Computer Science ›› 2021, Vol. 48 ›› Issue (8): 118-124.doi: 10.11896/jsjkx.200600150
Special Issue: Medical Imaging
• Computer Graphics & Multimedia • Previous Articles Next Articles
YE Zhong-yu, WU Meng-lin
CLC Number:
[1]MIYATA M,OOTO S,HATA M,et al.Detection of myopicchoroidal neovascularization using optical coherence tomography angiography[J].American Journal of Ophthalmology,2016,165:108-114. [2]WU J Y,YOU G D,YAN Y,et al.Analysis of blood vessels of diabetic retinopathy based on image segmentation[J].Chinese Medical Equipment Journal,2017,38(6):27-29,40. [3]ZHU S,SHI F,XIANG D,et al.Choroid neovascularizationgrowth prediction with treatment based on reaction-diffusion model in 3-D OCT images[J].IEEE Journal of Biomedical and Health Informatics,2017,21(6):1667-1674. [4]XIANG D,TIAN H,YANG X,et al.Automatic segmentation of retinal layer in OCT images with choroidal neovascularization[J].IEEE Transactions on Image Processing,2018,27(12):5880-5891. [5]LI Y,NIU S,JI Z,et al.Automated choroidal neovascularization detection for time series SD-OCT images[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention.Cham:Springer,2018:381-388. [6]BRANKIN E,MCCUILAGH P,BLACK N,et al.The optimisation of thresholding techniques for the identification of choroidal neovascular membranes in exudative age-related macular de-generation[C]//19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06).IEEE,2006:430-435. [7]BRANKIN E,MCCULLAGH P,PATTON W,et al.Identification of choroidal neovascularisation on fluorescein angiograms using gradient vector flow active contours[C]//2008 Internatio-nal Machine Vision and Image Processing Conference.IEEE,2008:165-169. [8]LIANG L M,HUANG C L,SHI F,et al.Vascular Segmentation of Fundus Image of Level Set Based on Shape Prior[J].Compu-ter Science,2018,41(7):1678-1692. [9]ROY A G,CONJETI S,KARRI S P K,et al.ReLayNet:retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks[J].Biomedical Optics Express,2017,8(8):3627-3642. [10]CHEN L C,ZHU Y,PAPANDREOU G,et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European Conference on Computer Vision (ECCV).2018:801-818. [11]XUE J,YAN S,WANG Y,et al.Unsupervised Segmentation of Choroidal Neovascularization for Optical Coherence Tomography Angiography by Grid Tissue-Like Membrane Systems[J].IEEE Access,2019,7:143058-143066. [12]PERDOMO O,OTÁLORA S,GONZÁLEZ F A,et al.Oct-net:A convolutional network for automatic classification of normal and diabetic macular edema using sd-oct volumes[C]//2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).IEEE,2018:1423-1426. [13]WANG G,LUO P,LIN L,et al.Learning object interactions and descriptions for semantic image segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:5859-5867. [14]QIN X,WANG Z,BAI Y,et al.FFA-Net:Feature Fusion Attention Network for Single Image Dehazing[C]//AAAI.2020:11908-11915. [15]SINHA A,DOLZ J.Multi-scale self-guided attention for medical image segmentation[J].IEEE Journal of Biomedical and Health Informatics,2020,25(1):121-130. [16]JETLEY S,LORD N A,LEE N,et al.Learn to pay attention[J].arXiv:1804.02391,2018. [17]RONNEBERGER O,FISCHER P,BROX T.U-net:Convolu-tional networks for biomedical image segmentation[C]//International Conference on Medical ImageComputing and Compu-ter-assisted Intervention.Cham:Springer,2015:234-241. [18]OKTAY O,SCHLEMPER J,FOLGOC L L,et al.Attention u-net:Learning where to look for the pancreas[J].arXiv:1804.03999,2018. [19]SHI X J,CHEN Z R,WANG H,et al.Convolutional LSTM network:A machine learning approach for precipitation nowcasting[J].arXiv:1506.04214,2105. [20]TAKIKAWA T,ACUNA D,JAMPANI V,et al.Gated-scnn:Gated shape cnns for semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:5229-5238. [21]REN M,ZEMEL R S.End-to-end instance segmentation withrecurrent attention[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:6656-6664. [22]ROMERA-PAREDES B,TORR P H S.Recurrent instance segmentation[C]//European Conference on Computer Vision.Cham:Springer,2016:312-329. [23]SALVADOR A,BELLVER M,CAMPOS V,et al.Recurrentneural networks for semantic instance segmentation[J].arXiv:1712.00617,2017. [24]ZHANG Y,JI Z,WANG Y,et al.Mpb-cnn:a multi-scale parallel branch cnn for choroidal neovascularization segmentation in sd-oct images[J].OSA Continuum,2019,2(3):1011-1027. [25]HE K,SUN J.Convolutional Neural Networks at Constrained Time Cost[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2015:5353-5360. |
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