{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T20:54:41Z","timestamp":1760043281910,"version":"3.41.2"},"reference-count":45,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Neurosci."],"abstract":"<jats:p>Brain extraction (skull stripping) is an essential step in the magnetic resonance imaging (MRI) analysis of brain sciences. However, most of the current brain extraction methods that achieve satisfactory results for human brains are often challenged by non-human primate brains. Due to the small sample characteristics and the nature of thick-slice scanning of macaque MRI data, traditional deep convolutional neural networks (DCNNs) are unable to obtain excellent results. To overcome this challenge, this study proposed a symmetrical end-to-end trainable hybrid convolutional neural network (HC-Net). It makes full use of the spatial information between adjacent slices of the MRI image sequence and combines three consecutive slices from three axes for 3D convolutions, which reduces the calculation consumption and promotes accuracy. The HC-Net consists of encoding and decoding structures of 3D convolutions and 2D convolutions in series. The effective use of 2D convolutions and 3D convolutions relieves the underfitting of 2D convolutions to spatial features and the overfitting of 3D convolutions to small samples. After evaluating macaque brain data from different sites, the results showed that HC-Net performed better in inference time (approximately 13 s per volume) and accuracy (mean Dice coefficient reached 95.46%). The HC-Net model also had good generalization ability and stability in different modes of brain extraction tasks.<\/jats:p>","DOI":"10.3389\/fncom.2023.1113381","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T11:04:54Z","timestamp":1675940694000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["HC-Net: A hybrid convolutional network for non-human primate brain extraction"],"prefix":"10.3389","volume":"17","author":[{"given":"Hong","family":"Fei","sequence":"first","affiliation":[]},{"given":"Qianshan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Fangxin","family":"Shang","sequence":"additional","affiliation":[]},{"given":"Wenyi","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xiaofeng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yifei","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haifang","family":"Li","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.116800","article-title":"Towards HCP-Style macaque connectomes: 24-Channel 3T multi-array coil, MRI sequences and preprocessing.","volume":"215","author":"Autio","year":"2020","journal-title":"Neuroimage"},{"key":"B2","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: a deep convolutional encoder-decoder architecture for image segmentation.","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell"},{"key":"B3","doi-asserted-by":"publisher","first-page":"3799","DOI":"10.1523\/JNEUROSCI.2727-19.2020","article-title":"MECP2 duplication causes aberrant GABA pathways, circuits and behaviors in transgenic monkeys: neural mappings to patients with autism.","volume":"40","author":"Cai","year":"2020","journal-title":"J. Neurosci"},{"key":"B4","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00371-021-02326-9","article-title":"Whole brain segmentation method from 2.5D brain MRI slice image based on Triple U-Net.","volume":"39","author":"Chen","year":"2021","journal-title":"Vis. Comput."},{"key":"B5","first-page":"424","article-title":"3D U-Net: learning dense volumetric segmentation from sparse annotation","author":"\u00c7i\u00e7ek","year":"2016","journal-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"B6","first-page":"1337","article-title":"Deep learning with COTS HPC systems","author":"Coates","year":"2013","journal-title":"Proceedings of the International Conference on Machine Learning 2013"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106563","article-title":"Patch-based 3D U-Net and transfer learning for longitudinal piglet brain segmentation on MRI.","volume":"214","author":"Coupeau","year":"2022","journal-title":"Comput. Methods Programs Biomed"},{"key":"B8","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1006\/cbmr.1996.0014","article-title":"AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.","volume":"29","author":"Cox","year":"1996","journal-title":"Comput. Biomed Res"},{"key":"B9","doi-asserted-by":"publisher","first-page":"3829","DOI":"10.1093\/cercor\/bhx244","article-title":"Structural variability across the primate brain: a cross-species comparison.","volume":"28","author":"Croxson","year":"2018","journal-title":"Cereb. Cortex"},{"key":"B10","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.neuroimage.2017.04.039","article-title":"3D