Journal of Optoelectronics · Laser, Volume. 35, Issue 10, 1050(2024)
A precise segmentation algorithm suitable for corneal deformation regions
[1] [1] SANTODOMINGO-RUBIDO J, CARRACEDO G, SUZAKI A, et al. Keratoconus: An updated review[J]. Contact Lens & Anterior Eye, 2022, 45(3): 101559.
[2] [2] SHARIF R, BAK-NIELSEN S, HJORTDAL J , et al. Pathogenesis of Keratoconus: The intriguing therapeutic potential of Prolactin- inducible protein[J]. Progress in Retinal and Eye Research, 2018, 67: 150-167.
[6] [6] SHEN Y, FANG Z J, GAO Y B, et al. Coronary arteries segmentation based on 3D FCN with attention gate and level set function[J]. IEEE Access, 2019, 7: 42826-42835.
[7] [7] CAELLES S, MANINIS K K, PONT- TUSET J, et al. One-shot video object segmentation[J]. IEEE Transactions on Pattern Analysis and and Machine Intelligence, 2019, 41(6): 1515-1530.
[8] [8] CASANOVA A, PINHEIRO P O, ROSTAMZADEH N, et al. Reinforced active learning for image segmentation[EB/OL].(2020-02-16)[2023-04-06]. https://arxiv.org/abs/2002.06583.
[10] [10] LU X K, WANG W G, SHEN J B, et al. Learning video object segmentation from unlabeled videos[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 8957-8967.
[11] [11] ELIASOF M, ZIKRI N B, TREISTER E. Unsupervised image semantic segmentation through superpixels and graph neural networks[EB/OL].(2022-10-21)[2024-04-22]. https://arxiv.org/abs/2210.11810.
[12] [12] ZHOU T F, LI J W, LI X Y, et al. Target-aware object discovery and association for unsupervised video multi-object segmentation[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 6981-6990.
[13] [13] REN S C, LIU W X, LIU Y T, et al. Reciprocal transformations for unsupervised video object segmentation[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 15430-15439.
[14] [14] LIAN L, WU Z R, YU S X. Improving unsupervised video object segmentation with motion-appearance synergy[EB/OL].(2022-12-17)[2024-04-22]. https://arxiv.org/abs/2212.08816.
[15] [15] HASAN M K, DAHAL L, SAMARAKOON P N, et al. DSNet: automatic dermoscopic skin lesion segmentation[J]. Computers in Biology and Medicine, 2020, 120: 103738.
[16] [16] RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation, medical image computing and computer-assisted intervention[C]//MICCAI 2015, Medical Image Computing and Computer-Assisted Intervention, October 5-9, 2015, Munich, Germany. Cham: Springer, 2015: 234-241.
[17] [17] CHEN L C, ZHU Y K, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//European Conference on Computer Vision (ECCV), September 8-14, 2018, Munich, Germany. Cham: Springer, 2018: 801-818.
[18] [18] HE K M, GKIOXARI G, DOLL'AR P, et al. Mask R-CNN[C]/IEEE International Conference on Computer Vision, October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 2961-2969.
[19] [19] KUMAR P, NAGAR P, ARORA C, et al. U-Segnet: fully convolutional neural network based automated brain tissue segmentation tool[C]//2018 25th IEEE International Conference on Image Processing (ICIP), October 07-10, 2018, Athens, Greece. New York: IEEE, 2018: 3503-3507.
[21] [21] LIU S, ZHUANG Z, ZHENG Y F, et al. A VAN-based multi-scale cross-attention mechanism for skin lesion segmentation network[J]. IEEE Access, 2023, 11: 81953-81964.
[22] [22] HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023.
[24] [24] SZEGEDY C, LIU W, JIA Y Q. Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 07-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 1-9.
[25] [25] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770-778.
[26] [26] OKTAY O, SCHLEMPER J, LE FOLGOC L, et al. Attention U-Net: Learning where to look for the pancreas[EB/OL].(2018-04-11)[2024-04-22]. https://arxiv.org/abs/1804.03999.
[27] [27] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//IEEE International Conference on Computer Vision, October 22-29, 2017, Venice, Italy. New York: IEEE, 2017: 2999-3007.
Get Citation
Copy Citation Text
LI Jing, LI Mingyue, LAI Yuqing, BAI Jinshuai. A precise segmentation algorithm suitable for corneal deformation regions[J]. Journal of Optoelectronics · Laser, 2024, 35(10): 1050
Category:
Received: Apr. 22, 2024
Accepted: Dec. 31, 2024
Published Online: Dec. 31, 2024
The Author Email: