INet: Convolutional Networks for Biomedical Image Segmentation
Encoder-decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may discard spatial information, and therefore low-level semantics are lost. Feature fusion methods can mitigate these problems but feature maps...
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| Published in: | IEEE access Vol. 9; pp. 16591 - 16603 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 2169-3536, 2169-3536 |
| Online Access: | Get full text |
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| Abstract | Encoder-decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may discard spatial information, and therefore low-level semantics are lost. Feature fusion methods can mitigate these problems but feature maps of different scales cannot be easily fused because down- and upsampling change the spatial resolution of feature map. To address these issues, we propose INet, which enlarges receptive fields by increasing the kernel sizes of convolutional layers in steps (e.g., from <inline-formula> <tex-math notation="LaTeX">3\times 3 </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">7\times 7 </tex-math></inline-formula> and then <inline-formula> <tex-math notation="LaTeX">15\times 15 </tex-math></inline-formula>) instead of downsampling. Inspired by an Inception module, INet extracts features by kernels of different sizes through concatenating the output feature maps of all preceding convolutional layers. We also find that the large kernel makes the network feasible for biomedical image segmentation. In addition, INet uses two overlapping max-poolings, i.e., max-poolings with stride 1, to extract the sharpest features. Fixed-size and fixed-channel feature maps enable INet to concatenate feature maps and add multiple shortcuts across layers. In this way, INet can recover low-level semantics by concatenating the feature maps of all preceding layers and expedite the training by adding multiple shortcuts. Because INet has additional residual shortcuts, we compare INet with a UNet system that also has residual shortcuts (ResUNet). To confirm INet as a backbone architecture for biomedical image segmentation, we implement dense connections on INet (called DenseINet) and compare it to a DenseUNet system with residual shortcuts (ResDenseUNet). INet and DenseINet require 16.9% and 37.6% fewer parameters than ResUNet and ResDenseUNet, respectively. In comparison with six encoder-decoder approaches using nine public datasets, INet and DenseINet demonstrate efficient improvements in biomedical image segmentation. INet outperforms DeepLabV3, which implementing atrous convolution instead of downsampling to increase receptive fields. INet also outperforms two recent methods (named HRNet and MS-NAS) that maintain high-resolution representations and repeatedly exchange the information across resolutions. |
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| AbstractList | Encoder–decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may discard spatial information, and therefore low-level semantics are lost. Feature fusion methods can mitigate these problems but feature maps of different scales cannot be easily fused because down- and upsampling change the spatial resolution of feature map. To address these issues, we propose INet, which enlarges receptive fields by increasing the kernel sizes of convolutional layers in steps (e.g., from [Formula Omitted] to [Formula Omitted] and then [Formula Omitted]) instead of downsampling. Inspired by an Inception module, INet extracts features by kernels of different sizes through concatenating the output feature maps of all preceding convolutional layers. We also find that the large kernel makes the network feasible for biomedical image segmentation. In addition, INet uses two overlapping max-poolings, i.e., max-poolings with stride 1, to extract the sharpest features. Fixed-size and fixed-channel feature maps enable INet to concatenate feature maps and add multiple shortcuts across layers. In this way, INet can recover low-level semantics by concatenating the feature maps of all preceding layers and expedite