Fetching the paper…
Reading the bibliography…
Fetal cortical plate segmentation is essential in quantitative analysis of fetal brain maturation and cortical folding.
F. V. Coakley, O. A. Glenn, A. Qayyum, A. J. Barkovich, R. Goldstein, and R. A. Filly, “Fetal MRI: a developing technique for the developing patient,” American Journal of Roentgenology , vol. 182, no. 1, pp. 243–252, 2004
2004
Earlier work this paper cites.
H. Xue, L. Srinivasan, S. Jiang, M. Rutherford, A. D. Edwards, D. Rueckert et al. , “Automatic segmentation and reconstruction of the cortex from neonatal MRI,” Neuroimage , vol. 38, no. 3, pp. 461–477, 2007
2007
Earlier work this paper cites.
P. A. Habas, K. Kim, F. Rousseau, O. A. Glenn, A. J. Barkovich, and C. Studholme, “Atlas-based segmentation of the germinal matrix from in utero clinical MRI of the fetal brain,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2008, pp. 351–358
2008
Earlier work this paper cites.
N. I. Weisenfeld and S. K. Warfield, “Automatic segmentation of newborn brain MRI,” Neuroimage , vol. 47, no. 2, pp. 564–572, 2009
2009
Earlier work this paper cites.
P. A. Habas, K. Kim, D. Chandramohan, F. Rousseau, O. A. Glenn, and C. Studholme, “Statistical model of laminar structure for atlas-based segmentation of the fetal brain from in utero mr images,” in Medical Imaging 2009: Image Processing , vol. 7259. International Society for Optics and Photonics, 2009, p. 725917
2009
Earlier work this paper cites.
H.-H. Chang, A. H. Zhuang, D. J. Valentino, and W.-C. Chu, “Performance measure characterization for evaluating neuroimage segmentation algorithms,” Neuroimage , vol. 47, no. 1, pp. 122–135, 2009
2009
Earlier work this paper cites.
A. Gholipour, J. A. Estroff, and S. K. Warfield, “Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI,” IEEE transactions on medical imaging , vol. 29, no. 10, pp. 1739–1758, 2010
2010
Earlier work this paper cites.
P. A. Habas, K. Kim, F. Rousseau, O. A. Glenn, A. J. Barkovich, and C. Studholme, “Atlas-based segmentation of developing tissues in the human brain with quantitative validation in young fetuses,” Human brain mapping , vol. 31, no. 9, pp. 1348–1358, 2010
2010
Earlier work this paper cites.
P. A. Habas, K. Kim, J. M. Corbett-Detig, F. Rousseau, O. A. Glenn, A. J. Barkovich et al. , “A spatiotemporal atlas of mr intensity, tissue probability and shape of the fetal brain with application to segmentation,” Neuroimage , vol. 53, no. 2, pp. 460–470, 2010
2010
Earlier work this paper cites.
V. Rajagopalan, J. Scott, P. A. Habas, K. Kim, J. Corbett-Detig, F. Rousseau et al. , “Local tissue growth patterns underlying normal fetal human brain gyrification quantified in utero,” Journal of neuroscience , vol. 31, no. 8, pp. 2878–2887, 2011
2011
Earlier work this paper cites.
J. A. Scott, P. A. Habas, K. Kim, V. Rajagopalan, K. S. Hamzelou, J. M. Corbett-Detig et al. , “Growth trajectories of the human fetal brain tissues estimated from 3d reconstructed in utero MRI,” International Journal of Developmental Neuroscience , vol. 29, no. 5, pp. 529–536, 2011
2011
Earlier work this paper cites.
J. Corbett-Detig, P. Habas, J. A. Scott, K. Kim, V. Rajagopalan, P. McQuillen et al. , “3d global and regional patterns of human fetal subplate growth determined in utero,” Brain Structure and Function , vol. 215, no. 3-4, pp. 255–263, 2011
2011
Earlier work this paper cites.
C. Studholme, “Mapping fetal brain development in utero using magnetic resonance imaging: the big bang of brain mapping,” Annual review of biomedical engineering , vol. 13, pp. 345–368, 2011
2011
Earlier work this paper cites.
B. B. Avants, N. J. Tustison, G. Song, P. A. Cook, A. Klein, and J. C. Gee, “A reproducible evaluation of ants similarity metric performance in brain image registration,” Neuroimage , vol. 54, no. 3, pp. 2033–2044, 2011
2011
Earlier work this paper cites.
