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纸质出版日期： 2021-09-16 ，
录用日期： 2021-02-11
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周涛, 董雅丽, 霍兵强, 刘珊, 马宗军. U-Net网络医学图像分割应用综述[J]. 中国图象图形学报, 2021,26(9):2058-2077.
Tao Zhou, Yali Dong, Bingqiang Huo, Shan Liu, Zongjun Ma. U-Net and its applications in medical image segmentation: a review[J]. Journal of Image and Graphics, 2021,26(9):2058-2077.
病灶精确分割对患者病情评估和治疗方案制定有重要意义，由于医学图像中病灶与周围组织的对比度低，同一疾病病灶边缘和形状存在很大差异，从而增加了分割难度。U-Net是近些年深度学习研究中的热点，为医生提供了一致性的量化病灶方法，一定程度上提高了分割性能，广泛应用于医学图像语义分割领域。本文对U-Net网络进行全面综述。阐述U-Net网络的基本结构和工作原理；从编码器个数、多个U-Net级联、与U-Net结合的其他模型以及3D U-Net等方面对U-Net网络模型的改进进行总结；从卷积操作、下采样操作、上采样操作、跳跃连接、模型优化策略和数据增强等方面对U-Net网络结构改进进行总结；从残差思想、密集思想、注意力机制和多机制组合等方面对U-Net的改进机制进行总结；对U-Net网络未来的发展方向进行展望。本文对U-Net网络的原理、结构和模型进行详细总结，对U-Net网络的发展具有一定积极意义。
Medical imaging has been a proactive tool for doctors to diagnose and treat diseases via the qualitative and quantitative analyses based on non-invasive lesions. Medical images have been interpreted via computer tomography (CT)
X-ray
magnetic resonance imaging (MRI) and positron emission tomography (PET). The barriers of medical image segmentation need to be resolved due to low contrast amongst the lesion
the surrounding tissue and blurred edges of the lesion. Labeling manually for hundreds of slices of organs or lesions has been quite time-consuming due to anatomy of the human body and shape of lesions. Manual labeling has intended to high subjective and low reproducibility. Doctors have been beneficial from a automatically locating
segmenting and quantifying lesions. Deep learning has been used widely in medical image processing. Deep learning-based U-Net has played a key role in the lesions segmentation. The encoding and decoding ways has made U-Net structures simply and symmetrically. Features extraction of medical images has been realized via convolution and down-sampling operations. The image segmentation mask via the transposed convolution and concatenation has been interpreted. A small-sized dataset has achieved qualified medical image segmentation. U-Net has been summarized and analyzed on the four aspects: the definition of U-Net
the upgrading of U-Net model
the setup of U-Net structure and the mechanism of U-Net. Four research areas have been proposed as below: 1) the basic structure and working principle of U-Net via convolution operation
down sampling
up sampling and concatenation. 2) U-Net network model have been demonstrated in three aspects in the context of the number of encoders
multiple U-Net cascades and other models combined with U-Net. U-Net based network have been divided into two
three and four encoders further in terms of the amount of encoders: Y-Net
Ψ-Net and multi-path dense U-Net. Multiple U-Nets cascade has been categorized into multiple U-Nets in series and multiple U-Nets in parallel based on the cascades mode of multiple U-Nets. In addition U-Net has improved the segmentation performance on the aspects of dual tree complex wavelet transform
local difference method
level set
random walk
graph cutting
CNNs(convolutional neural networks) and deep reinforcement learning. The upgrading of U-Net network structure have been divided into six subcategories including image augmentation
convolution operation
down-sampling operation
up-sampling operation
model optimization strategies and concatenation. Image enhancement has be divided into elastic deformation
geometric transformation
generative adversarial networks (GAN)
Wasserstein generative adversarial networks (WGAN) and real-time image enhancement further. The convolution operation has been improved via padding mode and convolution redesign. The padding mode mentioned has adapted constant padding
