메뉴 건너뛰기




Volumn 2016-December, Issue , 2016, Pages 2487-2496

DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; PATTERN RECOGNITION;

EID: 84986267644     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2016.273     Document Type: Conference Paper
Times cited : (635)

References (41)
  • 3
    • 84973888826 scopus 로고    scopus 로고
    • High-for-low and lowfor-high: Efficient boundary detection from deep object fea-tures and its applications to high-level vision
    • G. Bertasius, J. Shi, and L. Torresani. High-for-low and lowfor-high: Efficient boundary detection from deep object fea-tures and its applications to high-level vision. In ICCV, pages 504-512, 2015.
    • (2015) ICCV , pp. 504-512
    • Bertasius, G.1    Shi, J.2    Torresani, L.3
  • 4
    • 84947424557 scopus 로고    scopus 로고
    • Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks
    • Springer
    • H. Chen, Q. Dou, D. Ni, J.-Z. Cheng, J. Qin, S. Li, and P.-A. Heng. Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. In MICCAI, pages 507-514. Springer, 2015.
    • (2015) MICCAI , pp. 507-514
    • Chen, H.1    Dou, Q.2    Ni, D.3    Cheng, J.-Z.4    Qin, J.5    Li, S.6    Heng, P.-A.7
  • 6
    • 84947419089 scopus 로고    scopus 로고
    • Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks
    • Springer
    • H. Chen, C. Shen, J. Qin, D. Ni, L. Shi, J. C. Cheng, and P.-A. Heng. Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks. In MICCAI, pages 515-522. Springer, 2015.
    • (2015) MICCAI , pp. 515-522
    • Chen, H.1    Shen, C.2    Qin, J.3    Ni, D.4    Shi, L.5    Cheng, J.C.6    Heng, P.-A.7
  • 7
    • 85083954148 scopus 로고    scopus 로고
    • Semantic image segmentation with deep convolutional nets and fully connected CRFs
    • L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected CRFs. In ICLR, 2015.
    • (2015) ICLR
    • Chen, L.-C.1    Papandreou, G.2    Kokkinos, I.3    Murphy, K.4    Yuille, A.L.5
  • 8
    • 84877789057 scopus 로고    scopus 로고
    • Deep neural networks segment neuronal membranes in electron microscopy images
    • D. Ciresan, A. Giusti, L. M. Gambardella, and J. Schmidhuber. Deep neural networks segment neuronal membranes in electron microscopy images. In NIPS, pages 2843-2851, 2012.
    • (2012) NIPS , pp. 2843-2851
    • Ciresan, D.1    Giusti, A.2    Gambardella, L.M.3    Schmidhuber, J.4
  • 9
    • 84973890848 scopus 로고    scopus 로고
    • Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
    • J. Dai, K. He, and J. Sun. Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In ICCV, pages 1635-1643, 2015.
    • (2015) ICCV , pp. 1635-1643
    • Dai, J.1    He, K.2    Sun, J.3
  • 10
    • 84947599684 scopus 로고    scopus 로고
    • Deep learning and structured prediction for the segmentation of mass in mammograms
    • Springer
    • N. Dhungel, G. Carneiro, and A. P. Bradley. Deep learning and structured prediction for the segmentation of mass in mammograms. In MICCAI, pages 605-612. Springer, 2015.
    • (2015) MICCAI , pp. 605-612
    • Dhungel, N.1    Carneiro, G.2    Bradley, A.P.3
  • 11
    • 4444356703 scopus 로고    scopus 로고
    • The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia
    • J. Diamond, N. H. Anderson, P. H. Bartels, R. Montironi, and P. W. Hamilton. The use of morphological characteristics and texture analysis in the identification of tissue composition in prostatic neoplasia. Human Pathology, 35(9):1121-1131, 2004.
    • (2004) Human Pathology , vol.35 , Issue.9 , pp. 1121-1131
    • Diamond, J.1    Anderson, N.H.2    Bartels, P.H.3    Montironi, R.4    Hamilton, P.W.5
  • 13
    • 71749111270 scopus 로고    scopus 로고
    • A boosting cascade for automated detection of prostate cancer from digitized histology
    • Springer
    • S. Doyle, A. Madabhushi, M. Feldman, and J. Tomaszeweski. A boosting cascade for automated detection of prostate cancer from digitized histology. In MICCAI, pages 504-511. Springer, 2006.
    • (2006) MICCAI , pp. 504-511
    • Doyle, S.1    Madabhushi, A.2    Feldman, M.3    Tomaszeweski, J.4
  • 14
    • 0026072872 scopus 로고
    • Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: Experience from a large study with long-term followup
    • C.W. Elston, I. O. Ellis, et al. Pathological prognostic factors in breast cancer. i. the value of histological grade in breast cancer: experience from a large study with long-term followup. Histopathology, 19(5):403-410, 1991.
    • (1991) Histopathology , vol.19 , Issue.5 , pp. 403-410
    • Elston, C.W.1    Ellis, I.O.2
  • 18
    • 84896288043 scopus 로고    scopus 로고
    • A novel polar space random field model for the detection of glandular structures
    • H. Fu, G. Qiu, J. Shu, and M. Ilyas. A novel polar space random field model for the detection of glandular structures. Medical Imaging, IEEE Transactions on, 33(3):764-776, 2014.
    • (2014) Medical Imaging, IEEE Transactions on , vol.33 , Issue.3 , pp. 764-776
    • Fu, H.1    Qiu, G.2    Shu, J.3    Ilyas, M.4
  • 19
    • 0026589997 scopus 로고
    • Histologic grading of prostate cancer: A perspective
