Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images-Reference-Cited by-同舟云学术

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Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images

Published:2022-02-02 Issue:7 Volume:81 Page:9201-9224
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Chanchal Amit Kumar,Lal Shyam^ORCID,Kini Jyoti

Funder

This research work was supported in part by the Science Engineering and Research Board, Department of Science and Technology, Govt. of India

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-021-11873-1.pdf

Reference39 articles.

1. Albayrak A, Bilgin G (2019) Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. Med Biol Eng Comput 57(3):653–665

2. Aatresh AA, Yatgiri RP, Chanchal AK, Kumar A, Ravi A, Das D, Raghavendra BS, Lal S, Kini J (2021) Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images. Comput Med Imaging Graph 93:101975. https://doi.org/10.1016/j.compmedimag.2021.101975

3. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

4. Cousty J, Bertrand G, Najman L, Couprie M (2010) Watershed cuts: Thinnings, shortest path forests, and topological watersheds. IEEE Trans Pattern Anal Mach Intell 32(5):925–939

5. Chen H, Qi X, Yu L, Heng PA (2016) DCAN: deep contour-aware networks for accurate gland segmentation. Computer Vision and Pattern Recognition. arXiv:1604.02677v1 [cs.CV]

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