zero padding
replication padding and reflection padding and improvements to dilated convolution
inception module and asymmetric convolution. The down-sampling has been improved via max-pooling
average-pooling
stride convolution
dilated convolution
inception module and spatial pyramid pooling. Several up-sampling improvements have illustrated simultaneously via sub-pixel convolution
transposed convolution
nearest neighbor interpolation
bilinear interpolation and trilinear interpolation. Model optimization strategies have been divided into two aspects in detail of activation function and normalization
the improvements of activation function includes rectified linear unit(ReLU)
parametric ReLU(PReLU)
random ReLU(RReLU)
leaky ReLU(LReLU)
hard exponential linear sigmoid squahing(HardELiSH) and exponential linear sigmoid squashing(ELiSH)
and normalization method. The improvements have been to shown based on batch normalization
group normalization
instance normalization and layer normalization. The concatenation based improvement has been one of the future research area. The current concatenation improvements have been mainly realized via attention mechanism
new concatenation
feature reuse and de-convolution with activation function
annotation information fusion from Siamese network. The improved mechanisms in the U-Net network have been emphasized based on residual mechanism
dense mechanism
attention mechanism and the multi-mechanisms integration. The segmentation performance of the network can be enhanced. The further four research areas in U-Net have been illustrated as below: 1) the generalization of deep learning methods cannot be customized to fit the segmentation network for specific scenarios in the future. 2) Supervised deep learning models have required a lot of annotated images labeled for treatment. Unsupervised and semi-supervised deep learning models have been a vital research work further. 3) The low interpretability of U-Net network has lead the low acceptance in the mechanism of its operation.4) More accurate segmentation mask with fewer parameters has been obtained via good quality network structure. The precise manual segmentation has been so time-consuming and labor intensive. The simplified and quick semi-automatic segmentation has relied on the parameters and user-specified image preprocessing. The deep learning-based U-Net network has been segmented the lesions quickly
accurately and consistently. The structure
improvements and further research areas of U-Net network have been analyzed to the development of U-Net network.
U-Net医学图像语义分割网络结构网络模型
U-Netmedical imagesemantic segmentationnetwork structurenetwork model
Ai L M, Li T D, Liao F Y and Shi K Z. 2020. Magnetic resonance brain tumor image segmentation based on attention U-Net. Laser and Optoelectronics Progress, 57(14): 279-286
艾玲梅, 李天东, 廖福元, 石康珍. 2020. 基于注意力U-Net的脑肿瘤磁共振图像分割. 激光与光电子学进展, 57(14): 279-286[DOI: 10.3788/LOP57.141030]
Aitken A, Ledig C, Theis L, Caballero J, Wang Z and Shi W Z. 2017. Checkerboard artifact free sub-pixel convolution: a note on sub-pixel convolution, resize convolution and convolution resize[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1707.02937.pdfhttps://arxiv.org/pdf/1707.02937.pdf
Alom Z, Yakopcic C, Taha T M and Asari V K. 2018. Nuclei segmentation with recurrent residual convolutional neural networks based U-Net (R2U-Net)//Proceedings of the NAECON 2018-IEEE National Aerospace and Electronics Conference. Dayton, USA: IEEE: 228-233[DOI: 10.1109/NAECON.2018.8556686http://dx.doi.org/10.1109/NAECON.2018.8556686]
