P, et al. Structural MRI changes detectable up to ten years before clinical Alzheimer's disease[J]. Neurobiol Aging, 2012, 33(4): 25-825. DOI: 10.1016/j.neurobiolaging.2011.05.018.
21
Chan D, Fox NC, Scahill RI, et al. Patterns of temporal lobe atrophy in semantic dementia and Alzheimer's disease[J]. Ann Neurol, 2001, 49(4): 433-442.
22
Cavedo E, Pievani M, Boccardi M, et al. Medial temporal atrophy in early and late-onset Alzheimer's disease[J]. Neurobiol Aging, 2014, 35(9): 2004-2012.
23
Li QQ, Du JC. The research progress on the change of entcrhinal cortex in Alzheimer's disease[J]. J Med Imaging, 2019, 29(5): 852-854.
24
Zhou M, Zhang F, Zhao L, et al. Entorhinal cortex: a good biomarker of mild cognitive impairment and mild Alzheimer's disease[J]. Rev Neurosci, 2016, 27(2): 185-195. DOI: 10.1515/revneuro-2015-0019.
25
Enkirch SJ, Traschütz A, Müller A, et al. The ERICA score: An MR imaging-based visual scoring system for the assessment of entorhinal cortex atrophy in Alzheimer disease[J]. Radiology, 2018, 288(1): 226-333. DOI: 10.1148/radiol.2018171888.
26
Migliaccio R, Agosta F, Possin KL, et al. Mapping the progression of atrophy in early-and late-onset Alzheimer's disease[J]. J Alzheimers Dis, 2015, 46(2): 351-364. DOI: 10.3233/JAD-142292.
27
Ribeiro LG, Filho Busatto G. Voxel-based morphometry in Alzheimers disease and mild cognitive impairment: Systematic review of studies addressing the frontal lobe[J]. Dement Neuropsychol, 2016, 10(2): 104-112. DOI: 10.1590/S1980-5764-2016DN1002006.
28
Ten Kate M, Dicks E, Visser PJ, et al. Atrophy subtypes in prodromal Alzheimer's disease are associated with cognitive decline[J]. Brain, 2018, 141(12): 3443-3456. DOI: 10.1093/brain/awy264.
29
Pengas G, Hodges JR, Watson P, et al. Focal posterior cingulate atrophy in incipient Alzheimer's disease[J]. Neurobiol Aging, 2010, 31(1): 25-33. DOI: 10.1016/j.neurobiolaging.2008.03.014.
30
Teipel SJ, Flatz WH, Heinsen H, et al. Measurement of basal forebrain atrophy in Alzheimer's disease using MRI[J]. Brain, 2005, 128(Pt 11): 2626-2644. DOI: 10.1093/brain/awh589.
31
Baker-Nigh A, Vahedi S, Davis EG, et al. Neuronal amyloid-β accumulation within cholinergic basal forebrain in ageing and Alzheimer's disease[J]. Brain, 2015, 138(Pt6): 1722-1737. DOI: 10.1093/brain/awv024.
32
Kilimann I, Grothe M, Heinsen H, et al. Subregional basal forebrain atrophy in Alzheimer's disease: a multicenter study[J]. J Alzheimers Dis, 2014, 40(3): 687-700. DOI: 10.3233/JAD-132345.
33
Lehmann M, Douiri A, Kim LG, et al. Atrophy patterns in Alzheimer's disease and semantic dementia: a comparison of FreeSurfer and manual volumetric measurements[J]. Neuroimagem, 2010, 49(3): 2264-2274. DOI: 10.1016/j.neuroimage.2009.10.056.
34
Matsuda H, Yokoyama K, Sato N, et al. Differentiation between dementia with lewy bodies and Alzheimer's disease using voxel-based morphometry of structural MRI: A multicenter study[J]. Neuropsychiatr Dis Treat, 2019, 15: 2715-2722. DOI: 10.2147/NDT.S222966.
35
Marchewka A, Kherif F, Krueger G, et al. Alzheimer's Disease Neuroimaging Initiative. Influence of magnetic field strength and image registration strategy on voxel-based morphometry in a study of Alzheimer's disease[J]. Hum Brain Mapp, 2014, 35(5): 1865-1874. DOI: 10.1002/hbm.22297.
36
Mulder ER, de Jong RA, Knol DL, et al. Alzheimer's Disease Neuroimaging Initiative. Hippocampal volume change measurement: quantitative assessment of the reproducibility of expert manual outlining and the automated methods FreeSurfer and FIRST[J]. Neuroimage, 2014, 92: 169-81. DOI: 10.1016/j.neuroimage.2014.01.058.
37
Brain Development Cooperative Group. Total and regional brain volumes in a population-based normative sample from 4 to 18 years: the NIH MRI Study of Normal Brain Development[J]. Cereb Cortex, 2012, 22(1): 1-12. DOI: 10.1093/cercor/bhr018.

PREV Changes of iron in deep gray matter nuclei of children aged 0—6 years: Quantitative susceptibility mapping versus R2*
NEXT Study on the relationship between epicardial adipose tissue and left ventricular remodeling in hypertrophic cardiomyopathy based on T1 mapping technology
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn