- [1] Chawla, I. Kister, T. Sinnecker, J. Wuerfel, J.-C. Brisset, F. Paul et al., "Longitudinal study of multiple sclerosis lesions using ultrahigh field (7T) multiparametric MR imaging", PLoS One, Vol. 13, pp. e0202918, 2018.
- [2] R. Nayak, R. Dash, B. Majhi and V. Prasad, "Automated pathological brain detection system: A fast discrete curvelet transform and probabilistic neural network based approach", Expert Systems with Applications, Vol. 88, pp. 152-164, 2017.
- [3] Frisch, M. L. Elkjaer, R. Reynolds, T. M. Michel, T. Kacprowski, M. Burton et al., "Multiple sclerosis atlas: A molecular map of brain lesion stages in progressive multiple sclerosis", Network and systems medicine, Vol. 3, pp. 122-129, 2020.
- [4] Kim, E. Shim, J. Park, Y.-J. Kim, U. Lee and Y. Kim, "Web-based fully automated cephalometric analysis by deep learning", Computer methods and programs in biomedicine, Vol. 194, pp. 105513, 2020.
- [5] McKinley, R. Wepfer, L. Grunder, F. Aschwanden, T. Fischer, C. Friedli et al., "Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence", NeuroImage: Clinical, Vol. 25, pp. 102104, 2020.
- [6] Pourreza, Y. Zhuge, H. Ning, and R. Miller, “Brain Tumor Segmentation in MRI Scan Using Deeply-Supervised Neural Networks,” brainless, Lect. Notes Comput. Sci., Vol. 10670, pp. 320–331, 2018.
- [7] Li, F. Jia, and J. Qin, “Brain Tumor Segmentation from Multimodal Magnetic Resonance Images via Sparse Representation,” Artif. Intell. Med.,Vol. 73, pp. 1–13, 2016.
- [8] Ahmadvand, M. R. Daliri, and S. M. Zahiri, “Segmentation of Brain MR Images using a Proper Combination of DCS based method with MRF,” Multimedia. Tools Appl., Vol. 77, no. 7, pp. 8001–8018, 2018.
- [9] Wang et al., “Interactive Medical Image Segmentation Using Deep Learning with Image-Specific Fine Tuning,” IEEE Trans. Med. Imaging, Vol. 37, No. 7, pp. 1562–1573, 2018.
- Amin, M. Sharif, M. Yasmin, and S. L. Fernandes, “A Distinctive Approach in Brain Tumor Detection and Classification using MRI,” Pattern Recognit. Lett., pp. 1–10, 2017.
- M. K. Hasan and M. Ahmad, “Two-Step Verification of Brain Tumor Segmentation using Watershed-Matching Algorithm,” Brain Informatics, Vol. 5, 2018.
- Kats, J. Goldberger and H. Greenspan, “Soft labeling by distilling anatomical knowledge for improved ms lesion segmentation,”, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1563-1566, 2019.
- Narayana, I. Coronado, S. Sujit, J. Wolinsky, F. Lublin and R. Gabr, “Deep learning for predicting enhancing lesions in multiple sclerosis from noncontrast mri,”, Radiology, Vol. 294, pp. 191, Dec. 2019.
- La Rosa, A. Abdulkadir, M. Fartaria, R. Rahmanzadeh, P.-J. Lu, R. Gal-busera, et al., “Multiple sclerosis cortical and wm lesion segmentation at 3t mri: A deep learning method based on flair and mp2rage,”, NeuroImage: Clinical, Vol. 27, pp. 102, Jun. 2020.
- Christoph Baur, Benedikt Wiestler, Shadi Albarqouni and Nassir Navab, “Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation,”, Proceedings of Machine Learning Research, Vol. 102, pp. 63-72, 2019.
- de Santiago, E. Sánchez Morla, M. Ortiz, E. López, C. Amo Usanos, M. Alonso-Rodríguez et al., “A computer-aided diagnosis of multiple sclerosis based on mfVEP recordings,”, PloS one, Vol. 14, pp. e0214662, 2019.
- L. Jui et al., “Brain MRI Tumor Segmentation with 3D Intracranial Structure Deformation Features,” IEEE Intell. Syst., Vol. 31, No. 2, pp. 66–76, 2016.
- Lakshmi, T. Arivoli, and M. P. Rajasekaran, “A Novel M-ACABased Tumor Segmentation and DAPP Feature Extraction with PPCSO-PKC-Based MRI Classification,” Arab. J. Sci. Eng., pp. 1–17, 2017.