    • D. F. Gleason. Histologic grading of prostate cancer: a perspective. Human pathology, 23(3):273-279, 1992.
    • (1992) Human Pathology , vol.23 , Issue.3 , pp. 273-279
    • Gleason, D.F.1
  • 21
    • 85013813121 scopus 로고    scopus 로고
    • Deep residual learning for image recognition
    • K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.
    • (2015) ArXiv Preprint ArXiv , vol.1512 , pp. 03385
    • He, K.1    Zhang, X.2    Ren, S.3    Sun, J.4
  • 23
    • 84921753566 scopus 로고    scopus 로고
    • Gleason grading of prostate tumours with max-margin conditional random fields
    • Springer
    • J. G. Jacobs, E. Panagiotaki, and D. C. Alexander. Gleason grading of prostate tumours with max-margin conditional random fields. In Machine Learning in Medical Imaging, pages 85-92. Springer, 2014.
    • (2014) Machine Learning in Medical Imaging , pp. 85-92
    • Jacobs, J.G.1    Panagiotaki, E.2    Alexander, D.C.3
  • 25
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1097-1105, 2012.
    • (2012) NIPS , pp. 1097-1105
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 27
    • 84945230598 scopus 로고    scopus 로고
    • Fully convolutional networks for semantic segmentation
    • J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, pages 3431-3440, 2015.
    • (2015) CVPR , pp. 3431-3440
    • Long, J.1    Shelhamer, E.2    Darrell, T.3
  • 28
    • 84872534252 scopus 로고    scopus 로고
    • Structure and context in prostatic gland segmentation and classification
    • Springer
    • K. Nguyen, A. Sarkar, and A. K. Jain. Structure and context in prostatic gland segmentation and classification. In MICCAI, pages 115-123. Springer, 2012.
    • (2012) MICCAI , pp. 115-123
    • Nguyen, K.1    Sarkar, A.2    Jain, A.K.3
  • 29
    • 84973867110 scopus 로고    scopus 로고
    • Semantic segmentation with object clique potential
    • X. Qi, J. Shi, S. Liu, R. Liao, and J. Jia. Semantic segmentation with object clique potential. In ICCV, pages 2587-2595, 2015.
    • (2015) ICCV , pp. 2587-2595
    • Qi, X.1    Shi, J.2    Liu, S.3    Liao, R.4    Jia, J.5
  • 30
    • 84951834022 scopus 로고    scopus 로고
    • U-net: Convolutional networks for biomedical image segmentation
    • Springer
    • O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, pages 234-241. Springer, 2015.
    • (2015) MICCAI , pp. 234-241
    • Ronneberger, O.1    Fischer, P.2    Brox, T.3
  • 31
    • 84947475390 scopus 로고    scopus 로고
    • Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation
    • Springer
    • 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 MICCAI, pages 556-564. Springer, 2015.
    • (2015) MICCAI , pp. 556-564
    • Roth, H.R.1    Lu, L.2    Farag, A.3    Shin, H.-C.4    Liu, J.5    Turkbey, E.B.6    Summers, R.M.7
  • 32
    • 78149394261 scopus 로고    scopus 로고
    • Automated analysis of pin-4 stained prostate needle biopsies
    • Springer
    • B. Sabata, B. Babenko, R. Monroe, and C. Srinivas. Automated analysis of pin-4 stained prostate needle biopsies. In Prostate Cancer Imaging, pages 89-100. Springer, 2010.
    • (2010) Prostate Cancer Imaging , pp. 89-100
    • Sabata, B.1    Babenko, B.2    Monroe, R.3    Srinivas, C.4
  • 35
    • 84955289509 scopus 로고    scopus 로고
    • A stochastic polygons model for glandular structures in colon histology images
    • K. Sirinukunwattana, D. Snead, and N. Rajpoot. A stochastic polygons model for glandular structures in colon histology images. Medical Imaging, IEEE Transactions on, 34(11):2366-2378, 2015.
    • (2015) Medical Imaging, IEEE Transactions on , vol.34 , Issue.11 , pp. 2366-2378
    • Sirinukunwattana, K.1    Snead, D.2    Rajpoot, N.3
  • 38
    • 29744433299 scopus 로고    scopus 로고
    • Segmentation of intestinal gland images with iterative region growing
    • H.-S. WU, R. Xu, N. Harpaz, D. Burstein, and J. Gil. Segmentation of intestinal gland images with iterative region growing. Journal of Microscopy, 220(3):190-204, 2005.
    • (2005) Journal of Microscopy , vol.220 , Issue.3 , pp. 190-204
    • Wu, H.-S.1    Xu, R.2    Harpaz, N.3    Burstein, D.4    Gil, J.5
  • 39
    • 84973859794 scopus 로고    scopus 로고
    • Holistically-nested edge detection
    • S. Xie and Z. Tu. Holistically-nested edge detection. In ICCV, pages 1395-1403, 2015.
    • (2015) ICCV , pp. 1395-1403
    • Xie, S.1    Tu, Z.2
  • 40
    • 84937508363 scopus 로고    scopus 로고
    • How transferable are features in deep neural networks?
    • J. Yosinski, J. Clune, Y. Bengio, and H. Lipson. How transferable are features in deep neural networks? In NIPS, pages 3320-3328, 2014.
    • (2014) NIPS , pp. 3320-3328
    • Yosinski, J.1    Clune, J.2    Bengio, Y.3    Lipson, H.4
  • 41
    • 84906348918 scopus 로고    scopus 로고
    • Facial landmark detection by deep multi-task learning
    • Springer
    • Z. Zhang, P. Luo, C. C. Loy, and X. Tang. Facial landmark detection by deep multi-task learning. In ECCV, pages 94-108. Springer, 2014.
    • (2014) ECCV , pp. 94-108
    • Zhang, Z.1    Luo, P.2    Loy, C.C.3    Tang, X.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.