Arjovsky M, Chintala S and Bottou L. 2017. Wasserstein GAN[EB/OL]. [2020-11-15].http://arxiv.org/pdf/1701.07875.pdfhttp://arxiv.org/pdf/1701.07875.pdf
Ba J L, Kiros J R and Hinton G E. 2016. Layer normalization[EB/OL]. [2020-11-15].https://xueshu.baidu.com/s?wd=paperuri%3A%28bce50fa3f4f88216264baf4ff6c26f5d%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fabs%2F1607.06450&ie=utf-8&sc_us=3874236310593320015https://xueshu.baidu.com/s?wd=paperuri%3A%28bce50fa3f4f88216264baf4ff6c26f5d%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fabs%2F1607.06450&ie=utf-8&sc_us=3874236310593320015
Basirat M and Roth P M. 2018. The quest for the golden activation function[EB/OL]. [2020-11-5].https://arxiv.org/pdf/1808.00783.pdfhttps://arxiv.org/pdf/1808.00783.pdf
Byra M, Jarosik P, Szubert A, Galperin M, Ojeda-Fournier H, Olson L, O'Boyle M, Comstock C and Andre M. 2020. Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. Biomedical Signal Processing and Control, 61: #102027[DOI: 10.1016/j.bspc.2020.102027]
Chen H, Dou Q, Yu L Q, Qin J and Heng P A. 2018. VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR images. NeuroImage, 170: 446-455[DOI: 10.1016/j.neuroimage.2017.04.041]
Chen L C, Papandreou G, Schroff F and Adam H. 2017. Rethinking atrous convolution for semantic image segmentation[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1706.05587.pdfhttps://arxiv.org/pdf/1706.05587.pdf
Cheng Z P, Guo K X, Wu C F, Shen J K and Qu L. 2019. U-Net cascaded with dilated convolution for medical image registration//Proceedings of 2019 Chinese Automation Congress (CAC). Hangzhou, China: IEEE: 3647-3651[DOI: 10.1109/CAC48633.2019.8996569http://dx.doi.org/10.1109/CAC48633.2019.8996569]
ÇiçekÖ, Abdulkadir A, Lienkamp S S, Brox T and Ronneberger O. 2016. 3D U-Net: learning dense volumetric segmentation from sparse annotation[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1606.06650.pdfhttps://arxiv.org/pdf/1606.06650.pdf
Das S, Deka A, Iwahori Y, Bhuyan M K, Iwamoto T and Ueda J. 2019. Contour-aware residual W-Net for nuclei segmentation. Procedia Computer Science, 159: 1479-1488[DOI: 10.1016/j.procs.2019.09.318]
Ding X F, Peng Y X, Shen C M and Zeng T Y. 2020. CAB U-Net: an end-to-end category attention boosting algorithm for segmentation. Computerized Medical Imaging and Graphics, 84: #101764[DOI: 10.1016/j.compmedimag.2020.101764]
Dolz J, Ben Ayed I and Desrosiers C. 2019. Dense multi-path U-Net for ischemic stroke lesion segmentation in multiple image modalities//Proceedings of the 4th International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Granada, Spain: Springer: 271-282[DOI: 10.1007/978-3-030-11723-8_27http://dx.doi.org/10.1007/978-3-030-11723-8_27]
Dou Q, Yu L Q, Chen H, Jin Y M, Yang X, Qin J and Heng P A. 2017. 3D deeply supervised network for automated segmentation of volumetric medical images. Medical Image Analysis, 41: 40-54[DOI: 10.1016/j.media.2017.05.001]
Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial nets//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press: 2670-2680
Guimarães H R, Nagano H and Silva D W. 2020. Monaural speech enhancement through deep wave-U-net. Expert Systems with Applications, 158: #113582[DOI: 10.1016/j.eswa.2020.113582]
Hariyani Y S, Eom H and Park C. 2020. DA-Capnet: dual attention deep learning based on U-Net for nailfold capillary segmentation. IEEE Access, 8: 10543-10553[DOI: 10.1109/ACCESS.2020.2965651]
He K M, Zhang X Y, Ren S Q and Sun J. 2015a. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE: 1026-1034[DOI: 10.1109/ICCV.2015.123http://dx.doi.org/10.1109/ICCV.2015.123]
He K M, Zhang X Y, Ren S Q and Sun J. 2015b. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9): 1904-1916[DOI: 10.1109/TPAMI.2015.2389824]
