Deep-Stacked Auto Encoder for Liver Segmentation-Reference-Cited by-同舟云学术

Deep-Stacked Auto Encoder for Liver Segmentation

Published:2017-11-26 Issue: Volume: Page:243-251
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Ahmad Mubashir,Yang Jian,Ai Danni,Qadri Syed Furqan,Wang Yongtian

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-7389-2_24

Reference16 articles.

1. Li, D., Liu, L., Kapp, D.S., Xing, L.: Automatic liver contouring for radiotherapy treatment planning. Phys. Med. Biol. 60(19), 7461 (2015)

2. Lecture Notes in Computer Science;K-S Seo,2005

3. Oliveira, D.A., Feitosa, R.Q., Correia, M.M.: Segmentation of liver, its vessels and lesions from CT images for surgical planning. Biomed. Eng. 10(1), 30 (2011)

4. Peng, J., Hu, P., Lu, F., Peng, Z., Kong, D., Zhang, H.: 3D liver segmentation using multiple region appearances and graph cuts. Med. Phys. 42(12), 6840–6852 (2015)

5. Rusko, L., Bekes, G., Nemeth, G., Fidrich, M.: Fully automatic liver segmentation for contrast enhanced CT images. In: Proceedings of MICCAI Workshop 3D Segmentation in the Clinic: A Grand Challenge, Brisbane, Australia, vol. 2 (2007)

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