Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

Author:

Christ Patrick Ferdinand,Elshaer Mohamed Ezzeldin A.,Ettlinger Florian,Tatavarty Sunil,Bickel Marc,Bilic Patrick,Rempfler Markus,Armbruster Marco,Hofmann Felix,D’Anastasi Melvin,Sommer Wieland H.,Ahmadi Seyed-Ahmad,Menze Bjoern H.

Publisher

Springer International Publishing

Reference24 articles.

1. Ben-Cohen, A., et al.: Automated method for detection and segmentation of liver metastatic lesions in follow-up CT examinations. J. Med. Imaging 3 (2015)

2. Chartrand, G., et al.: Semi-automated liver CT segmentation using Laplacian meshes. In: ISBI, pp. 641–644. IEEE (2014)

3. Chen, L.C., et al.: Semantic image segmentation with deep convolutional nets and fully connected CRFs. In: ICLR (2015)

4. Deng, X., Du, G.: Editorial: 3D segmentation in the clinic: a grand challenge ii-liver tumor segmentation. In: MICCAI Workshop (2008)

5. Foruzan, A.H., Chen, Y.W.: Improved segmentation of low-contrast lesions using sigmoid edge model. Int. J. Comput. Assist. Radiol. Surg., 1–17 (2015)

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