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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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WRAP-Micro-Net-unified-segmentation-various-objects-microscopy-Rajpoot-2018.pdf - Accepted Version - Requires a PDF viewer. Download (4Mb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.media.2018.12.003
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 | ||||||||
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| Subjects: | Q Science > QH Natural history R Medicine > RB Pathology |
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| Divisions: | Faculty of Science > Computer Science Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics |
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| 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: |
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| 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: |
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