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Mapping fetal brain development based on automated segmentation and 4D brain atlasing

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Abstract

Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23 and 36 weeks of gestation. The proposed pipeline enabled the fully automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for the quantitative characterization of fetal brain development, especially in the Chinese population.

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Availability of data and material

The data (anonymized) in this study can be accessed with a data-sharing agreement.

Code availability

The code used in this work is available from the authors upon request.

References

  • Alexander B, Murray AL, Loh WY, Matthews LG, Adamson C, Beare R, Chen J, Kelly CE, Rees S, Warfield SK (2017) A new neonatal cortical and subcortical brain atlas: the Melbourne Children’s Regional Infant Brain (M-CRIB) atlas. Neuroimage 147:841–851

    Article  PubMed  Google Scholar 

  • Avants BB, Epstein CL, Grossman M, Gee JC (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12(1):26–41

    Article  CAS  PubMed  Google Scholar 

  • Bendersky M, Musolino PL, Rugilo C, Schuster G, Sica RE (2006) Normal anatomy of the developing fetal brain Ex vivo anatomical–magnetic resonance imaging correlation. J Neurol Sci 250(1–2):20–26

    Article  PubMed  Google Scholar 

  • Breu M, Reisinger D, Wu D, Zhang Y, Fatemi A, Zhang J (2013) In vivo diffusion tensor imaging of the neonatal rat brain development. Neuropediatrics 44(S01):A11

    Google Scholar 

  • Chartier AL, Bouvier MJ, McPherson DR, Stepenosky JE, Taysom DA, Marks RM (2019) The safety of maternal and fetal MRI at 3 T. Am J Roentgenol 213(5):1170–1173

    Article  Google Scholar 

  • Chee MW, Chen KH, Zheng H, Chan KP, Isaac V, Sim SK, Chuah LY, Schuchinsky M, Fischl B, Ng TP (2009) Cognitive function and brain structure correlations in healthy elderly East Asians. Neuroimage 46(1):257–269

    Article  PubMed  Google Scholar 

  • Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3):162–173

    Article  CAS  PubMed  Google Scholar 

  • Ebner M, Chung KK, Prados F, Cardoso MJ, Chard DT, Vercauteren T, Ourselin S (2018a) Volumetric reconstruction from printed films: enabling 30 year longitudinal analysis in MR neuroimaging. NeuroImage 165:238–250

    Article  PubMed  PubMed Central  Google Scholar 

  • Ebner M, Wang G, Li W, Aertsen M, Patel PA, Aughwane R, Melbourne A, Doel T, David AL, Deprest J (2018b) An automated localization, segmentation and reconstruction framework for fetal brain MRI. International conference on medical image computing and computer-assisted intervention. Springer, pp 313–320

  • Ebner M, Wang G, Li W, Aertsen M, Patel PA, Aughwane R, Melbourne A, Doel T, Dymarkowski S, De Coppi P (2020) An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage 206:116324

    Article  PubMed  PubMed Central  Google Scholar 

  • Fogliarini C, Chaumoitre K, Chapon F, Fernandez C, Lévrier O, Figarella-Branger D, Girard N (2005) Assessment of cortical maturation with prenatal MRI. Part I: Normal Cortical Matur 15(8):1671–1685

    Google Scholar 

  • Garel C, Chantrel E, Brisse H, Elmaleh M, Luton D, Oury J-F, Sebag G, Hassan M (2001) Fetal cerebral cortex: normal gestational landmarks identified using prenatal MR imaging. Am J Neuroradiol 22(1):184–189

    CAS  PubMed  Google Scholar 

  • Garel C, Chantrel E, Elmaleh M, Brisse H, Sebag G (2003) Fetal MRI: normal gestational landmarks for cerebral biometry, gyration and myelination. Childs Nerv Syst 19(7–8):422–425

    Article  PubMed  Google Scholar 

  • Gholipour A, Estroff JA, Warfield SK (2010) Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI. IEEE Trans Med Imaging 29(10):1739–1758

    Article  PubMed  PubMed Central  Google Scholar 

  • Gholipour A, Limperopoulos C, Clancy S, Clouchoux C, Akhondi-Asl A, Estroff JA, Warfield SK (2014) Construction of a deformable spatiotemporal MRI atlas of the fetal brain: evaluation of similarity metrics and deformation models. International conference on medical image computing and computer-assisted intervention. Springer, pp 292–299

    Google Scholar 

  • Gholipour A, Rollins CK, Velasco-Annis C, Ouaalam A, Akhondi-Asl A, Afacan O, Ortinau CM, Clancy S, Limperopoulos C, Yang E (2017) A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci Rep 7(1):476

    Article  PubMed  PubMed Central  Google Scholar 

  • Glenn OA (2006) Fetal central nervous system MR imaging. Neuroimaging Clinics 16(1):1–17