the training by adding multiple shortcuts. Because INet has additional residual shortcuts, we compare INet with a UNet system that also has residual shortcuts (ResUNet). To confirm INet as a backbone architecture for biomedical image segmentation, we implement dense connections on INet (called DenseINet) and compare it to a DenseUNet system with residual shortcuts (ResDenseUNet). INet and DenseINet require 16.9% and 37.6% fewer parameters than ResUNet and ResDenseUNet, respectively. In comparison with six encoder–decoder approaches using nine public datasets, INet and DenseINet demonstrate efficient improvements in biomedical image segmentation. INet outperforms DeepLabV3, which implementing atrous convolution instead of downsampling to increase receptive fields. INet also outperforms two recent methods (named HRNet and MS-NAS) that maintain high-resolution representations and repeatedly exchange the information across resolutions. Encoder-decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may discard spatial information, and therefore low-level semantics are lost. Feature fusion methods can mitigate these problems but feature maps of different scales cannot be easily fused because down- and upsampling change the spatial resolution of feature map. To address these issues, we propose INet, which enlarges receptive fields by increasing the kernel sizes of convolutional layers in steps (e.g., from <inline-formula> <tex-math notation="LaTeX">3\times 3 </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">7\times 7 </tex-math></inline-formula> and then <inline-formula> <tex-math notation="LaTeX">15\times 15 </tex-math></inline-formula>) instead of downsampling. Inspired by an Inception module, INet extracts features by kernels of different sizes through concatenating the output feature maps of all preceding convolutional layers. We also find that the large kernel makes the network feasible for biomedical image segmentation. In addition, INet uses two overlapping max-poolings, i.e., max-poolings with stride 1, to extract the sharpest features. Fixed-size and fixed-channel feature maps enable INet to concatenate feature maps and add multiple shortcuts across layers. In this way, INet can recover low-level semantics by concatenating the feature maps of all preceding layers and expedite the training by adding multiple shortcuts. Because INet has additional residual shortcuts, we compare INet with a UNet system that also has residual shortcuts (ResUNet). To confirm INet as a backbone architecture for biomedical image segmentation, we implement dense connections on INet (called DenseINet) and compare it to a DenseUNet system with residual shortcuts (ResDenseUNet). INet and DenseINet require 16.9% and 37.6% fewer parameters than ResUNet and ResDenseUNet, respectively. In comparison with six encoder-decoder approaches using nine public datasets, INet and DenseINet demonstrate efficient improvements in biomedical image segmentation. INet outperforms DeepLabV3, which implementing atrous convolution instead of downsampling to increase receptive fields. INet also outperforms two recent methods (named HRNet and MS-NAS) that maintain high-resolution representations and repeatedly exchange the information across resolutions. Encoder-decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may discard spatial information, and therefore low-level semantics are lost. Feature fusion methods can mitigate these problems but feature maps of different scales cannot be easily fused because downand upsampling change the spatial resolution of feature map. To address these issues, we propose INet, which enlarges receptive fields by increasing the kernel sizes of convolutional layers in steps (e.g., from 3 × 3 to 7 × 7 and then 15 × 15) instead of downsampling. Inspired by an Inception module, INet extracts features by kernels of different sizes through concatenating the output feature maps of all preceding convolutional layers. We also find that the large kernel makes the network feasible for biomedical image segmentation. In addition, INet uses two overlapping max-poolings, i.e., max-poolings with stride 1, to