Automatic Tumor Segmentation by Means of Deep Convolutional U-Net With Pre-Trained Encoder in PET Images

Yongzhou Lu; Jinqiu Lin; Sheng Chen; Hui He; Yuantao Cai

doi:10.1109/access.2020.3003138

journal article Open Access Jan 01, 2020

Automatic Tumor Segmentation by Means of Deep Convolutional U-Net With Pre-Trained Encoder in PET Images

Yongzhou Lu Jinqiu Lin

Sheng Chen

Hui He

Yuantao Cai

IEEE Access Vol. 8 pp. 113636-113648 · Institute of Electrical and Electronics Engineers (IEEE)

View at Publisher Save 10.1109/access.2020.3003138

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References

52

[1]

badrinarayanan "SegNet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling" arXiv 1505 07293 (2015)

[2]

10.1109/tmi.2004.824238

[3]

10.1016/j.sigpro.2020.107456

[4]

10.1109/jbhi.2018.2890045

[5]

10.1109/isbi.2016.7493536

[6]

10.1109/siu.2017.7960697

[7]

10.1109/jbhi.2018.2810379

[8]

Fully convolutional networks for semantic segmentation

Jonathan Long, Evan Shelhamer, Trevor Darrell

2015 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2015.7298965

[9]

10.1118/1.4944498

[10]

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

Sergio Pereira, Adriano Pinto, Victor Alves et al.

IEEE Transactions on Medical Imaging 10.1109/tmi.2016.2538465

[11]

Gradient-based learning applied to document recognition

Y. Lecun, L. Bottou, Y. Bengio et al.

Proceedings of the IEEE 10.1109/5.726791

[12]

10.1109/tip.2012.2214045

[13]

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

Qi Dou, Hao Chen, Lequan Yu et al.

IEEE Transactions on Medical Imaging 10.1109/tmi.2016.2528129

[14]

10.1088/0031-9155/42/1/008

[15]

10.7150/thno.5629

[16]

10.1007/s00259-010-1423-3

[17]

10.1109/jbhi.2016.2624798

[18]

10.1016/j.compbiomed.2014.04.014

[19]

10.1109/icip.2011.6116401

[20]

10.1109/tgrs.2020.2963848

[21]

10.1109/tbme.2015.2503421

[22]

10.1109/tbme.2013.2284600

[23]

10.1109/tmi.2017.2695227

[24]

10.1016/j.acra.2005.06.017

[25]

kingma "Adam: A method for stochastic optimization" arXiv 1412 6980 (2014)

[26]

10.1118/1.2870215

[27]

10.1118/1.3213099

[28]

ronneberger "U-net: Convolutional networks for biomedical image segmentation" Proc Int Conf Med Image Comput Comput -Assist Intervent (2015)

[29]

10.1109/tns.2012.2212723

[30]

Random Walks for Image Segmentation

L. Grady

IEEE Transactions on Pattern Analysis and Machine... 10.1109/tpami.2006.233

[31]

10.1109/isbi.2012.6235791

[32]

10.1118/1.2956713

[33]

10.1109/42.996338

[34]

10.1118/1.3301610

[35]

10.1016/j.csda.2010.09.021

[36]

10.1016/j.media.2014.06.014

[37]

10.1007/s00259-016-3427-0

[38]

10.1016/j.radonc.2010.07.012

[39]

10.1016/s0167-8140(03)00270-6

[40]

10.1002/mp.12124

[41]

10.1120/jacmp.v13i5.3875

[42]

10.1088/0031-9155/50/24/014

[43]

simonyan "Very deep convolutional networks for large-scale image recognition" arXiv 1409 1556 (2014)

[44]

10.1118/1.2361076

[45]

krizhevsky "ImageNet classification with deep convolutional neural networks" Proc Adv Neural Inf Process Syst (2012)

[46]

10.1088/0031-9155/61/6/2283

[47]

iglovikov "TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation" arXiv 1801 05746 (2018)

[48]

ImageNet Large Scale Visual Recognition Challenge

Olga Russakovsky, Jia Deng, Hao Su et al.

International Journal of Computer Vision 10.1007/s11263-015-0816-y

[49]

10.1109/tmi.2006.871549

[50]

10.1109/isbi.2018.8363561

Showing 50 of 52 references

Cited By

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Metrics

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Citations

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References

Details

Published: Jan 01, 2020
Vol/Issue: 8
Pages: 113636-113648
License: View

Authors

Funding

National Natural Science Foundation Award: 81101116

Cite This Article

Yongzhou Lu, Jinqiu Lin, Sheng Chen, et al. (2020). Automatic Tumor Segmentation by Means of Deep Convolutional U-Net With Pre-Trained Encoder in PET Images. IEEE Access, 8, 113636-113648. https://doi.org/10.1109/access.2020.3003138

Automatic Tumor Segmentation by Means of Deep Convolutional U-Net With Pre-Trained Encoder in PET Images

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