- Bieniek and A. Moga, “An efficient watershed algorithm based on connected components,” ,Pattern Recognition, Vol. 33, No. 6, pp. 907–916, 2000.
- Meyer, “Levelings and morphological segmentation,” in Proceedings of SIBGRAPI’98, (Rio de Janeiro, Brazil), pp. 28–35, 1998.
- Roura, A. Oliver, M. Cabezas, S. Valverde, D. Pareto, J. Vilanova, L. Rami_o-Torrent_a, A. Rovira, and X. Llad_o, “A toolbox for multiple sclerosis lesion segmentation,” Neuroradiology, Vol. 57, No. 10, pp. 1031–1043, 2015.
- Cabezas, A. Oliver, E. Roura, J. Freixenet, J. C. Vilanova, L. Rami_o-Torrent_a,_A. Rovira, and X. Llad_o, “Automatic multiple sclerosis lesion detection in brain mri by air thresholding,” Computer Methods and Programs in Biomedicine, Vol. 115, No. 3, pp. 147–161, 2014.
- H. Zhuang, D. J. Valentino, and A. W. Toga, “Skull-stripping magnetic resonance brain images using a model-based level set,” NeuroImage, Vol. 32, nNo. 1, pp. 79–92, 2006.
- G. Park and C. Lee, “Skull stripping based on region growing for magnetic resonance brain images,” NeuroImage, Vol. 47, No. 4, pp. 1394–1407, 2009.
- Somasundaram and T. Kalaiselvi, “Fully automatic brain extraction algorithm for axial T2-weighted magnetic resonance images,” Computers in Biology and Medicine, Vol. 40, No. 10, pp. 811–822, 2010.
- G. R. Balan, A. J. M. Traina, M. X. Ribeiro, P. M. A. Marques, and C. Traina Jr, “Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI,”, Computers in Biology and Medicine, Vol. 42, No. 5, pp. 509–522, 2012.
- Diez, A. Oliver, M. Cabezas, S. Valverde, R. Mart_, J. Vilanova, L. Rami_o-Torrent_a, _A. Rovira, and X. Llad_o, “Intensity based methods for brain mri longitudinal registration. a study on multiple sclerosis patients,”, Neuroinformatics, Vol. 12, No. 3, pp. 365–379, 2014.
- Roura, T. Schneider, M. Modat, P. Daga, N. Muhlert, D. Chard, S. Ourselin, X. Llad_o, and C. A. M. Wheeler-Kingshott, “Multi-channel registration of fa and t1w images in the presence of atrophy: application to multiple sclerosis,”, Functional Neurology, Vol. 30, No. 4, 2015.
- de Bresser, M. P. Portegies, A. Leemans, G. J. Biessels, L. J. Kappelle, and M. A. Viergever, “A comparison of fMRg based segmentation methods for measuring brain atrophy progression,”, NeuroImage, Vol. 54, No. 2, pp. 760–768, 2011.
- Popescu, N. Ran, F. Barkhof, D. Chard, C. Wheeler-Kingshott, and H. Vrenken, “Accurate fGMg atrophy quanti_cation in fMSg using lesion-_lling with coregistered 2d lesion masks,”, NeuroImage: Clinical, Vol. 4, pp. 366–373, 2014.
- Wu, S. K. War_eld, I. L. Tan, W. M. W. III, D. S. Meier, R. A. van Schijndel, F. Barkhof, and C. R. Guttmann, “Automated segmentation of multiple sclerosis lesion subtypes with multichannel fMRIg,”, NeuroImage, Vol. 32, No. 3, pp. 1205–1215, 2006.
- Garcia-Lorenzo, S. Prima, D. Arnold, D. Collins, and C. Barillot, “Trimmedlikelihood estimation for focal lesions and tissue segmentation in multisequence mri for multiple sclerosis,”, Medical Imaging, IEEE Transactions on, Vol. 30, No. 8, pp. 1455–1467, 2011.
- Battaglini, M. Jenkinson, and N. De Stefano, “Evaluating and reducing the impact of white matter lesions on brain volume measurements,”, Human Brain Map- ping, Vol. 33, No. 9, pp. 2062–2071, 2012.
- Sdika and D. Pelletier, “Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping,”, Human Brain Mapping, Vol. 30, No. 4, pp. 1060–1067, 2009.
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