extract the sharpest features. Fixed-size and fixed-channel feature maps enable INet to concatenate feature maps and add multiple shortcuts across layers. In this way, INet can recover low-level semantics by concatenating the feature maps of all preceding layers and expedite the training by adding multiple shortcuts. Because INet has additional residual shortcuts, we compare INet with a UNet system that also has residual shortcuts (ResUNet). To confirm INet as a backbone architecture for biomedical image segmentation, we implement dense connections on INet (called DenseINet) and compare it to a DenseUNet system with residual shortcuts (ResDenseUNet). INet and DenseINet require 16.9% and 37.6% fewer parameters than ResUNet and ResDenseUNet, respectively. In comparison with six encoder- decoder approaches using nine public datasets, INet and DenseINet demonstrate efficient improvements in biomedical image segmentation. INet outperforms DeepLabV3, which implementing atrous convolution instead of downsampling to increase receptive fields. INet also outperforms two recent methods (named HRNet and MS-NAS) that maintain high-resolution representations and repeatedly exchange the information across resolutions. |
| Author | Weng, Weihao Zhu, Xin |
| Author_xml | – sequence: 1 givenname: Weihao orcidid: 0000-0002-0869-3409 surname: Weng fullname: Weng, Weihao organization: Biomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu, Japan – sequence: 2 givenname: Xin surname: Zhu fullname: Zhu, Xin email: zhuxin@u-aizu.ac.jp organization: Biomedical Information Engineering Laboratory, The University of Aizu, Aizu-Wakamatsu, Japan |
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| Cites_doi | 10.1109/CVPRW.2017.156 10.1016/j.compbiomed.2019.05.002 10.1109/SSCI.2017.8280804 10.1109/TPAMI.2015.2389824 10.1111/hpb.12056 10.1007/s10278-017-9983-4 10.1109/CVPR.2016.98 10.1155/2017/9283480 10.1007/978-3-030-00889-5_1 10.1109/TBME.2009.2035102 10.1109/TMI.2017.2664042 10.1109/ISBI.2018.8363547 10.1007/s00268-004-7435-z 10.1109/ISBI.2008.4540988 10.1109/TPAMI.2007.56 10.1007/978-3-319-46484-8_29 10.1007/978-3-319-46976-8_19 10.1109/CVPR.2016.314 10.1016/j.media.2018.10.004 10.1109/5.726791 10.1109/CVPR.2015.7299173 10.1109/CVPR.2017.243 10.1109/LGRS.2018.2802944 10.1109/CVPR.2015.7298965 10.1109/TMI.2013.2290491 10.1002/mp.13141 10.1016/j.eswa.2014.09.020 10.1109/CVPR.2007.383157 10.1109/CVPR.2017.189 10.1109/CVPR.2016.90 10.1007/s40273-014-0198-y 10.1371/journal.pone.0140381 10.1109/CVPR.2019.00584 10.1038/s41598-019-48802-0 10.1016/j.neunet.2019.08.025 10.1109/TPAMI.2016.2644615 10.1109/CVPR.2015.7298594 10.1109/TMI.2018.2845918 |
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| References | ref56 ref12 ref15 ref14 health (ref47) 2016 ref53 ref52 ref55 ref11 ref54 ref10 bilic (ref45) 2019 krizhevsky (ref2) 2012 ref17 ref16 ref19 ref18 liu (ref27) 2015 liu (ref37) 2018 ref50 szegedy (ref36) 2017 ref46 luo (ref33) 2016 ref48 bakas (ref30) 2018 ref42 dumoulin (ref31) 2016 ref41 ref43 hinton (ref38) 1986; 1 simpson (ref44) 2019 ref49 simonyan (ref3) 2014 ref8 ref9 ref4 ref6 ref5 chen (ref32) 2017 ref40 ref35 ref34 wang (ref23) 2016 ref1 ref39 ref24 ref26 ref25 ref22 ref21 ronneberger (ref20) 2015 ref28 chollet (ref51) 2015 ref29 ray (ref13) 2004 goodfellow (ref7) 2016 |
| References_xml | – ident: ref41 doi: 10.1109/CVPRW.2017.156 – ident: ref42 doi: 10.1016/j.compbiomed.2019.05.002 – year: 2016 ident: ref7 publication-title: Deep Learning – year: 2015 ident: ref27 article-title: ParseNet: Looking wider to see better publication-title: arXiv 1506 04579 – ident: ref11 doi: 10.1109/SSCI.2017.8280804 – year: 2016 ident: ref47 publication-title: Ultrasound Nerve Segmentation – year: 2017 ident: ref32 article-title: Rethinking atrous convolution for semantic image segmentation publication-title: arXiv 1706 05587 – ident: ref35 doi: 10.1109/TPAMI.2015.2389824 – year: 2004 ident: ref13 publication-title: Information Technology Principles and Applications – ident: ref50 doi: 10.1111/hpb.12056 – year: 2015 ident: ref51 publication-title: Keras Github – ident: ref8 doi: 10.1007/s10278-017-9983-4 – ident: