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Open AccessArticle

Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection

1
Harbin Institute of Technology, Shenzhen 518055, China
2
German Cancer Research Center, 69120 Heidelberg, Germany
3
School of Computer Engineering, Shenzhen Polytechnic, Shenzhen 518055, China
*
Authors to whom correspondence should be addressed.
Cells 2019, 8(5), 499; https://doi.org/10.3390/cells8050499
Received: 2 March 2019 / Revised: 22 April 2019 / Accepted: 14 May 2019 / Published: 23 May 2019
(This article belongs to the Special Issue Bioinformatics and Computational Biology 2019)
As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are commonly small and dense with many overlapping nuclei in the images. In order to detect nuclei, the most important key step is to segment the cell targets accurately. Based on Mask RCNN model, we designed a multi-path dilated residual network, and realized a network structure to segment and detect dense small objects, and effectively solved the problem of information loss of small objects in deep neural network. The experimental results on two typical nuclear segmentation data sets show that our model has better recognition and segmentation capability for dense small targets. View Full-Text
Keywords: nuclei segmentation; microscopic pathological images observation; object detection; deep learning nuclei segmentation; microscopic pathological images observation; object detection; deep learning
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Wang, E.K.; Zhang, X.; Pan, L.; Cheng, C.; Dimitrakopoulou-Strauss, A.; Li, Y.; Zhe, N. Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection. Cells 2019, 8, 499.

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