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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| Vydané v: | IET image processing Ročník 14; číslo 12; s. 2682 - 2689 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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The Institution of Engineering and Technology
16.10.2020
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| ISSN: | 1751-9659, 1751-9667 |
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| 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. |
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| AbstractList | 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. |
| Author | Li, Jun Liu, Shigang Cao, Yue Peng, Yali |
| Author_xml | – sequence: 1 givenname: Yue surname: Cao fullname: Cao, Yue organization: 2School of Computer Science, Shaanxi Normal University, Xi'an 710119, People's Republic of China – sequence: 2 givenname: Shigang orcidid: 0000-0003-0162-3595 surname: Liu fullname: Liu, Shigang organization: 2School of Computer Science, Shaanxi Normal University, Xi'an 710119, People's Republic of China – sequence: 3 givenname: Yali orcidid: 0000-0002-0055-3081 surname: Peng fullname: Peng, Yali email: pengyl@snnu.edu.cn organization: 2School of Computer Science, Shaanxi Normal University, Xi'an 710119, People's Republic of China – sequence: 4 givenname: Jun surname: Li fullname: Li, Jun organization: 3School of Computer Science and Technology, Nanjing Normal University, Nanjing 210046, People's Republic of China |
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| Copyright | The Institution of Engineering and Technology 2021 The Authors. IET Image Processing published by John Wiley & Sons, Ltd. on behalf of The Institution of Engineering and Technology |
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| Keywords | 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 |
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| Snippet | Electron microscopy (EM) image segmentation plays an important role in computer-aided diagnosis of specific pathogens or disease. However, EM image... Electron microscopy (EM) image segmentation plays an important role in computer‐aided diagnosis of specific pathogens or disease. However, EM image... |
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| SubjectTerms | 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 |
| Title | DenseUNet: densely connected UNet for electron microscopy image segmentation |
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