ref26 doi: 10.1109/CVPR.2016.98 – ident: ref54 doi: 10.1155/2017/9283480 – year: 2016 ident: ref23 article-title: Deeply-fused nets publication-title: arXiv 1605 07716 – ident: ref52 doi: 10.1007/978-3-030-00889-5_1 – start-page: 9605 year: 2018 ident: ref37 article-title: An intriguing failing of convolutional neural networks and the coordconv solution publication-title: Proc Adv Neural Inf Process Syst – ident: ref10 doi: 10.1109/TBME.2009.2035102 – ident: ref46 doi: 10.1109/TMI.2017.2664042 – ident: ref16 doi: 10.1109/ISBI.2018.8363547 – year: 2014 ident: ref3 article-title: Very deep convolutional networks for large-scale image recognition publication-title: arXiv 1409 1556 – ident: ref49 doi: 10.1007/s00268-004-7435-z – year: 2019 ident: ref44 article-title: A large annotated medical image dataset for the development and evaluation of segmentation algorithms publication-title: arXiv 1902 09063 – start-page: 4898 year: 2016 ident: ref33 article-title: Understanding the effective receptive field in deep convolutional neural networks publication-title: Proc Adv Neural Inf Process Syst – start-page: 1097 year: 2012 ident: ref2 article-title: ImageNet classification with deep convolutional neural networks publication-title: Proc Adv Neural Inf Process Syst – ident: ref18 doi: 10.1109/ISBI.2008.4540988 – ident: ref34 doi: 10.1109/TPAMI.2007.56 – ident: ref22 doi: 10.1007/978-3-319-46484-8_29 – ident: ref21 doi: 10.1007/978-3-319-46976-8_19 – year: 2016 ident: ref31 article-title: A guide to convolution arithmetic for deep learning publication-title: ArXiv 1603 07285 – ident: ref28 doi: 10.1109/CVPR.2016.314 – ident: ref53 doi: 10.1016/j.media.2018.10.004 – start-page: 1 year: 2017 ident: ref36 article-title: Inception-v4, inception-ResNet and the impact of residual connections on learning publication-title: Proc 31st AAAI Conf Artif Intell – ident: ref1 doi: 10.1109/5.726791 – ident: ref55 doi: 10.1109/CVPR.2015.7299173 – ident: ref6 doi: 10.1109/CVPR.2017.243 – ident: ref39 doi: 10.1109/LGRS.2018.2802944 – year: 2018 ident: ref30 article-title: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge publication-title: arXiv 1811 02629 – ident: ref14 doi: 10.1109/CVPR.2015.7298965 – volume: 1 year: 1986 ident: ref38 article-title: Distributed representations publication-title: Parallel Distributed Processing Explorations in the Microstructure of Cognition Foundations – ident: ref9 doi: 10.1109/TMI.2013.2290491 – ident: ref17 doi: 10.1002/mp.13141 – ident: ref19 doi: 10.1016/j.eswa.2014.09.020 – ident: ref12 doi: 10.1109/CVPR.2007.383157 – ident: ref25 doi: 10.1109/CVPR.2017.189 – ident: ref5 doi: 10.1109/CVPR.2016.90 – ident: ref48 doi: 10.1007/s40273-014-0198-y – ident: ref43 doi: 10.1371/journal.pone.0140381 – ident: ref24 doi: 10.1109/CVPR.2019.00584 – ident: ref40 doi: 10.1038/s41598-019-48802-0 – year: 2019 ident: ref45 article-title: The liver tumor segmentation benchmark (LiTS) publication-title: arXiv 1901 04056 – ident: ref56 doi: 10.1016/j.neunet.2019.08.025 – ident: ref15 doi: 10.1109/TPAMI.2016.2644615 – ident: ref4 doi: 10.1109/CVPR.2015.7298594 – start-page: 234 year: 2015 ident: ref20 article-title: U-Net: Convolutional networks for biomedical image segmentation publication-title: Proc Int Conf Med Image Comput Comput -Assist Intervent – ident: ref29 doi: 10.1109/TMI.2018.2845918 |
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| Snippet | Encoder-decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may... Encoder–decoder networks are state-of-the-art approaches to biomedical image segmentation, but have two problems: i.e., the widely used pooling operations may... |
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| SubjectTerms | Biomedical image Biomedical imaging Coders Convolution convolutional networks encoder–decoder networks Feature extraction Feature maps Image segmentation Kernel Kernels Medical imaging semantic segmentation Semantics Spatial data Spatial resolution Tumors |
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| Title | INet: Convolutional Networks for Biomedical Image Segmentation |
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