C. Clouchoux, D. Kudelski, A. Gholipour, S. K. Warfield, S. Viseur, M. Bouyssi-Kobar et al. , “Quantitative in vivo MRI measurement of cortical development in the fetus,” Brain Structure and Function , vol. 217, no. 1, pp. 127–139, 2012
2012
Earlier work this paper cites.
M. Kuklisova-Murgasova, G. Quaghebeur, M. A. Rutherford, J. V. Hajnal, and J. A. Schnabel, “Reconstruction of fetal brain MRI with intensity matching and complete outlier removal,” Medical image analysis , vol. 16, no. 8, pp. 1550–1564, 2012
2012
Earlier work this paper cites.
A. Serag, V. Kyriakopoulou, M. Rutherford, A. Edwards, J. Hajnal, P. Aljabar et al. , “A multi-channel 4d probabilistic atlas of the developing brain: application to fetuses and neonates,” Annals of the BMVA , vol. 2012, no. 3, pp. 1–14, 2012
2012
Earlier work this paper cites.
A. Gholipour, A. Akhondi-Asl, J. A. Estroff, and S. K. Warfield, “Multi-atlas multi-shape segmentation of fetal brain MRI for volumetric and morphometric analysis of ventriculomegaly,” NeuroImage , vol. 60, no. 3, pp. 1819–1831, 2012
2012
Earlier work this paper cites.
C. Clouchoux, A. Du Plessis, M. Bouyssi-Kobar, W. Tworetzky, D. McElhinney, D. Brown et al. , “Delayed cortical development in fetuses with complex congenital heart disease,” Cerebral cortex , vol. 23, no. 12, pp. 2932–2943, 2013
2013
Earlier work this paper cites.
A. Akhondi-Asl and S. K. Warfield, “Simultaneous truth and performance level estimation through fusion of probabilistic segmentations,” IEEE transactions on medical imaging , vol. 32, no. 10, pp. 1840–1852, 2013
2013
Earlier work this paper cites.
A. Gholipour, J. A. Estroff, C. E. Barnewolt, R. L. Robertson, P. E. Grant, B. Gagoski et al. , “Fetal MRI: a technical update with educational aspirations,” Concepts in Magnetic Resonance Part A , vol. 43, no. 6, pp. 237–266, 2014
2014
Earlier work this paper cites.
R. Wright, V. Kyriakopoulou, C. Ledig, M. A. Rutherford, J. V. Hajnal, D. Rueckert et al. , “Automatic quantification of normal cortical folding patterns from fetal brain MRI,” NeuroImage , vol. 91, pp. 21–32, 2014
2014
Earlier work this paper cites.
A. Gholipour, C. Limperopoulos, S. Clancy, C. Clouchoux, A. Akhondi-Asl, J. A. Estroff et al. , “Construction of a deformable spatiotemporal MRI atlas of the fetal brain: evaluation of similarity metrics and deformation models,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2014, pp. 292–299
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
B. Kainz, M. Steinberger, W. Wein, M. Kuklisova-Murgasova, C. Malamateniou, K. Keraudren et al. , “Fast volume reconstruction from motion corrupted stacks of 2d slices,” IEEE transactions on medical imaging , vol. 34, no. 9, pp. 1901–1913, 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1026–1034
2015
Cited alongside, same era.
T. Tarui, N. Madan, N. Farhat, R. Kitano, A. Ceren Tanritanir, G. Graham et al. , “Disorganized patterns of sulcal position in fetal brains with agenesis of corpus callosum,” Cerebral Cortex , vol. 28, no. 9, pp. 3192–3203, 2018
2018
Later among the works it cites.
O. M. Benkarim, N. Hahner, G. Piella, E. Gratacos, M. A. G. Ballester, E. Eixarch et al. , “Cortical folding alterations in fetuses with isolated non-severe ventriculomegaly,” NeuroImage: Clinical , vol. 18, pp. 103–114, 2018
2018
Later among the works it cites.
A. Makropoulos, S. J. Counsell, and D. Rueckert, “A review on automatic fetal and neonatal brain MRI segmentation,” NeuroImage , vol. 170, pp. 231–248, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. Xie and Z. Tu, “Holistically-nested edge detection,” in Proceedings of the IEEE Int. conference on computer vision , 2015, pp. 1395–1403
2015
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Cited alongside, same era.
A. M. Mendrik, K. L. Vincken, H. J. Kuijf, M. Breeuwer, W. H. Bouvy, J. De Bresser et al. , “Mrbrains challenge: online evaluation framework for brain image segmentation in 3t MRI scans,” Computational intelligence and neuroscience , vol. 2015, p. 1, 2015
2015
Cited alongside, same era.
