Micro-Net: A unified model for segmentation of various objects in microscopy images |
| |
| Institution: | 1. Division of Molecular Pathology, The Institute of Cancer Research, UK;2. Department of Computer Science, University of Warwick, UK;3. School of Life Sciences, University of Warwick, UK;4. Department of Mathematics, University of Warwick, UK;5. Department of Pathology, University Hospitals Coventry and Warwickshire, UK;6. The Alan Turing Institute, London, UK;7. Centre for Evolution and Cancer, The Institute of Cancer Research, London, UK;1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China;2. School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China;1. Mathematics for Real World Systems Centre for Doctoral Training, University of Warwick, Coventry, CV4 7AL, UK;2. Department of Computer Science, University of Warwick, UK;3. Department of Computer Science and Engineering, The Chinese University of Hong Kong, China;4. Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK;5. The Alan Turing Institute, London, UK;1. Department of Biomedical Engineering, University of Florida, FL 32611 USA;2. Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA;3. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Drive 637553 Singapore;1. Biomedical Computer Vision Group, BioQuant, IPMB, Heidelberg University and DKFZ, Im Neuenheimer Feld 267, Heidelberg, Germany;2. High-Content Analysis of the Cell (HiCell) and Advanced Biological Screening Facility, BioQuant, Heidelberg University, Germany;3. Division of Chromatin Networks, DKFZ and BioQuant, Heidelberg, Germany |
| |
| 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. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
