Fetching the paper…
Reading the bibliography…
Liver cancer is one of the leading causes of cancer death.
D. H. Wolpert, “Stacked generalization,” Neural networks , vol. 5, no. 2, pp. 241–259, 1992
1992
Earlier work this paper cites.
L. Soler, H. Delingette, G. Malandain, J. Montagnat, N. Ayache, C. Koehl, O. Dourthe, B. Malassagne, M. Smith, D. Mutter et al. , “Fully automatic anatomical, pathological, and functional segmentation from ct scans for hepatic surgery,” Computer Aided Surgery , vol. 6, no. 3, pp. 131–142, 2001
2001
Earlier work this paper cites.
R. Lu, P. Marziliano, and C. H. Thng, “Liver tumor volume estimation by semi-automatic segmentation method,” in Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the . IEEE, 2006, pp. 3296–3299
2006
Earlier work this paper cites.
Z. Tu, “Auto-context and its application to high-level vision tasks,” in Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on . IEEE, 2008, pp. 1–8
2008
Earlier work this paper cites.
J. H. Moltz, L. Bornemann, V. Dicken, and H. Peitgen, “Segmentation of liver metastases in ct scans by adaptive thresholding and morphological processing,” in MICCAI workshop , vol. 41, no. 43, 2008, p. 195
2008
Earlier work this paper cites.
D. Wong, J. Liu, Y. Fengshou, Q. Tian, W. Xiong, J. Zhou, Y. Qi, T. Han, S. Venkatesh, and S.-c. Wang, “A semi-automated method for liver tumor segmentation based on 2d region growing with knowledge-based constraints,” in MICCAI workshop , vol. 41, no. 43, 2008, p. 159
2008
Earlier work this paper cites.
J. Ferlay, H.-R. Shin, F. Bray, D. Forman, C. Mathers, and D. M. Parkin, “Estimates of worldwide burden of cancer in 2008: Globocan 2008,” International journal of cancer , vol. 127, no. 12, pp. 2893–2917, 2010
2010
Earlier work this paper cites.
L. Soler, A. Hostettler, V. Agnus, A. Charnoz, J. Fasquel, J. Moreau, A. Osswald, M. Bouhadjar, and J. Marescaux, “3d image reconstruction for comparison of algorithm database: a patient-specific anatomical and medical image database,” 2010
2010
Earlier work this paper cites.
D. Jimenez-Carretero, L. Fernandez-de Manuel, J. Pascau, J. M. Tellado, E. Ramon, M. Desco, A. Santos, and M. J. Ledesma-Carbayo, “Optimal multiresolution 3d level-set method for liver segmentation incorporating local curvature constraints,” in Engineering in medicine and biology society, EMBC, 2011 annual international conference of the IEEE . IEEE, 2011, pp. 3419–3422
2011
Earlier work this paper cites.
A. Prasoon, K. Petersen, C. Igel, F. Lauze, E. Dam, and M. Nielsen, “Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network,” in International conference on medical image computing and computer-assisted intervention . Springer, 2013, pp. 246–253
2013
Earlier work this paper cites.
C. Li, X. Wang, S. Eberl, M. Fulham, Y. Yin, J. Chen, and D. D. Feng, “A likelihood and local constraint level set model for liver tumor segmentation from ct volumes,” IEEE Transactions on Biomedical Engineering , vol. 60, no. 10, pp. 2967–2977, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
W. Huang, Y. Yang, Z. Lin, G.-B. Huang, J. Zhou, Y. Duan, and W. Xiong, “Random feature subspace ensemble based extreme learning machine for liver tumor detection and segmentation,” in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE . IEEE, 2014, pp. 4675–4678
2014
Earlier work this paper cites.
E. Vorontsov, N. Abi-Jaoudeh, and S. Kadoury, “Metastatic liver tumor segmentation using texture-based omni-directional deformable surface models,” in International MICCAI Workshop on Computational and Clinical Challenges in Abdominal Imaging . Springer, 2014, pp. 74–83
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
M. F. Stollenga, W. Byeon, M. Liwicki, and J. Schmidhuber, “Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation,” in Advances in Neural Information Processing Systems , 2015, pp. 2998–3006
2015
Earlier work this paper cites.
H. R. Roth, L. Lu, A. Farag, H.-C. Shin, J. Liu, E. B. Turkbey, and R. M. Summers, “Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2015, pp. 556–564
2015
Earlier work this paper cites.
J. Wang, J. D. MacKenzie, R. Ramachandran, and D. Z. Chen, “Detection of glands and villi by collaboration of domain knowledge and deep learning,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2015, pp. 20–27
2015
Earlier work this paper cites.
H. Chen, D. Ni, J. Qin, S. Li, X. Yang, T. Wang, and P. A. Heng, “Standard plane localization in fetal ultrasound via domain transferred deep neural networks,” IEEE journal of biomedical and health informatics , vol. 19, no. 5, pp. 1627–1636, 2015
2015
Earlier work this paper cites.
F. Chollet et al. , “Keras,” https://github.com/fchollet/keras
2015
Earlier work this paper cites.
G. Li, X. Chen, F. Shi, W. Zhu, J. Tian, and D. Xiang, “Automatic liver segmentation based on shape constraints and deformable graph cut in ct images,” IEEE Transactions on Image Processing , vol. 24, no. 12, pp. 5315–5329, 2015
2015
Cited alongside, same era.