    Article  PubMed  Google Scholar 

  • Gousias IS, Edwards AD, Rutherford MA, Counsell SJ, Hajnal JV, Rueckert D, Hammers A (2012) Magnetic resonance imaging of the newborn brain: manual segmentation of labelled atlases in term-born and preterm infants. Neuroimage 62(3):1499–1509

    Article  PubMed  Google Scholar 

  • Griffiths PD, Bradburn M, Campbell MJ, Cooper CL, Graham R, Jarvis D, Kilby MD, Mason G, Mooney C, Robson SC (2017) Use of MRI in the diagnosis of fetal brain abnormalities in utero (MERIDIAN): a multicentre, prospective cohort study. Lancet 389(10068):538–546

    Article  PubMed  Google Scholar 

  • Habas PA, Kim K, Corbett-Detig JM, Rousseau F, Glenn OA, Barkovich AJ, Studholme C (2010) A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation. Neuroimage 53(2):460–470

    Article  PubMed  PubMed Central  Google Scholar 

  • Iglesias JE, Liu C-Y, Thompson PM, Tu Z (2011) Robust brain extraction across datasets and comparison with publicly available methods. IEEE Trans Med Imaging 30(9):1617–1634

    Article  PubMed  Google Scholar 

  • Jarvis DA, Griffiths PD (2019) Current state of MRI of the fetal brain in utero. J Magn Reson Imaging 49(3):632–646

    Article  PubMed  Google Scholar 

  • Jenkinson M, Pechaud M, Smith S (2005) BET2: MR-based estimation of brain, skull and scalp surfaces. In: Eleventh annual meeting of the organization for human brain mapping, vol 17, pp 167

  • Jiang S, Xue H, Glover A, Rutherford M, Rueckert D, Hajnal JV (2007) MRI of moving subjects using multislice snapshot images with volume reconstruction (SVR): application to fetal, neonatal, and adult brain studies. IEEE Trans Med Imaging 26(7):967–980

    Article  PubMed  Google Scholar 

  • Kainz B, Steinberger M, Wein W, Kuklisova-Murgasova M, Malamateniou C, Keraudren K, Torsney-Weir T, Rutherford M, Aljabar P, Hajnal JV (2015) Fast volume reconstruction from motion corrupted stacks of 2D slices. IEEE Trans Med Imaging 34(9):1901–1913

    Article  PubMed  PubMed Central  Google Scholar 

  • Khalili N, Lessmann N, Turk E, Claessens N, de Heus R, Kolk T, Viergever M, Benders M, Išgum I (2019) Automatic brain tissue segmentation in fetal MRI using convolutional neural networks. Mag Reson Imaging 64(77):89

    Google Scholar 

  • Khan S, Vasung L, Marami B, Rollins CK, Afacan O, Ortinau CM, Yang E, Warfield SK, Gholipour A (2019) Fetal brain growth portrayed by a spatiotemporal diffusion tensor MRI atlas computed from in utero images. Neuroimage 185:593–608

    Article  PubMed  Google Scholar 

  • Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, Biller A (2016) Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. Neuroimage 129:460–469

    Article  PubMed  Google Scholar 

  • Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang M-C, Christensen GE, Collins DL, Gee J, Hellier P (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3):786–802

    Article  PubMed  PubMed Central  Google Scholar 

  • Kochunov P, Castro C, Davis D, Dudley D, Brewer J, Zhang Y, Kroenke CD, Purdy D, Fox PT, Simerly C (2010) Mapping primary gyrogenesis during fetal development in primate brains: high-resolution in utero structural MRI study of fetal brain development in pregnant baboons. Front Neurosci 4:20

    PubMed  PubMed Central  Google Scholar 

  • Kuklisova-Murgasova M, Quaghebeur G, Rutherford MA, Hajnal JV, Schnabel JA (2012) Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med Image Anal 16(8):1550–1564

    Article  PubMed  PubMed Central  Google Scholar 

  • Lee JS, Lee DS, Kim J, Kim YK, Kang E, Kang H, Kang KW, Lee JM, Kim J-J, Park H-J (2005) Development of Korean standard brain templates. J Korean Med Sci 20(3):483–488

    Article  PubMed  PubMed Central  Google Scholar 

  • Liang P, Shi L, Chen N, Luo Y, Wang X, Liu K, Mok VC, Chu WC, Wang D, Li K (2015) Construction of brain atlases based on a multi-center MRI dataset of 2020 Chinese adults. Sci Rep 5:18216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Lin G, Adiga U, Olson K, Guzowski JF, Barnes CA, Roysam B (2003) A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytom Part A: J Int Soc Anal Cytol 56(1):23–36

