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Micro-Net : a unified model for segmentation of various objects in microscopy images

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Raza, Shan-e-Ahmed, Cheung, Linda, Shaban, Muhammad, Graham, Simon, Epstein, D. B. A., Pelengaris, Stella, Khan, Michael and Rajpoot, Nasir M. (2019) Micro-Net : a unified model for segmentation of various objects in microscopy images. Medical Image Analysis, 52 . pp. 160-173. doi:10.1016/j.media.2018.12.003

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Official URL: http://dx.doi.org/10.1016/j.media.2018.12.003

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Abstract

Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms recent deep learning algorithms.

Item Type: Journal Article
Subjects: Q Science > QH Natural history
R Medicine > RB Pathology
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- )
Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH): Fluorescence microscopy, Histology, Pathological -- Technique
Journal or Publication Title: Medical Image Analysis
Publisher: Elsevier Science BV
ISSN: 1361-8415
Official Date: February 2019
Dates:
DateEvent
February 2019Published
15 December 2018Available
14 December 2018Accepted
Volume: 52
Page Range: pp. 160-173
DOI: 10.1016/j.media.2018.12.003
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
BB/K018868/1[BBSRC] Biotechnology and Biological Sciences Research Councilhttp://dx.doi.org/10.13039/501100000268

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