X. Li, Q. Dou, H. Chen, C.-W. Fu, and P.-A. Heng, “Multi-scale and modality dropout learning for intervertebral disc localization and segmentation,” in International Workshop on Computational Methods and Clinical Applications for Spine Imaging . Springer, 2016, pp. 85–91
2016
Cited alongside, same era.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2016, pp. 424–432
2016
Cited alongside, same era.
C.-L. Kuo, S.-C. Cheng, C.-L. Lin, K.-F. Hsiao, and S.-H. Lee, “Texture-based treatment prediction by automatic liver tumor segmentation on computed tomography,” in Computer, Information and Telecommunication Systems (CITS), 2017 International Conference on . IEEE, 2017, pp. 128–132
2017
Closest in time.
P.-H. Conze, V. Noblet, F. Rousseau, F. Heitz, V. de Blasi, R. Memeo, and P. Pessaux, “Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced ct scans,” International journal of computer assisted radiology and surgery , vol. 12, no. 2, pp. 223–233, 2017
2017
Closest in time.
A. Hoogi, C. F. Beaulieu, G. M. Cunha, E. Heba, C. B. Sirlin, S. Napel, and D. L. Rubin, “Adaptive local window for level set segmentation of ct and mri liver lesions,” Medical image analysis , vol. 37, pp. 46–55, 2017
2017
Closest in time.
F. Chaieb, T. B. Said, S. Mabrouk, and F. Ghorbel, “Accelerated liver tumor segmentation in four-phase computed tomography images,” Journal of Real-Time Image Processing , vol. 13, no. 1, pp. 121–133, 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi et al. , “Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2016, pp. 415–423
2016
Cited alongside, same era.
A. Ben-Cohen, I. Diamant, E. Klang, M. Amitai, and H. Greenspan, “Fully convolutional network for liver segmentation and lesions detection,” in International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis . Springer, 2016, pp. 77–85
2016
Cited alongside, same era.
Q. Dou, H. Chen, Y. Jin, L. Yu, J. Qin, and P.-A. Heng, “3d deeply supervised network for automatic liver segmentation from ct volumes,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2016, pp. 149–157
2016
Cited alongside, same era.
N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang, “Convolutional neural networks for medical image analysis: Full training or fine tuning?” IEEE transactions on medical imaging , vol. 35, no. 5, pp. 1299–1312, 2016
2016
Cited alongside, same era.
T.-N. Le, H. T. Huynh et al. , “Liver tumor segmentation from mr images using 3d fast marching algorithm and single hidden layer feedforward neural network,” BioMed research international , vol. 2016, 2016
2016
Cited alongside, same era.
A. H. Foruzan and Y.-W. Chen, “Improved segmentation of low-contrast lesions using sigmoid edge model,” International journal of computer assisted radiology and surgery , vol. 11, no. 7, pp. 1267–1283, 2016
2016
Cited alongside, same era.
M. Moghbel, S. Mashohor, R. Mahmud, and M. I. B. Saripan, “Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring,” EXCLI journal , vol. 15, p. 406, 2016
2016
Cited alongside, same era.
M. Moghbel, S. Mashohor, R. Mahmud, and M. I. B. Saripan, “Automatic liver segmentation on computed tomography using random walkers for treatment planning,” EXCLI journal , vol. 15, p. 500, 2016
2016
Cited alongside, same era.
M. Moghbel, S. Mashohor, R. Mahmud, and M. I. B. Saripan, “Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography,” Artificial Intelligence Review , pp. 1–41, 2017
2017
Cited alongside, same era.
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.
Y. Zhou, L. Xie, W. Shen, Y. Wang, E. K. Fishman, and A. L. Yuille, “A fixed-point model for pancreas segmentation in abdominal ct scans,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2017, pp. 693–701
2017
Closest in time.
A. Farag, L. Lu, H. R. Roth, J. Liu, E. Turkbey, and R. M. Summers, “A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling,” IEEE Transactions on Image Processing , vol. 26, no. 1, pp. 386–399, 2017
2017
Closest in time.
Y. Zhou, L. Xie, E. K. Fishman, and A. L. Yuille, “Deep supervision for pancreatic cyst segmentation in abdominal ct scans,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2017, pp. 222–230
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.
2017
Closest in time.
W. Wu, S. Wu, Z. Zhou, R. Zhang, and Y. Zhang, “3d liver tumor segmentation in ct images using improved fuzzy c-means and graph cuts,” BioMed research international , vol. 2017, 2017
2017
Closest in time.
K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation,” Medical image analysis , vol. 36, pp. 61–78, 2017
2017
Closest in time.
J. Li, X. Liang, Y. Wei, T. Xu, J. Feng, and S. Yan, “Perceptual generative adversarial networks for small object detection,” in IEEE CVPR , 2017
2017
Closest in time.
X. Li, Q. Dou, H. Chen, C.-W. Fu, X. Qi, D. L. Belavỳ, G. Armbrecht, D. Felsenberg, G. Zheng, and P.-A. Heng, “3d multi-scale fcn with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality mr images,” Medical Image Analysis , 2018
2018
Closest in time.
M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “Gan-based data augmentation for improved liver lesion classification,” 2018
2018
Closest in time.