    Article  Google Scholar 

  • Makropoulos A, Gousias IS, Ledig C, Aljabar P, Serag A, Hajnal JV, Edwards AD, Counsell SJ, Rueckert D (2014) Automatic whole brain MRI segmentation of the developing neonatal brain. IEEE Trans Med Imaging 33(9):1818–1831

    Article  PubMed  Google Scholar 

  • Makropoulos A, Aljabar P, Wright R, Hüning B, Merchant N, Arichi T, Tusor N, Hajnal JV, Edwards AD, Counsell SJ (2016) Regional growth and atlasing of the developing human brain. Neuroimage 125:456–478

    Article  PubMed  PubMed Central  Google Scholar 

  • Makropoulos A, Counsell SJ, Rueckert D (2018) A review on automatic fetal and neonatal brain MRI segmentation. Neuroimage 170:231–248

    Article  PubMed  Google Scholar 

  • Monteagudo A, Timor-Tritsch I (1997) Development of fetal gyri, sulci and fissures: a transvaginal sonographic study. Ultrasound Obstet Gynecol: off J Int Soc Ultrasound Obstet Gynecol 9(4):222–228

    Article  CAS  Google Scholar 

  • Nielsen BW, Scott RC (2017) Brain abnormalities in fetuses: in-utero MRI versus ultrasound. Lancet 389(10068):483–485

    Article  PubMed  Google Scholar 

  • Ou Y, Akbari H, Bilello M, Da X, Davatzikos C (2014) Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights. IEEE Trans Med Imaging 33(10):2039–2065

    Article  PubMed  PubMed Central  Google Scholar 

  • Rao NP, Jeelani H, Achalia R, Achalia G, Jacob A, dawn Bharath R, Varambally S, Venkatasubramanian G, Yalavarthy PK (2017) Population differences in brain morphology: Need for population specific brain template. Psychiatr Res: Neuroimaging 265:1–8

    Article  Google Scholar 

  • Rolo LC, Araujo E, Nardozza LMM, de Oliveira PS, Ajzen SA, Moron AF (2011) Development of fetal brain sulci and gyri: assessment through two and three-dimensional ultrasound and magnetic resonance imaging. Arch Gynecol Obstet 283(2):149–158

    Article  PubMed  Google Scholar 

  • Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. International conference on medical image computing and computer-assisted intervention. Springer, pp 234–241

    Google Scholar 

  • Rousseau F, Glenn OA, Iordanova B, Rodriguez-Carranza C, Vigneron DB, Barkovich JA, Studholme C (2006) Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. Acad Radiol 13(9):1072–1081

    Article  PubMed  Google Scholar 

  • Rousseau F, Kim K, Studholme C, Koob M, Dietemann J-L (2010) On super-resolution for fetal brain MRI. International conference on medical image computing and computer-assisted intervention. Springer, pp 355–362

    Google Scholar 

  • Rousseau F, Oubel E, Pontabry J, Schweitzer M, Studholme C, Koob M, Dietemann J-L (2013) BTK: An open-source toolkit for fetal brain MR image processing. Comput Methods Programs Biomed 109(1):65–73

    Article  PubMed  Google Scholar 

  • Salehi SSM, Erdogmus D, Gholipour A (2017) Auto-context convolutional neural network (auto-net) for brain extraction in magnetic resonance imaging. IEEE Trans Med Imaging 36(11):2319–2330

    Article  PubMed Central  Google Scholar 

  • Schuh A, Murgasova M, Makropoulos A, Ledig C, Counsell SJ, Hajnal JV, Aljabar P, Rueckert D (2014) Construction of a 4D brain atlas and growth model using diffeomorphic registration. International workshop on spatio-temporal image analysis for longitudinal and time-series image data. Springer, pp 27–37

    Google Scholar 

  • Schuh A, Makropoulos A, Robinson EC, Cordero-Grande L, Hughes E, Hutter J, Price AN, Murgasova M, Teixeira RPA, Tusor N (2018) Unbiased construction of a temporally consistent morphological atlas of neonatal brain development. https://doi.org/10.1101/251512

  • Scott JA, Habas PA, Kim K, Rajagopalan V, Hamzelou KS, Corbett-Detig JM, Barkovich AJ, Glenn OA, Studholme C (2011) Growth trajectories of the human fetal brain tissues estimated from 3D reconstructed in utero MRI. Int J Dev Neurosci 29(5):529–536

    Article  PubMed  PubMed Central  Google Scholar 

  • Serag A, Aljabar P, Ball G, Counsell SJ, Boardman JP, Rutherford MA, Edwards AD, Hajnal JV, Rueckert D (2012a) Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression. Neuroimage 59(3):2255–2265

