Page 68 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

56 Chapter 2 Deep convolutional neural network in medical image processing

[62] W. Zhang, R. Li, H. Deng, L. Wang, W. Lin, S. Ji, D. Shen, Deep
convolutional neural networks for multi-modality isointense infant brain
image segmentation, Neuroimage 108 (2015) 214e224.
[63] A. de Brebisson, G. Montana, Deep neural networks for anatomical brain
segmentation, in: Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition Workshops, 2015, pp. 20e28.
[64] Y. Pan, W. Huang, Z. Lin, W. Zhu, J. Zhou, J. Wong, Z. Ding, Brain tumor
grading based on neural networks and convolutional neural networks, in:
2015 37th Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC), IEEE, 2015, August, pp. 699e702.
[65] S. Sarraf, G. Tofighi, Classification of Alzheimer's Disease Using Fmri Data
and Deep Learning Convolutional Neural Networks, 2016 arXiv preprint
arXiv:1603.08631.
[66] E. Hosseini-Asl, G. Gimel'farb, A. El-Baz, Alzheimer's Disease Diagnostics
by a Deeply Supervised Adaptable 3D Convolutional Network, 2016 arXiv
preprint arXiv:1607.00556.
[67] H. Choi, K.H. Jin, Fast and robust segmentation of the striatum using
deep convolutional neural networks, J. Neurosci. Methods 274 (2016)
146e153.
[68] A. Birenbaum, H. Greenspan, Longitudinal multiple sclerosis lesion
segmentation using multi-view convolutional neural networks, in: Deep
Learning and Data Labeling for Medical Applications, Springer, Cham,
2016, pp. 58e67.
[69] T. Brosch, L.Y. Tang, Y. Yoo, D.K. Li, A. Traboulsee, R. Tam, Deep 3D
convolutional encoder networks with shortcuts for multiscale feature
integration applied to multiple sclerosis lesion segmentation, IEEE Trans.
Med. Imaging 35 (5) (2016) 1229e1239.
[70] H. Chen, Q. Dou, L. Yu, P.A. Heng, Voxresnet: Deep Voxelwise Residual
Networks for Volumetric Brain Segmentation, 2016 arXiv preprint arXiv:
1608.05895.
[71] M. Ghafoorian, N. Karssemeijer, T. Heskes, I.W. van Uden, C.I. Sanchez,
G. Litjens, F.E. de Leeuw, B. van Ginneken, E. Marchiori, B. Platel,
Location sensitive deep convolutional neural networks for segmentation
of white matter hyperintensities, Sci. Rep. 7 (1) (2017) 1e12.
[72] F. Milletari, S.A. Ahmadi, C. Kroll, A. Plate, V. Rozanski, J. Maiostre,
J. Levin, O. Dietrich, B. Ertl-Wagner, K. Bötzel, N. Navab, Hough-CNN:
deep learning for segmentation of deep brain regions in MRI and
ultrasound, Comput. Vis. Image Understand. 164 (2017) 92e102.
[73] M. Ghafoorian, N. Karssemeijer, T. Heskes, I.W.M. Van Uder, F.E. de
Leeuw, E. Marchiori, B. van Ginneken, B. Platel, Non-uniform patch
sampling with deep convolutional neural networks for white matter
hyperintensity segmentation, in: 2016 IEEE 13th International Symposium
on Biomedical Imaging (ISBI), IEEE, 2016, April, pp. 1414e1417.
[74] M. Havaei, N. Guizard, N. Chapados, Y. Bengio, Hemis: Hetero-modal
image segmentation, in: International Conference on Medical Image
Computing and Computer-Assisted Intervention, Springer, Cham, 2016,
October, pp. 469e477.
[75] M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio,
C. Pal, P.M. Jodoin, H. Larochelle, Brain tumor segmentation with deep
neural networks, Med. Image Anal. 35 (2017) 18e31.
[76] J. Kleesiek, G. Urban, A. Hubert, D. Schwarz, K. Maier-Hein, M. Bendszus,
A. Biller, Deep MRI brain extraction: a 3D convolutional neural network
for skull stripping, Neuroimage 129 (2016) 460e469.