참고문헌
- J. Kawahara; C. J. Brown; S. P. Miller; B. G. Booth; V. Chau; R. E. Grunau; J. G. Zwicker; G. Hamarneh; "Brainnetcnn: Convolutional neural networks for brain networks; towards predicting neurodevelopment," NeuroImage, vol.146, pp. 1038-1049, 2017. https://doi.org/10.1016/j.neuroimage.2016.09.046
- P. A. Yushkevich; B. B. Avants; S. R. Das; J. Pluta; M. Altinay; C. Craige; A. D. N. Initiative; et al.; "Bias in estimation of hippocampal atrophy using deformation-based morphometry arises from asymmetric global normalization: an illustration in adni 3T mri data," Neuroimage, vol.50, no.2, pp.434-445, 2010 https://doi.org/10.1016/j.neuroimage.2009.12.007
- J. D. Bremner; P. Randall; T. M. Scott; R. A. Bronen; J. P. Seibyl; S. M. Southwick; R. C. Delaney; G. McCarthy; D. S. Charney; R. B. Innis; "Mri-based measurement of hippocampal volume in patients with combatrelated posttraumatic stress disorder," The American journal of psychiatry, vol.152, no.7, p.973, 1995 https://doi.org/10.1176/ajp.152.7.973
- E. R. Mulder; R. A. de Jong; D. L. Knol; R. A. van Schijndel; K. S. Cover; P. J. Visser; F. Barkhof; H. Vrenken; "Hippocampal volume change measurement: Quantitative assessment of the reproducibility of expert manual outlining and the automated methods freesurfer and FIRST," NeuroImage, vol.92, pp.169-181, 2014 https://doi.org/10.1016/j.neuroimage.2014.01.058
- I. B. Malone; K. K. Leung; S. Clegg; J. Barnes; J. L. Whitwell; J. Ashburner; N. C. Fox; G. R. Ridgway; "Accurate automatic estimation of total intracranial volume: A nuisance variable with less nuisance," NeuroImage, vol.104, pp.366-372, 2015 https://doi.org/10.1016/j.neuroimage.2014.09.034
- J. L. Winterburn; J. C. Pruessner; S. Chavez; M. M. Schira; N. J. Lobaugh; A. N. Voineskos; M. M. Chakravarty; "A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging," NeuroImage, vol.74, pp.254-265, 2013 https://doi.org/10.1016/j.neuroimage.2013.02.003
- N. Sharma; A. N. Singh; "Exploring biomarkers for alzheimer's disease," Journal of clinical and diagnostic research: JCDR, vol.10, no.7, p. KE01, 2016
- C. Omizzolo; D. K. Thompson; S. E. Scratch; R. Stargatt; K. J. Lee; J. Cheong; G. Roberts; L. W. Doyle; P. J. Anderson; "Hippocampal volume and memory and learning outcomes at 7 years in children born very preterm," Journal of the International Neuropsychological Society, vol.19, no.10, pp.1065-1075, 2013 https://doi.org/10.1017/S1355617713000891
- J. Dolz; C. Desrosiers; I. B. Ayed; "3d fully convolutional networks for subcortical segmentation in MRI: A large-scale study," NeuroImage, vol.170, pp.456-470, 2018 https://doi.org/10.1016/j.neuroimage.2017.04.039
- J. E. Iglesias; J. C. Augustinack; K. Nguyen; C. M. Player; A. Player; M. Wright; N. Roy; M. P. Frosch; A. C. McKee; L. L. Wald; B. Fischl; K. V. Leemput; "A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI," NeuroImage, vol.115, pp.117-137, 2015 https://doi.org/10.1016/j.neuroimage.2015.04.042
- J. E. Iglesias; K. V. Leemput; J. Augustinack; R. Insausti; B. Fischl; M. Reuter; "Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific atlases," NeuroImage, vol.141, pp.542-555, 2016 https://doi.org/10.1016/j.neuroimage.2016.07.020
- Y. LeCun; Y. Bengio; G. E. Hinton; "Deep learning," Nature, vol.521, no.7553, pp.436-444, 2015 https://doi.org/10.1038/nature14539
- Y. Lecun; L. Bottou; Y. Bengio; P. Haffner; "Gradient-based learning applied to document recognition," in Proc. of the IEEE, pp.2278-2324, 1998 https://doi.org/10.1109/5.726791
- A. Basher; K. Y. Choi; J. J. Lee; B. Lee; B. C. Kim; K. H. Lee; H. Y. Jung; "Hippocampus localization using a two-stage ensemble hough convolutional neural network," IEEE Access, vol.7, pp.73436-73447, 2019 https://doi.org/10.1109/ACCESS.2019.2920005
- N. J. Tustison; P. A. Cook; A. Klein; G. Song; S. R. Das; J. T. Duda; B. M. Kandel; N. van Strien; J. R. Stone; J. C. Gee; B. B. Avants; "Largescale evaluation of ants and freesurfer cortical thickness measurements," NeuroImage, vol.99, pp.166-179, 2014 https://doi.org/10.1016/j.neuroimage.2014.05.044
- F. Cendes; F. Andermann; P. Gloor; A. Evans; M. Jones-Gotman; C. Watson; D. Melanson; A. Olivier; T. Peters; I. Lopes-Cendes; et al.; "Mri volumetric measurement of amygdala and hippocampus in temporal lobe epilepsy," Neurology, vol.43, no.4, pp.719-719, 1993 https://doi.org/10.1212/WNL.43.4.719
- F. van der Lijn; T. den Heijer; M. M. B. Breteler; W. J. Niessen; "Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts," NeuroImage, vol.43, no.4, pp.708-720, 2008 https://doi.org/10.1016/j.neuroimage.2008.07.058