    Article  PubMed  Google Scholar 

  • Serag A, Kyriakopoulou V, Rutherford M, Edwards A, Hajnal J, Aljabar P, Counsell S, Boardman J, Rueckert D (2012b) A multi-channel 4D probabilistic atlas of the developing brain: application to fetuses and neonates. Ann BMVA 2012(3):1–14

    Google Scholar 

  • Taimouri V, Gholipour A, Velasco-Annis C, Estroff JA, Warfield SK (2015) A template-to-slice block matching approach for automatic localization of brain in fetal MRI. In: 2015 IEEE 12th international symposium on biomedical imaging (ISBI). IEEE, pp 144–147

  • Tang Y, Hojatkashani C, Dinov ID, Sun B, Fan L, Lin X, Qi H, Hua X, Liu S, Toga AW (2010) The construction of a Chinese MRI brain atlas: a morphometric comparison study between Chinese and Caucasian cohorts. Neuroimage 51(1):33–41

    Article  PubMed  PubMed Central  Google Scholar 

  • Tourbier S, Velasco-Annis C, Taimouri V, Hagmann P, Meuli R, Warfield SK, Cuadra MB, Gholipour A (2017) Automated template-based brain localization and extraction for fetal brain MRI reconstruction. Neuroimage 155:460–472

    Article  PubMed  PubMed Central  Google Scholar 

  • Uchiyama HT, Seki A, Tanaka D, Koeda T (2013) A study of the standard brain in Japanese children: Morphological comparison with the MNI template. Brain Develop 35(3):228–235

    Article  Google Scholar 

  • Wang J, Perez L (2017) The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Network Vis Recognit 11

  • Weisstanner C, Kasprian G, Gruber G, Brugger P, Prayer D (2015) MRI of the fetal brain. Clin Neuroradiol 25(2):189–196

    Article  PubMed  Google Scholar 

  • Wright R, Kyriakopoulou V, Ledig C, Rutherford MA, Hajnal JV, Rueckert D, Aljabar P (2014) Automatic quantification of normal cortical folding patterns from fetal brain MRI. Neuroimage 91:21–32

    Article  CAS  PubMed  Google Scholar 

  • Wu D, Lei J, Rosenzweig JM, Burd I, Zhang J (2015) In utero localized diffusion MRI of the embryonic mouse brain microstructure and injury. J Mag Reson Imaging 42(3):717–728

    Article  Google Scholar 

  • Zhao L, Feng X, Meyer C, Wu Y, Plessis AJd, Limperopoulos C (2019a) Fetal brain automatic segmentation using 3D deep convolutional neural network. In: ISMRM 27th annual meeting, 2019, pp 11–16

  • Zhao T, Liao X, Fonov VS, Wang Q, Men W, Wang Y, Qin S, Tan S, Gao J-H, Evans A (2019b) Unbiased age-specific structural brain atlases for Chinese pediatric population. Neuroimage 189:55–70

    Article  PubMed  Google Scholar 

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Funding

This work was supported by the Ministry of Science and Technology of the People’s Republic of China (2018YFE0114600), National Natural Science Foundation of China (61801424, 81971606, 61801421, and 81971605), and the Leading Innovation and Entrepreneurship Team of Zhejiang Province (202006140).

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Authors and Affiliations

  1. Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China

    Haotian Li, Wanrong Luo, Tingting Liu, Yan Wang, Ruibin Liu, Weihao Zheng, Yi Zhang & Dan Wu

  2. Department of Radiology, School of Medicine, Women’s Hospital, Zhejiang University, Hangzhou, Zhejiang, China

    Guohui Yan, Kui Li & Yu Zou

  3. Department of Neurology, The First Affiliated Hospital, Zhejiang University, Hangzhou, China

    Yi Zhang

  4. Center for the Developing Brain, Diagnostic Imaging and Radiology, Children’s National Medical Center, Washington, DC, USA

    Li Zhao & Catherine Limperopoulos

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  1. Haotian Li
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  2. Guohui Yan
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  3. Wanrong Luo
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  4. Tingting Liu
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  5. Yan Wang
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  7. Weihao Zheng
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  8. Yi Zhang
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  9. Kui Li
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  10. Li Zhao
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  11. Catherine Limperopoulos
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  12. Yu Zou
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  13. Dan Wu
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Contributions

HL: processed MRI data and performed analyses. DW: was in charge of the study design and overall progress of the study. HL and DW: drafted the manuscript. GY, KL, and YZ: contributed to the collection of the MRI data. All authors contributed to the interpretation and review of the manuscript.

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Correspondence to Dan Wu.

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Li, H., Yan, G., Luo, W. et al. Mapping fetal brain development based on automated segmentation and 4D brain atlasing. Brain Struct Funct 226, 1961–1972 (2021). https://doi.org/10.1007/s00429-021-02303-x

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