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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Beschreibung
Zusammenfassung: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)-based methods have been proposed for EM image segmentation and achieved considerable progress. Among those CNN-based methods, UNet is regarded as the state-of-the-art method. However, the UNet usually has millions of parameters to increase training difficulty and is limited by the issue of vanishing gradients. To address those problems, the authors present a novel highly parameter efficient method called DenseUNet, which is inspired by the approach that takes particular advantage of recent advances in both UNet and DenseNet. In addition, they successfully apply the weighted loss, which enables us to boost the performance of segmentation. They conduct several comparative experiments on the ISBI 2012 EM dataset. The experimental results show that their method can achieve state-of-the-art results on EM image segmentation without any further post-processing module or pre-training. Moreover, due to smart design of the model, their approach has much less parameters than currently published encoder–decoder architecture variants for this dataset.
ISSN:1751-9659
1751-9667
DOI:10.1049/iet-ipr.2019.1527