fully convolutional networks for subcortical segmentation in MRI: a large-scale study.","volume":"170","author":"Dolz","year":"2018","journal-title":"Neuroimage"},{"key":"B11","doi-asserted-by":"publisher","first-page":"6758","DOI":"10.1523\/Jneurosci.0493-16.2016","article-title":"Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey.","volume":"36","author":"Donahue","year":"2016","journal-title":"J. Neurosci"},{"key":"B12","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1038\/s41592-018-0235-4","article-title":"fMRIPrep: a robust preprocessing pipeline for functional MRI.","volume":"16","author":"Esteban","year":"2019","journal-title":"Nat. Methods"},{"key":"B13","first-page":"153","article-title":"Motor and communicative correlates of the inferior frontal gyrus (Broca\u2019s Area) in chimpanzees","author":"Hopkins","year":"2018","journal-title":"Origins of Human Language: Continuities and Discontinuities with Nonhuman Primates"},{"key":"B14","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1159\/000362431","article-title":"Evolution of the central sulcus morphology in primates.","volume":"84","author":"Hopkins","year":"2014","journal-title":"Brain Behav. Evol"},{"key":"B15","first-page":"1055","article-title":"Unet 3+: a full-scale connected unet for medical image segmentation","author":"Huang","year":"2020","journal-title":"Proceedings of the 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106230","article-title":"Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET\/CT images.","volume":"151","author":"Huang","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"B17","article-title":"3D U-Net for skull stripping in brain MRI.","volume":"9","author":"Hwang","year":"2019","journal-title":"Appl. Sci. Basel"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.117997","article-title":"A comprehensive macaque fMRI pipeline and hierarchical atlas.","volume":"235","author":"Jung","year":"2021","journal-title":"Neuroimage"},{"key":"B19","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.neuroimage.2016.01.024","article-title":"Deep MRI brain extraction: a 3D convolutional neural network for skull stripping.","volume":"129","author":"Kleesiek","year":"2016","journal-title":"Neuroimage"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117622","article-title":"CIVET-Macaque: an automated pipeline for MRI-based cortical surface generation and cortical thickness in macaques.","volume":"227","author":"Lepage","year":"2021","journal-title":"Neuroimage"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2019.00059","article-title":"Distinct mechanism of audiovisual integration with informative and uninformative sound in a visual detection task: a DCM study.","volume":"13","author":"Li","year":"2019","journal-title":"Front. Comput. Neurosci"},{"key":"B22","doi-asserted-by":"publisher","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: hybrid densely connected UNet for Liver and Tumor Segmentation From CT Volumes.","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"key":"B23","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1016\/j.cell.2018.01.020","article-title":"Cloning of macaque monkeys by somatic cell nuclear transfer.","volume":"172","author":"Liu","year":"2018","journal-title":"Cell"},{"key":"B24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-48489-3","article-title":"atlasBREX: automated template-derived brain extraction in animal MRI.","volume":"9","author":"Lohmeier","year":"2019","journal-title":"Sci. Rep"},{"key":"B25","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.artmed.2019.06.008","article-title":"Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks.","volume":"98","author":"Lucena","year":"2019","journal-title":"Artif. Intell. Med"},{"key":"B26","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.neuron.2018.08.039.","article-title":"An open resource for non-human primate imaging.","volume":"100","author":"Milham","year":"2018","journal-title":"Neuron"},{"key":"B27","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1109\/3dv.2016.79","article-title":"V-Net: fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016","journal-title":"Proceedings of 2016 Fourth International Conference on 3d Vision (3dv)"},{"key":"B28","doi-asserted-by":"publisher","first-page":"2497","DOI":"10.1073\/pnas.051611498","article-title":"Estimation of divergence times from multiprotein sequences for a few mammalian species and several distantly related