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE: 770-778[DOI: 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90]
Hu J F, Wang H, Gao S B, Bao M K, Liu T, Wang Y X and Zhang J C. 2019. S-UNet: a bridge-style u-net framework with a saliency mechanism for retinal vessel segmentation. IEEE Access, 7: 174167-174177[DOI: 10.1109/ACCESS.2019.2940476]
Hu K, Liu C, Yu X, Zhang J, He Y and Zhu H C. 2018. A 2.5D cancer segmentation for MRI images based on U-Net//Proceedings of the 5th International Conference on Information Science and Control Engineering (ICISCE). Zhengzhou, China: IEEE: 6-10[DOI: 10.1109/ICISCE.2018.00011http://dx.doi.org/10.1109/ICISCE.2018.00011]
Huang G, Liu Z, Van Der Maaten L and Weinberger K Q. 2017. Densely connected convolutional networks//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 2261-2269[DOI: 10.1109/CVPR.2017.243http://dx.doi.org/10.1109/CVPR.2017.243]
Huang Y J, Shi Z F, Wang Z Q and Wang Z. 2020. Improved U-Net based on mixed loss function for liver medical image segmentation. Laser and Optoelectronics Progress, 57(22): 74-83
黄泳嘉, 史再峰, 王仲琦, 王哲. 2020. 基于混合损失函数的改进型U-Net肝部医学影像分割方法. 激光与光电子学进展, 57(22): 74-83[DOI: 10.3788/LOP57.221003]
Ibtehaz N and Rahman M S. 2020. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 121: 74-87[DOI: 10.1016/j.neunet.2019.08.025]
Ioffe S and Szegedy C. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1502.03167v2.pdfhttps://arxiv.org/pdf/1502.03167v2.pdf
Jin Q, Meng Z, Sun C, Cui H and Su R. 2020. RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans. Frontiers in Bioengineering and Biotechnology, 8: #605132[DOI: 10.3389/fbios.2020.605132]
Kang S, Iwana B K and Uchida S. 2021. Complex image processing with less data-Document image binarization by integrating multiple pre-trained U-Net modules. Pattern Recognition, 109: #107577[DOI: 10.1016/j.patcog.2020.107577]
Khanna A, Londhe N D, Gupta S and Semwal A. 2020. A deep Residual U-Net convolutional neural network for automated lung segmentation in computed tomography images. Biocybernetics and Biomedical Engineering, 40(3): 1314-1327[DOI: 10.1016/j.bbe.2020.07.007]
Kim J H, Lee H, Hong S J, Kim S, Park J, Hwang J Y and Choi J P. 2019. Objects segmentation from high-resolution aerial images using U-Net with pyramid pooling layers. IEEE Geoscience and Remote Sensing Letters, 16(1): 115-119[DOI: 10.1109/LGRS.2018.2868880]
Kuang Z, Deng X B, Yu L, Wang H K, Li T S and Wang S W. 2020. Ψ-Net: focusing on the border areas of intracerebral hemorrhage on CT images. Computer Methods and Programs in Biomedicine, 194: #105546[DOI: 10.1016/j.cmpb.2020.105546]
Lan H R, Jiang D H, Yang C C, Gao F and Gao F. 2020. Y-Net: hybrid deep learning image reconstruction for photoacoustic tomography in vivo. Photoacoustics, 20: #100197[DOI: 10.1016/j.pacs.2020.100197]
Lee S, Negishi M, Urakubo H, Kasai H and Ishii S. 2020. Mu-net: multi-scale U-net for two-photon microscopy image denoising and restoration. Neural Networks, 125: 92-103[DOI: 10.1016/j.neunet.2020.01.026]
Li C, Tan Y S, Chen W, Luo X, He Y L, Gao Y M and Li F. 2020a. ANU-Net: attention-based nested U-Net to exploit full resolution features for medical image segmentation. Computers and Graphics, 90: 11-20[DOI: 10.1016/j.cag.2020.05.003]
Li J F, Chen C X and Wang L. 2020b. Fusion algorithm of multi-spectral images based on dual-tree complex wavelet transform and frequency-domain U-Net. Journal of Biomedical Engineering Research, 39(2): 145-150
李建飞, 陈春晓, 王亮. 2020b. 基于双树复小波变换和频域U-Net的多光谱图像融合算法. 生物医学工程研究, 39(2): 145-150[DOI: 10.19529/j.cnki.1672-6278.2020.02.07]
