NDLI: DenseUNet: densely connected UNet for electron microscopy image segmentation

Content Provider	IET Digital Library
Author	Cao, Yue Liu, Shigang Peng, Yali Li, Jun
Abstract	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.
Starting Page	2682
Ending Page	2689
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 12, Oct (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/12
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1527
Journal	IET Image Processing
Publisher Date	2020-04-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Measurement And Imaging CNN-based Method Computer Vision And Image Processing Technique Computer-aided Diagnosis Convolutional Neural Nets Convolutional Neural Network-based Method DenseUNet Disease Diseases Electron Microscopy Electron Microscopy Image Segmentation EM Image Segmentation Encoder-decoder Architecture Variants Encoding Health Physics Image Segmentation ISBI 2012 EM Dataset Medical And Biomedical Uses of Field Medical Image Processing Neural Computing Technique Optical, Image And Video Signal Processing Parameter Efficient Method Patient Diagnostic Method And Instrumentation Radiations Radioactivity Smart Design Specific Pathogens Weighted Loss
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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