DenseUNet: densely connected UNet for electron microscopy image segmentation

Electron microscopy (EM) image segmentation plays an important role in computer-aided diagnosis of specific pathogens or disease. However, EM image segmentation is a laborious task and needs to impose experts knowledge, which can take up valuable time from research. Convolutional neural network (CNN...

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Veröffentlicht in:	IET image processing Jg. 14; H. 12; S. 2682 - 2689
Hauptverfasser:	Cao, Yue, Liu, Shigang, Peng, Yali, Li, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 16.10.2020
Schlagworte:	CNN‐based methods computer‐aided diagnosis convolutional neural nets convolutional neural network‐based methods DenseUNet disease diseases electron microscopy electron microscopy image segmentation EM image segmentation encoder‐decoder architecture variants encoding image segmentation ISBI 2012 EM dataset medical image processing parameter efficient method smart design Special Section: AI-Powered 3D Vision specific pathogens weighted loss DenseUNet smart design disease parameter efficient method diseases encoding computer-aided diagnosis weighted loss image segmentation ISBI 2012 EM dataset electron microscopy EM image segmentation convolutional neural nets electron microscopy image segmentation CNN-based methods specific pathogens medical image processing convolutional neural network-based methods encoder-decoder architecture variants
ISSN:	1751-9659, 1751-9667
Online-Zugang:	Volltext
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