organisms.","volume":"98","author":"Nei","year":"2001","journal-title":"Proc. Natl. Acad. Sci"},{"key":"B29","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1007\/978-3-642-40763-5_31","article-title":"Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network","author":"Prasoon","year":"2013","journal-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"B30","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2-Net: going deeper with nested U-structure for salient object detection.","volume":"106","author":"Qin","year":"2020","journal-title":"Patt. Recognit"},{"key":"B31","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015","journal-title":"Proceedings of the International Conference on Medical image Computing and Computer-Assisted Intervention"},{"key":"B32","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: an overview.","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw"},{"key":"B33","doi-asserted-by":"publisher","first-page":"2168","DOI":"10.1109\/ICCV.2013.269","article-title":"Image segmentation with cascaded hierarchical models and logistic disjunctive normal networks","author":"Seyedhosseini","year":"2013","journal-title":"Proceedings of the 2013 IEEE International Conference on Computer Vision"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106203","article-title":"UCR-Net: U-shaped context residual network for medical image segmentation.","volume":"151","author":"Sun","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116353","article-title":"Pypreclin: an automatic pipeline for macaque functional MRI preprocessing.","volume":"207","author":"Tasserie","year":"2020","journal-title":"Neuroimage"},{"key":"B36","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1002\/mrm.20708","article-title":"B1 destructive interferences and spatial phase patterns at 7 T with a head transceiver array coil.","volume":"54","author":"Van de Moortele","year":"2005","journal-title":"Magn. Reson. Med"},{"key":"B37","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1016\/j.neuroimage.2012.02.018","article-title":"The Human Connectome Project: a data acquisition perspective.","volume":"62","author":"Van Essen","year":"2012","journal-title":"Neuroimage"},{"key":"B38","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci12020260","article-title":"A macaque brain extraction model based on U-Net combined with residual structure.","volume":"12","author":"Wang","year":"2022","journal-title":"Brain Sci."},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118001","article-title":"U-net model for brain extraction: trained on humans for transfer to non-human primates.","volume":"235","author":"Wang","year":"2021","journal-title":"Neuroimage"},{"key":"B40","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0221185","article-title":"Cortical network underlying audiovisual semantic integration and modulation of attention: an fMRI and graph-based study.","volume":"14","author":"Xi","year":"","journal-title":"PLoS One"},{"key":"B41","doi-asserted-by":"publisher","DOI":"10.3389\/fnint.2019.00067","article-title":"Optimized configuration of functional brain network for processing semantic audio visual stimuli underlying the modulation of attention: a graph-based study.","volume":"13","author":"Xi","year":"","journal-title":"Front. Integr. Neurosci"},{"key":"B42","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning.","volume":"521","author":"Yan","year":"2015","journal-title":"Nature"},{"key":"B43","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1007\/978-3-030-32248-9_38","article-title":"Multiple sclerosis lesion segmentation with tiramisu and 2.5 d stacked slices","author":"Zhang","year":"2019","journal-title":"Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"B44","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1007\/BF02215449","article-title":"Phylogeny of rheusus monkeys (Macaca mulatta) as revealed by mitochondrial DNA restriction enzyme analysis.","volume":"14","author":"Zhang","year":"1993","journal-title":"Int. J. Primatol."},{"key":"B45","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.neuroimage.2018.03.065","article-title":"Bayesian convolutional neural network based MRI brain extraction on nonhuman primates.","volume":"175","author":"Zhao","year":"2018","journal-title":"Neuroimage"}],"container-title":["Frontiers in Computational Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2023.1113381\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T11:05:07Z","timestamp":1675940707000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fncom.2023.1113381\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,9]]},"references-count":45,"alternative-id":["10.3389\/fncom.2023.1113381"],"URL":"https:\/\/doi.org\/10.3389\/fncom.2023.1113381","relation":{},"ISSN":["1662-5188"],"issn-type":[{"type":"electronic","value":"1662-5188"}],"subject":[],"published":{"date-parts":[[2023,2,9]]},"article-number":"1113381"}}