Li S, Tso G K F and He K J. 2020c. Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation. Expert Systems with Applications, 145: #113131[DOI: 10.1016/j.eswa.2019.113131]
Lian S, Luo Z M, Zhong Z, Lin X, Su S Z and Li S Z. 2018. Attention guided U-Net for accurate iris segmentation. Journal of Visual Communication and Image Representation, 56: 296-304[DOI: 10.1016/j.jvcir.2018.10.001]
Liu L L, Cheng J H, Quan Q, Wu F X, Wang Y P and Wang J X. 2020. A survey on U-shaped networks in medical image segmentations. Neurocomputing, 409: 244-258[DOI: 10.1016/j.neucom.2020.05.070]
Liu Y W, Qi N, Zhu Q and Li W R. 2019a. CR-U-Net: cascaded U-Net with residual mapping for liver segmentation in CT images//Proceedings of 2019 IEEE Visual Communications and Image Processing (VCIP). Sydney, Australia: IEEE: 1-4[DOI: 10.1109/VCIP47243.2019.8966072http://dx.doi.org/10.1109/VCIP47243.2019.8966072]
Liu T, Tian Y, Zhao S F, Huang X Y and Wang Q J. 2019b. Automatic whole heart segmentation using a two-stage U-Net framework and an adaptive threshold window. IEEE Access, 7: 83628-83636[DOI: 10.1109/ACCESS.2019.2923318]
Liu Z, Song Y Q, Sheng V S, Wang L M, Jiang R, Zhang X L and Yuan D Q. 2019c. Liver CT sequence segmentation based with improved U-Net and graph cut. Expert Systems with Applications, 126: 54-63[DOI: 10.1016/j.eswa.2019.01.055]
Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE: 3431-3440[DOI: 10.1109/CVPR.2015.7298965http://dx.doi.org/10.1109/CVPR.2015.7298965]
Maas A L, Hannun A Y and Ng A Y. 2013. Rectifier nonlinearities improve neural network acoustic models//Proceedings of the 30th International Conference on Machine Learning. Atlanta, USA: JMLR: 3
Man Y Z, Huang Y S B, Feng J Y, Li X and Wu F. 2019. Deep Q learning driven CT pancreas segmentation with geometry-aware U-Net. IEEE Transactions on Medical Imaging, 38(8): 1971-1980[DOI: 10.1109/TMI.2019.2911588]
Mehta R and Sivaswamy J. 2017. M-net: a convolutional neural network for deep brain structure segmentation//Proceedings of the 14th IEEE International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, Australia: IEEE: 437-440[DOI: 10.1109/ISBI.2017.7950555http://dx.doi.org/10.1109/ISBI.2017.7950555]
Milletari F, Navab N and Ahmadi S A. 2016. V-Net: fully convolutional neural networks for volumetric medical image segmentation//Proceedings of the 4th International Conference on 3D Vision (3DV). Stanford, USA: IEEE: 565-571[DOI: 10.1109/3DV.2016.79http://dx.doi.org/10.1109/3DV.2016.79]
Mnih V and Heess N, Graves A and Kavukcuoglu K. 2014. Recurrent models of visual attention[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1406.6247v1.pdfhttps://arxiv.org/pdf/1406.6247v1.pdf
Nair V, Hinton G E. 2010. Rectified linear units improve restricted boltzmann machines//Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML-10). Haifa, Israel: Omnipress: 807-814
Nasr-Esfahani E, RafieiS, Jafari M H, Karimi N, Wrobel J S, Samavi S and Reza Soroushmehr S M. 2019. Dense pooling layers in fully convolutional network for skin lesion segmentation. Computerized Medical Imaging and Graphics, 78: #101658[DOI: 10.1016/j.compmedimag.2019.101658]
Pan G, Zheng Y X, Guo S and Lyu Y Z. 2020. Automatic sewer pipe defect semantic segmentation based on improved U-Net. Automation in Construction, 119: #103383[DOI: 10.1016/j.autcon.2020.103383]
Patel G, Tekchandani H and Verma S. 2019. Cellular segmentation of bright-field absorbance images using residual U-Net//Proceedings of 2019 International Conference on Advances in Computing, Communication and Control (ICAC3). Mumbai, India: IEEE: 1-5[DOI: 10.1109/ICAC347590.2019.9036737http://dx.doi.org/10.1109/ICAC347590.2019.9036737]