- O. T. Carmichael; H. A. Aizenstein; S. W. Davis; J. T. Becker; P. M. Thompson; C. C. Meltzer; Y. Liu; "Atlas-based hippocampus segmentation in alzheimer's disease and mild cognitive impairment," NeuroImage, vol.27, no.4, pp.979-990, 2005 https://doi.org/10.1016/j.neuroimage.2005.05.005
- P. Coupe; J. V. Manjon; V. Fonov; J. Pruessner; M. Robles; D. L. Collins; "Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation," NeuroImage, vol.54, no.2, pp.940-954, 2011 https://doi.org/10.1016/j.neuroimage.2010.09.018
- J. E. Iglesias; K. V. Leemput; P. Bhatt; C. Casillas; S. Dutt; N. Schuff; D. Truran-Sacrey; A. L. Boxer; B. Fischl; "Bayesian segmentation of brainstem structures in MRI," NeuroImage, vol.113, pp.184-195, 2015 https://doi.org/10.1016/j.neuroimage.2015.02.065
- T. D. Vu; H.-J. Yang; L. N. Do; T. N. Thieu; "Classifying instantaneous cognitive states from fmri using discriminant based feature selection and adaboost," Smart Media Journal, vol.5, no.1, pp.30-37, 2016
- J. Gall; A. Yao; N. Razavi; L. J. V. Gool; V. S. Lempitsky; "Hough forests for object detection, tracking, and action recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol.33, no.11, pp.2188-2202, 2011 https://doi.org/10.1109/TPAMI.2011.70
- M. TrieuTran; G. SangLee; "Super-resolution in music score images by instance normalization," Smart Media Journal, vol.8, pp.64-71, 2019 https://doi.org/10.30693/SMJ.2019.8.4.64
- H. T. Tran; A. R. Oh; I. S. Na; S. H. Kim; "Liver segmentation and 3d modeling from abdominal ct images," Smart Media Journal, vol.5, no.1, pp.49-54, 2016
- C. Wachinger; M. Reuter; T. Klein; "Deepnat: Deep convolutional neural network for segmenting neuroanatomy," NeuroImage, vol.170, pp.434-445, 2018 https://doi.org/10.1016/j.neuroimage.2017.02.035
- F. Milletari; S. Ahmadi; C. Kroll; A. Plate; V. E. Rozanski; J. Maiostre; J. Levin; O. Dietrich; B. Ertl-Wagner; K. Botzel; N. Navab; "Houghcnn: Deep learning for segmentation of deep brain regions in MRI and ultrasound," Computer Vision and Image Understanding, vol.164, pp.92-102, 2017 https://doi.org/10.1016/j.cviu.2017.04.002
- W. A. Al; H. Y. Jung; I. D. Yun; Y. Jang; H.-B. Park; H.-J. Chang; "Automatic aortic valve landmark localization in coronary ct angiography using colonial walk," PLOS ONE, vol.13, pp.1-23, 2018
- O. Ronneberger; P. Fischer; T. Brox; "U-net: Convolutional networks for biomedical image segmentation," in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015-18th International Conference Munich, Germany, October 5 - 9, 2015, Proc. Part III vol.9351 of Lecture Notes in Computer Science, pp.234-241, Springer, 2015
- O. Cicek; A. Abdulkadir; S. S. Lienkamp; T. Brox; O. Ronneberger; "3d u-net: learning dense volumetric segmentation from sparse annotation," in International conference on medical image computing and computer-assisted intervention, pp.424-432, Springer, 2016
- N. T. Duc; S. Ryu; M. N. I. Qureshi; M. Choi; K. H. Lee; B. Lee; "3d-deep learning based automatic diagnosis of alzheimer's disease with joint mmse prediction using resting-state fmri," Neuroinformatics, vol.18, no.1, pp.71-86, 2020 https://doi.org/10.1007/s12021-019-09419-w
- M. N. I. Qureshi; S. Ryu; J. Song; K. H. Lee; B. Lee; "Evaluation of functional decline in alzheimer's dementia using 3d deep learning and group ica for rs-fmri measurements," Frontiers in aging neuroscience, vol.11, p.8, 2019 https://doi.org/10.3389/fnagi.2019.00008
- S. Ioffe; C. Szegedy; "Batch normalization: Accelerating deep network training by reducing internal covariate shift," in Proc of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, vol.37 of JMLR Workshop and Conference Proceedings, pp.448-456, JMLR.org, 2015
- V. Nair; G. E. Hinton; "Rectified linear units improve restricted boltzmann machines," in Proc. of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel (J. Furnkranz and T. Joachims, eds.), pp.807-814, Omnipress, 2010
- D. P. Kingma; J. Ba; "Adam: A method for stochastic optimization," in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015
- K. Y. Choi; J. J. Lee; T. I. Gunasekaran; S. Kang; W. Lee; J. Jeong; H. J. Lim; X. Zhang; C. Zhu; S.-Y. Won; et al.; "Apoe promoter polymorphism- 219t/g is an effect modifier of the influence of apoe "4 on alzheimer's disease risk in a multiracial sample," Journal of clinical medicine, vol.8, no.8, p.1236, 2019 https://doi.org/10.3390/jcm8081236
- https://surfer.nmr.mgh.harvard.edu/fswiki/HippocampalSubfields (accessed June 15, 2020).