V. Yeghiazaryan and I. Voiculescu, “An overview of current evaluation methods used in medical image segmentation,” Tech. Rep. RR-15-08, Department of Computer Science , 2015
2015
Cited alongside, same era.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov et al. , “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1–9
2015
Cited alongside, same era.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in International conference on medical image computing and computer-assisted intervention . Springer, 2016, pp. 424–432
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 Fourth International Conference on 3D Vision (3DV) . IEEE, 2016, pp. 565–571
2016
Cited alongside, same era.
A. G. Roy, N. Navab, and C. Wachinger, “Recalibrating fully convolutional networks with spatial and channel “squeeze and excitation” blocks,” IEEE transactions on medical imaging , vol. 38, no. 2, pp. 540–549, 2018
2018
Later among the works it cites.
S. S. M. Salehi, S. R. Hashemi, C. Velasco-Annis, A. Ouaalam, J. A. Estroff, D. Erdogmus et al. , “Real-time automatic fetal brain extraction in fetal MRI by deep learning,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) . IEEE, 2018, pp. 720–724
2018
Later among the works it cites.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141
2018
Later among the works it cites.
S. Woo, J. Park, J.-Y. Lee, and I. So Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 3–19
2018
Later among the works it cites.
2018
Later among the works it cites.
C. Llinares-Benadero and V. Borrell, “Deconstructing cortical folding: genetic, cellular and mechanical determinants,” Nature Reviews Neuroscience , vol. 20, no. 3, pp. 161–176, 2019
2019
Later among the works it cites.
S. Rana, R. Shishegar, S. Quezada, L. Johnston, D. W. Walker, and M. Tolcos, “The subplate: a potential driver of cortical folding?” Cerebral Cortex , vol. 29, no. 11, pp. 4697–4708, 2019
2019
Later among the works it cites.
C. M. Ortinau, C. K. Rollins, A. Gholipour, H. J. Yun, M. Marshall, B. Gagoski et al. , “Early-emerging sulcal patterns are atypical in fetuses with congenital heart disease,” Cerebral Cortex , vol. 29, no. 8, pp. 3605–3616, 2019
2019
Later among the works it cites.
N. Khalili, N. Lessmann, E. Turk, N. Claessens, R. de Heus, T. Kolk et al. , “Automatic brain tissue segmentation in fetal MRI using convolutional neural networks,” Magnetic resonance imaging , vol. 64, pp. 77–89, 2019
2019
Later among the works it cites.
P. A. Yushkevich, A. Pashchinskiy, I. Oguz, S. Mohan, J. E. Schmitt, J. M. Stein et al. , “User-guided segmentation of multi-modality medical imaging datasets with itk-snap,” Neuroinformatics , vol. 17, no. 1, pp. 83–102, 2019
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan et al. , “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems , 2019, pp. 8024–8035
2019
Later among the works it cites.
G. Wang, J. Shapey, W. Li, R. Dorent, A. Demitriadis, S. Bisdas et al. , “Automatic segmentation of vestibular schwannoma from t2-weighted mri by deep spatial attention with hardness-weighted loss,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2019, pp. 264–272
2019
Later among the works it cites.
M. H. Hesamian, W. Jia, X. He, and P. Kennedy, “Deep learning techniques for medical image segmentation: Achievements and challenges,” Journal of digital imaging , vol. 32, no. 4, pp. 582–596, 2019
2019
Later among the works it cites.
M. Tan and Q. V. Le, “Mixconv: Mixed depthwise convolutional kernels,” CoRR, abs/1907.09595 , 2019
2019
Later among the works it cites.
L. Vasung, C. K. Rollins, H. J. Yun, C. Velasco-Annis, J. Zhang, K. Wagstyl et al. , “Quantitative in vivo MRI assessment of structural asymmetries and sexual dimorphism of transient fetal compartments in the human brain,” Cerebral Cortex , vol. 30, no. 3, pp. 1752–1767, 2020
2020
Closest in time.
L. Vasung, C. K. Rollins, C. Velasco-Annis, H. J. Yun, J. Zhang, S. K. Warfield et al. , “Spatiotemporal differences in the regional cortical plate and subplate volume growth during fetal development,” Cerebral Cortex , 2020
2020
Closest in time.
M. Ebner, G. Wang, W. Li, M. Aertsen, P. A. Patel, R. Aughwane et al. , “An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI,” NeuroImage , vol. 206, p. 116324, 2020
2020
Closest in time.
G. Du, X. Cao, J. Liang, X. Chen, and Y. Zhan, “Medical image segmentation based on u-net: A review,” Journal of Imaging Science and Technology , 2020
2020
Closest in time.