Qin P L, Li C P, Chen J and Chai R. 2019. Research on improved algorithm of object detection based on feature pyramid. Multimedia Tools and Applications, 78(1): 913-927[DOI: 10.1007/s11042-018-5870-3]
Qing C, Yu J, Xiao C B and Duan J. 2020. Deep convolutional neural network for semantic image segmentation. Journal of Image and Graphics, 25(6): 1069-1090
青晨, 禹晶, 肖创柏, 段娟. 2020. 深度卷积神经网络图像语义分割研究进展. 中国图象图形学报, 25(6): 1069-1090[DOI: 10.11834/jig.190355]
Rad R M, Saeedi P, Au J and Havelock J. 2020. Trophectoderm segmentation in human embryo images via inceptioned U-Net. Medical Image Analysis, 62: #101612[DOI: 10.1016/j.media.2019.101612]
Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer: 234-241[DOI: 10.1007/978-3-319-24574-4_28http://dx.doi.org/10.1007/978-3-319-24574-4_28]
Salih M M, Salih M E and Ahmed M A A. 2019. Enhancement of U-Net performance in MRI brain Tumour segmentation using HardELiSH activation function//Proceedings of 2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). Khartoum, Sudan: IEEE: 1-5[DOI: 10.1109/ICCCEEE46830.2019.9071235http://dx.doi.org/10.1109/ICCCEEE46830.2019.9071235]
Sambyal N, Saini P, Syal R and Gupta V. 2020. Modified U-Net architecture for semantic segmentation of diabetic retinopathy images. Biocybernetics and Biomedical Engineering, 40(3): 1094-1109[DOI: 10.1016/j.bbe.2020.05.006]
Scherer D, Müller A and Behnke S. 2010. Evaluation of pooling operations in convolutional architectures for object recognition//Proceedings of the 20th International Conference on Artificial Neural Networks. Thessaloniki, Greece: Springer: 92-101[DOI: 10.1007/978-3-642-15825-4_10http://dx.doi.org/10.1007/978-3-642-15825-4_10]
Seo H, Huang C, Bassenne M, Xiao R X and Xing L. 2020. Modified U-Net (mU-Net) with incorporation of object-dependent high level features for improved liver and liver-tumor segmentation in CT images. IEEE Transactions on Medical Imaging, 39(5): 1316-1325[DOI: 10.1109/TMI.2019.2948320]
Shepard D S. 1984. Computer mapping: the SYMAP interpolation algorithm//Gaile G L, Willmott C J, eds. Spatial Statistics and Models. Dordrecht: Springer: 133-145[DOI: 10.1007/978-94-017-3048-8_7http://dx.doi.org/10.1007/978-94-017-3048-8_7]
Shi W Z, Caballero J, Huszár F, Totz J, Aitken A P, Bishop R, Rueckert D and Wang Z H. 2016. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE: 1874-1883[DOI: 10.1109/CVPR.2016.207http://dx.doi.org/10.1109/CVPR.2016.207]
Simard P Y, Steinkraus D and Platt J C. 2003. Best practices for convolutional neural networks applied to visual document analysis//Proceedings of the 7th International Conference on Document Analysis and Recognition. Edinburgh, UK: IEEE: 958-963[DOI: 10.1109/ICDAR.2003.1227801http://dx.doi.org/10.1109/ICDAR.2003.1227801]
Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, USA: IEEE: 1-9[DOI: 10.1109/CVPR.2015.7298594http://dx.doi.org/10.1109/CVPR.2015.7298594]
Szegedy C, Vanhoucke V, Ioffe S, Shlens J and Wojna Z. 2016. Rethinking the inception architecture for computer vision//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE: 2818-2826[DOI: 10.1109/CVPR.2016.308http://dx.doi.org/10.1109/CVPR.2016.308]
Tang Z Q, Peng X, Li K and Metaxas D N. 2020. Towards efficient U-Nets: a coupled and quantized approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(8): 2038-2050[DOI: 10.1109/TPAMI.2019.2907634]
Tian X, Wang L and Ding Q. 2019. Review of image semantic segmentation based on deep learning. Journal of Software, 30(2): 440-468
田萱, 王亮, 丁琪. 2019. 基于深度学习的图像语义分割方法综述. 软件学报, 30(2): 440-468[DOI: 10.13328/j.cnki.jos.005659]
Tong G F, Li Y, Chen H R, Zhang Q C and Jiang H Y. 2018. Improved U-NET network for pulmonary nodules segmentation. Optik, 174: 460-469[DOI: 10.1016/j.ijleo.2018.08.086]
