[1] [1] TIAN Z,LI X,CHEN Z,et al.Interactive prostate MR image segmentation based on ConvLSTMs and GGNN[J].Neurocomputing,2021,438:84-93.

[2] [2] ZHU Q,DU B,YAN P.Boundary-weighted domain adaptive neural network for prostate MR image segmentation[J].IEEE Transactions on Medical Imaging,2019,39(3):753-763.

[3] [3] ALMEIDA G,TAVARES J M R S.Deep learning in radiation on cology treatment planning for prostate cancer:A systematic review[J].Journal of Medical Systems,2020,44(10):1-15.

[4] [4] HAMBARDE P,TALBAR S,MAHAJAN A,et al.Prostate lesion segmentation in MR images using radiomics based deeply supervised U-Net[J].Biocybernetics and Biomedical Engineering,2020,40(4):1421-1435.

[5] [5] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,39(4):640-651.

[6] [6] TIAN Z,LIU L,ZHANG Z,et al.PSNet:prostate segmentation on MRI based on a convolutional neural network[J].Journal of Medical Imaging (Bellingham,Wash.),2018,5(2):021208.

[7] [7] ZHU QI,DU B,TURKBEY B,et al.Exploiting interslice correlation for MRI prostate image segmentation,from recursive neural networks aspect[J].Complexity,2018,2018:4185279.

[8] [8] RONNEBERGER O,FISCHER P,BROX T.U-net:Convolutional networks for biomedical image segmentation［C］//18th International Conference on Medical Image Computing and Computer-Assisted Intervention,October 5-9, 2015,Munich,Germany.Berlin,Heidelberg:Springer-Verlag,2015:234-241.

[9] [9] MILLETARI F,NAVAB N,AHMADI S A.V-Net:Fully convolutional neural networks for volumetric medical image segmentation ［C］//4th International Conference on 3D Vision,October 25-28,2016,Stanford,CA,USA.New York:IEEE,2016:565-571.

[10] [10] YU L,YANG X,CHEN H,et al.Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images［C］//Thirty-first AAAI Conference on Artificial Intelligence, February 4-9,2017,San Francisco,California,USA.Menlo Park:AAAI Press,2017:66-72.

[11] [11] YI W, DENG Z, HU X, et al. Deep attentional features for prostate segmentation in ultrasound［C］//International Conference on Medical Image Computing and Computer-Assisted Intervention,September 16-20,2018,Granada,Spain.Berlin,Heidelberg: Springer-Verlag,2018:523-530.

[12] [12] CHOLLET F.Xception: Deep learning with depthwise separable convolutions［C］// IEEE Conference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,HI,USA.New York:IEEE,2017:1251-1258.

[13] [13] 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,September 8-14,2018,Munich,Germany.Berlin:Springer,2018:801-818.

[14] [14] HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition［C］//IEEE Conference on Computer Vision and Pattern Recognition,June 27-30,2016,Las Vegas,NV,USA.New York:IEEE,2016:770-778.

[15] [15] HOLSCHNEIDER M,KRONLAND-MARTINET R,MORLET J,et al.A real-time algorithm for signal analysis with the help of the wavelet transform［M］//COMBES J-M,GROSSMANN A,TCHAMITCHIAN P. Wavelets.Berlin,Heidelberg:Springer,1990: 286-297.

[16] [16] HU J,SHEN L,SUN G.Squeeze-and-excitation networks［C］//IEEE Conference on Computer Vision and Pattern Recognition,June 18-23,2018,Salt Lake City,UT,USA.New York:IEEE,2018:7132-7141.