Fetching the paper…
Reading the bibliography…
Nuclear segmentation and classification within Haematoxylin & Eosin stained histology images is a fundamental prerequisite in the digital pathology work-flow.
X. Yang, H. Li, and X. Zhou, “Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and kalman filter in time-lapse microscopy,” IEEE Transactions on Circuits and Systems I: Regular Papers , vol. 53, no. 11, pp. 2405–2414, 2006
2006
Earlier work this paper cites.
A. E. Carpenter, T. R. Jones, M. R. Lamprecht, C. Clarke, I. H. Kang, O. Friman, D. A. Guertin, J. H. Chang, R. A. Lindquist, J. Moffat et al. , “Cellprofiler: image analysis software for identifying and quantifying cell phenotypes,” Genome biology , vol. 7, no. 10, p. R100, 2006
2006
Earlier work this paper cites.
J. Cheng, J. C. Rajapakse et al. , “Segmentation of clustered nuclei with shape markers and marking function,” IEEE Transactions on Biomedical Engineering , vol. 56, no. 3, pp. 741–748, 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in CVPR09 , 2009
2009
Earlier work this paper cites.
M. N. Gurcan, L. E. Boucheron, A. Can, A. Madabhushi, N. M. Rajpoot, and B. Yener, “Histopathological image analysis: A review,” IEEE reviews in biomedical engineering , vol. 2, pp. 147–171, 2009
2009
Earlier work this paper cites.
K. Nguyen, A. K. Jain, and B. Sabata, “Prostate cancer detection: Fusion of cytological and textural features,” Journal of pathology informatics , vol. 2, 2011
2011
Earlier work this paper cites.
S. Ali and A. Madabhushi, “An integrated region-, boundary-, shape-based active contour for multiple object overlap resolution in histological imagery,” IEEE transactions on medical imaging , vol. 31, no. 7, pp. 1448–1460, 2012
2012
Earlier work this paper cites.
S. Wienert, D. Heim, K. Saeger, A. Stenzinger, M. Beil, P. Hufnagl, M. Dietel, C. Denkert, and F. Klauschen, “Detection and segmentation of cell nuclei in virtual microscopy images: a minimum-model approach,” Scientific reports , vol. 2, p. 503, 2012
2012
Earlier work this paper cites.
Y. Yuan, H. Failmezger, O. M. Rueda, H. R. Ali, S. Gräf, S.-F. Chin, R. F. Schwarz, C. Curtis, M. J. Dunning, H. Bardwell et al. , “Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling,” Science translational medicine , vol. 4, no. 157, pp. 157ra143–157ra143, 2012
2012
Earlier work this paper cites.
M. Veta, P. van Diest, R. Kornegoor, A. Huisman, M. Viergever, and J. Pluim, “Automatic nuclei segmentation in h&e stained breast cancer histopathology images,” PLoS ONE , vol. 8, no. 7, p. e70221, 2013
2013
Earlier work this paper cites.
A. LaTorre, L. Alonso-Nanclares, S. Muelas, J. Peña, and J. DeFelipe, “Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images,” Expert Systems with Applications , vol. 40, no. 16, pp. 6521 – 6530, 2013. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0957417413003904
2013
Earlier work this paper cites.
J. G. Elmore, G. M. Longton, P. A. Carney, B. M. Geller, T. Onega, A. N. Tosteson, H. D. Nelson, M. S. Pepe, K. H. Allison, S. J. Schnitt et al. , “Diagnostic concordance among pathologists interpreting breast biopsy specimens,” Jama , vol. 313, no. 11, pp. 1122–1132, 2015
2015
Earlier work this paper cites.
H. Sharma, N. Zerbe, D. Heim, S. Wienert, H.-M. Behrens, O. Hellwich, and P. Hufnagl, “A multi-resolution approach for combining visual information using nuclei segmentation and classification in histopathological images.” in VISAPP (3) , 2015, pp. 37–46
2015
Earlier work this paper cites.
J. T. Kwak, S. M. Hewitt, S. Xu, P. A. Pinto, and B. J. Wood, “Nucleus detection using gradient orientation information and linear least squares regression,” in Medical Imaging 2015: Digital Pathology , vol. 9420. International Society for Optics and Photonics, 2015, p. 94200N
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, p. 436, 2015
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3431–3440
2015
Cited alongside, same era.