Ulyanov D, Vedaldi A and Lempitsky V. 2016. Instance normalization: the missing ingredient for fast stylization[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1607.08022v3.pdfhttps://arxiv.org/pdf/1607.08022v3.pdf
Wan T, Zhao L, Feng H X, Li DY, Tong C and Qin Z C. 2020. Robust nuclei segmentation in histopathology using ASPPU-Net and boundary refinement. Neurocomputing, 408: 144-156[DOI: 10.1016/j.neucom.2019.08.103]
Wang P Q, Chen P F, Yuan Y, Liu D, Huang Z H, Hou X D and Cottrell G. 2017. Understanding convolution for semantic segmentation[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1702.08502v1.pdfhttps://arxiv.org/pdf/1702.08502v1.pdf
Wang Z H, Liu Z, Song Y Q and Zhu Y. 2019. Densely connected deep U-Net for abdominal multi-organ segmentation//Proceedings of 2019 IEEE International Conference on Image Processing (ICIP). Taipei, China: IEEE: 1415-1419[DOI: 10.1109/ICIP.2019.8803103http://dx.doi.org/10.1109/ICIP.2019.8803103]
Wu B F, Fang Y and Lai X B. 2020. Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach. Computerized Medical Imaging and Graphics, 82: #101719[DOI: 10.1016/j.compmedimag.2020.101719]
Wu J, Zhang Y, Wang K and Tang X Y. 2019. Skip connection U-Net for white matter hyperintensities segmentation from MRI. IEEE Access, 7: 155194-155202[DOI: 10.1109/ACCESS.2019.2948476]
Wu Y X and He K M. 2018. Group normalization[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1803.08494.pdfhttps://arxiv.org/pdf/1803.08494.pdf
Xia X D and Kulis B. 2017. W-Net: a deep model for fully unsupervised image segmentation[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1711.08506.pdfhttps://arxiv.org/pdf/1711.08506.pdf
Xu B, Wang N Y, Chen T Q and Li M. 2015. Empirical evaluation of rectified activations in convolutional network[EB/OL]. [2020-11-15].https://arxiv.org/pdf/1505.00853.pdfhttps://arxiv.org/pdf/1505.00853.pdf
Xu L, Liu M Y, Shen Z R, Wang H, Liu X W, Wang X, Wang S Y, Li T F, Yu S M, Hou M, Guo J H, Zhang J C and He Y H. 2020. DW-Net: a cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography. Computerized Medical Imaging and Graphics, 80: #101690[DOI: 10.1016/j.compmedimag.2019.101690]
Yang J, Faraji M and Basu A. 2019a. Robust segmentation of arterial walls in intravascular ultrasound images using dual path U-Net. Ultrasonics, 96: 24-33[DOI: 10.1016/j.ultras.2019.03.014]
Yang X F, Li X T, Ye Y M, Zhang X F, Zhang H J, Huang X H and Zhang B W. 2019b. Road detection via deep residual dense U-Net//Proceedings of 2019 International Joint Conference on Neural Networks (IJCNN). Budapest, Hungary: IEEE: 1-7[DOI: 10.1109/IJCNN.2019.8851728http://dx.doi.org/10.1109/IJCNN.2019.8851728]
Yang Y Y, Feng C and Wang R F. 2020. Automatic segmentation model combining U-Net and level set method for medical images, Expert Systems with Applications, 153: #113419[DOI: 10.1016/j.eswa.2020.113419]
Yin Y J, Xu D, Wang X G and Zhang L. 2021. AGUnet: annotation-guided U-net for fast one-shot video object segmentation. Pattern Recognition, 110: #107580[DOI: 10.1016/j.patcog.2020.107580]
Ying W Q, Li J H, Yu W, Zheng K J, Deng Y L and Li J C. 2020. U-Net with dense encoder, residual decoder and depth-wise skip connections//Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN). Glasgow, UK: IEEE: 1-6[DOI: 10.1109/IJCNN48605.2020.9207371http://dx.doi.org/10.1109/IJCNN48605.2020.9207371]
Yu N B, Liu J N, Gao L, Sun Z W and Han J D. 2020. Auto-segmentation method based on deep learning for the knee joint in MR images. Chinese Journal of Scientific Instrument, 41(6): 140-149
于宁波, 刘嘉男, 高丽, 孙泽文, 韩建达. 2020. 基于深度学习的膝关节MR图像自动分割方法. 仪器仪表学报, 41(6): 140-149[DOI: 10.19650/j.cnki.cjsi.J2006199]
