[1] [1] SCHINDELIN J, RUEDEN C T, HINER M C, et al. The ImageJ ecosystem: an open platform for biomedical image analysis[J].Molecular Reproduction and Development, 2015,82(7-8):518-529.

[3] [3] CHRIST P F, ELSHAER M E A, ETTLINGER F, et al. Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Athens Greece:Springer, 2016:415-423.

[4] [4] MILLETARI F,NAVAB N,AHMADI S A. V-net:fully convolutional neural networks for volumetric medical image segmentation[C]// 2016 Fourth International Conference on 3D Vision(3DV). Stanford,CA,USA:IEEE, 2016:565-571.

[5] [5] JIN Q, MENG Z, PHAM T D, et al. DUNet: a deformable network for retinal vessel segmentation[J]. Knowledge-Based Systems,2019(178):149-162.

[6] [6] LIN B S,MICHAEL K,KALRA S,et al. Skin lesion segmentation: U-nets versus clustering[C]// 2017 IEEE Symposium Series on Computational Intelligence(SSCI). Honolulu,HI,USA:IEEE, 2017:1-7.

[7] [7] SIRINUKUNWATTANA K, PLUIM J P W, CHEN H, et al. Gland segmentation in colon histology images: the glas challenge contest[J]. Medical Image Analysis, 2017,35:489-502.

[8] [8] SETIO A A A,TRAVERSO A,DE BEL T,et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images:the LUNA16 challenge[J]. Medical Image Analysis, 2017(42):1-13.

[9] [9] ?I?EK ?zgün,ABDULKADIR Ahmed,LIENKAMP Soeren,et al. 3D U-Net:learning dense volumetric segmentation from sparse annotation[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Athens, Greece:Springer, 2016:424-432.

[11] [11] ZHANG Z,LIU Q,WANG Y. Road extraction by deep residual U-Net[J]. IEEE Geoscience and Remote Sensing Letters, 2018,15(5):749-753.

[12] [12] HE K,ZHANG X,REN S,et al. Deep residual learning for image recognition[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas,NV,USA:IEEE, 2016:770-778.

[13] [13] IBTEHAZ N, RAHMAN M S. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 2020,121:74-87.

[14] [14] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J]. Computer Science, 2014(4):357-361.

[15] [15] CHEN L C,PAPANDREOU G,KOKKINOS I,et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4):834-848.

[16] [16] CHEN L C,PAPANDREOU G,SCHROFF F,et al. Rethinking atrous convolution for semantic image segmentation[J/OL]. arXiv,2017:1706.05587-1-14.

[17] [17] CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]// European Conference on Computer Vision(ECCV). Munich,Germany:Springer, 2018:833-851.

[18] [18] LIN T Y, DOLLáR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// 2017 IEEE Conference on

[19] [19] HE K, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C]// 2017 IEEE International Conference on Computer Vision(ICCV).

[20] [20] WANG P,CHEN P,YUAN Y,et al. Understanding convolution for semantic segmentation[C]// 2018 IEEE Winter Conference on

[21] [21] CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu,HI,USA:IEEE, 2017:1251-1258.

[22] [22] STAAL J, ABRàMOFF M D, NIEMEIJER M, et al. Ridge-based vessel segmentation in color images of the retina[J]. IEEE

[23] [23] OKTAY O,SCHLEMPER J,FOLGOC L L,et al. Attention U-net:learning where to look for the pancreas[C]// 1st Conference on Medical Imaging with Deep Learning(MIDL 2018). Amsterdam, The Netherlands:arXiv, 2018.

[24] [24] ZHOU Z, SIDDIQUEE M R, TAJBAKHSH N, et al. UNet++ : a nested U-net architecture for medical image segmentation[C]//Deep learning in medical image analysis and multimodal learning for clinical decision support. Granada,Spain:PubMed Central?(PMC), 2018:3-11.