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
Cited alongside, same era.
A. Madabhushi and G. Lee, “Image analysis and machine learning in digital pathology: Challenges and opportunities,” Medical Image Analysis , vol. 33, pp. 170 – 175, 2016, 20th anniversary of the Medical Image Analysis journal (MedIA). [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1361841516301141
2016
Cited alongside, same era.
P. Wang, X. Hu, Y. Li, Q. Liu, and X. Zhu, “Automatic cell nuclei segmentation and classification of breast cancer histopathology images,” Signal Processing , vol. 122, pp. 1–13, 2016
P. Bankhead, M. B. Loughrey, J. A. Fernández, Y. Dombrowski, D. G. McArt, P. D. Dunne, S. McQuaid, R. T. Gray, L. J. Murray, H. G. Coleman et al. , “Qupath: Open source software for digital pathology image analysis,” Scientific reports , vol. 7, no. 1, p. 16878, 2017
2017
Later among the works it cites.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 12, pp. 2481–2495, 2017
2017
Later among the works it cites.
N. Alsubaie, K. Sirinukunwattana, S. E. A. Raza, D. Snead, and N. Rajpoot, “A bottom-up approach for tumour differentiation in whole slide images of lung adenocarcinoma,” in Medical Imaging 2018: Digital Pathology , vol. 10581. International Society for Optics and Photonics, 2018, p. 105810E
2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
M. Liao, Y. qian Zhao, X. hua Li, P. shan Dai, X. wen Xu, J. kai Zhang, and B. ji Zou, “Automatic segmentation for cell images based on bottleneck detection and ellipse fitting,” Neurocomputing , vol. 173, pp. 615 – 622, 2016. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0925231215011406
2016
Cited alongside, same era.
H. Chen, X. Qi, L. Yu, and P.-A. Heng, “Dcan: deep contour-aware networks for accurate gland segmentation,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2016, pp. 2487–2496
2016
Cited alongside, same era.
K. Sirinukunwattana, S. e Ahmed Raza, Y.-W. Tsang, D. R. Snead, I. A. Cree, and N. M. Rajpoot, “Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images.” IEEE Trans. Med. Imaging , vol. 35, no. 5, pp. 1196–1206, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al. , “Tensorflow: A system for large-scale machine learning.” in OSDI , vol. 16, 2016, pp. 265–283
2016
Cited alongside, same era.
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. van der Laak, B. Van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical image analysis , vol. 42, pp. 60–88, 2017
2017
Cited alongside, same era.
D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Annual review of biomedical engineering , vol. 19, pp. 221–248, 2017
2017
Cited alongside, same era.
2018
Closest in time.
K. Sirinukunwattana, D. Snead, D. Epstein, Z. Aftab, I. Mujeeb, Y. W. Tsang, I. Cree, and N. Rajpoot, “Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer,” Scientific reports , vol. 8, no. 1, p. 13692, 2018
2018
Closest in time.
S. Javed, M. M. Fraz, D. Epstein, D. Snead, and N. M. Rajpoot, “Cellular community detection for tissue phenotyping in histology images,” in Computational Pathology and Ophthalmic Medical Image Analysis . Springer, 2018, pp. 120–129
2018
Closest in time.
2018
Closest in time.
S. Graham and N. M. Rajpoot, “Sams-net: Stain-aware multi-scale network for instance-based nuclei segmentation in histology images,” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on . IEEE, 2018, pp. 590–594
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
P. Naylor, M. Laé, F. Reyal, and T. Walter, “Segmentation of nuclei in histopathology images by deep regression of the distance map,” IEEE Transactions on Medical Imaging , 2018
2018
Closest in time.
2018
Closest in time.
G. Corredor, X. Wang, Y. Zhou, C. Lu, P. Fu, K. Syrigos, D. L. Rimm, M. Yang, E. Romero, K. A. Schalper et al. , “Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non–small cell lung cancer,” Clinical Cancer Research , vol. 25, no. 5, pp. 1526–1534, 2019
2019
Closest in time.