Zhang Z, Wu C D, Coleman S and Kerr D. 2020a. DENSE-INception U-net for medical image segmentation. Computer Methods and Programs in Biomedicine, 192: #105395[DOI: 10.1016/j.cmpb.2020.105395]
Zhang H, Zhu H Q and Ling X F. 2020b. Polar coordinate sampling-based segmentation of overlapping cervical cells using attention U-Net and random walk. Neurocomputing, 383: 212-223[DOI: 10.1016/j.neucom.2019.12.036]
Zhang J L and Sheng G Q. 2020c. First arrival picking of microseismic signals based on nested U-Net and Wasserstein Generative Adversarial Network. Journal of Petroleum Science and Engineering, 195: #107527[DOI: 10.1016/j.petrol.2020.107527]
Zhang T Q, Kang B, Meng X F, Liu Y L and Zhou Y. 2020d. U-Net based intracranial hemorrhage recognition. Journal of Beijing University of Posts and Telecommunications, 43(3): 92-98
张天麒, 康波, 孟祥飞, 刘奕琳, 周颖. 2020. 基于U-Net的颅内出血识别算法. 北京邮电大学学报, 43(3): 92-98[DOI: 10.13190/j.jbupt.2019-146]
Zhang W X, Zhu Z C, Zhang Y H, Wang X Y and Ding G P. 2020e. Cell image segmentation based on residual block and attention mechanism. Acta Optica Sinica, 40(17): 76-83
张文秀, 朱振才, 张永合, 王新宇, 丁国鹏. 2020e. 基于残差块和注意力机制的细胞图像分割方法. 光学学报, 40(17): 76-83[DOI: 10.3788/AOS202040.171001]
Zhang C J, An R and Ma L. 2021. Building change detection in remote sensing image based on improved U-Net. Computer Engineering and Applications, 57(3): 239-246
张翠军, 安冉, 马丽. 2021. 改进U-Net的遥感图像中建筑物变化检测. 计算机工程与应用, 57(3): 239-246[DOI: 10.3778/j.issn.1002-8331.2005-0.331]
Zhang M D, Liu G C, Tina J X and Liu Y F. 2019a. Improved U-Net with multi-scale cross connection and dilated convolution for brain tumor segmentation//Proceedings of 2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE). Shenzhen, China: IEEE: 1-5[DOI: 10.1109/ICMIPE47306.2019.9098234http://dx.doi.org/10.1109/ICMIPE47306.2019.9098234]
Zhang W, Tang P, Zhao L J and Huang Q Q. 2019b. A comparative study of U-Nets with various convolution components for building extraction//Proceedings of 2019 Joint Urban Remote Sensing Event (JURSE). Vannes, France: IEEE: 1-4[DOI: 10.1109/JURSE.2019.8809055http://dx.doi.org/10.1109/JURSE.2019.8809055]
Zhao B C, Chen X, Li Z, Yu Z W, Yao S, Yan L X, Wang Y Q, Liu Z Y, Liang C H and Han C. 2020a. Triple U-net: hematoxylin-aware nuclei segmentation with progressive dense feature aggregation. Medical Image Analysis, 65: #101786[DOI: 10.1016/j.media.2020.101786]
Zhao W S, Jiang D H, Queralta J P and Westerlund T. 2020b. MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net. Informatics in Medicine Unlocked, 19: #100357[DOI: 10.1016/j.imu.2020.100357]
Zhong S H, Guo X M and Zheng Y N. 2020. Improved U-Net network for lung nodule segmentation. Computer Engineering and Applications. 56(17): 203-209
钟思华, 郭兴明, 郑伊能. 2020. 改进U-Net网络的肺结节分割方法. 计算机工程与应用. 56(17): 203-209[DOI: 10.3778/j.issn.1002-8331.1911-1024]
Zhou X Y and Yang G Z. 2019. Normalization in training U-Net for 2-D biomedical semantic segmentation. IEEE Robotics and Automation Letters, 4(2): 1792-1799[DOI: 10.1109/LRA.2019.2896518]
Zhou Z W, Rahman Siddiquee M, Tajbakhsh N and Liang J M. 2018. UNet++: a nested U-Net architecture for medical image segmentation//Proceedings of the 4th Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Granada, Spain: Springer: 3-11[DOI: doi.org/10.1007/978-3-030-00889-5_1http://dx.doi.org/doi.org/10.1007/978-3-030-00889-5_1]
Zhu H and Qin P L. 2019. U-Net pulmonary nodule detection algorithm based on multi-scale feature structure. Computer Engineering, 45(4): 254-261
朱辉, 秦品乐. 2019. 基于多尺度特征结构的U-Net肺结节检测算法. 计算机工程, 45(4): 254-261[DOI: 10.19678/j.issn.1000-3428.0051769]
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