Abstract
Optic Cup and Optic Disc segmentation plays a vital role in retinal image analysis, with significant implications for automated diagnosis. In fundus images, due to the difference between intra-class features and the complexity of inter-class features, existing methods often fail to explicitly consider the correlation and discrimination of target edge features. To overcome this limitation, our method aims to capture interdependency and consistency by involving differences in pixels on the edge. To accomplish this, we propose an Edge-Prior Contrastive Transformer (EPCT) architecture to augment the focus on the indistinct edge information. Our method incorporates pixel-to-pixel and pixel-to-region contrastive learning to achieve higher-level semantic information and global contextual feature representations. Furthermore, we incorporate prior information on edges with the Transformer model, which aims to capture the prior knowledge of the location and structure of the target edges. In addition, we propose an anchor sampling strategy tailored to the edge regions to achieve efficient edge features. Experimental results on three publicly available datasets demonstrate the effectiveness of our proposed method, as it achieves excellent segmentation performance.
Y. Feng and S. Zhou—Equal contribution.
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References
Almazroa, A., Burman, R., Raahemifar, K., Lakshminarayanan, V.: Optic disc and optic cup segmentation methodologies for glaucoma image detection: a survey. J. Ophthalmol. 2015, 180972 (2015)
Aquino, A., Gegúndez-Arias, M.E., Marín, D.: Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. TMI 29(11), 1860–1869 (2010)
Cao, H., et al.: Swin-Unet: unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) Computer Vision, ECCV 2022 Workshops. LNCS, vol. 13803, pp. 205–218. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25066-8_9
Chen, J., et al.: TransUNet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: ICML, pp. 1597–1607 (2020)
Fan, D.-P., Ji, G.-P., Zhou, T., Chen, G., Fu, H., Shen, J., Shao, L.: PraNet: parallel reverse attention network for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 263–273. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59725-2_26
Firdaus-Nawi, M., Noraini, O., Sabri, M., Siti-Zahrah, A., Zamri-Saad, M., Latifah, H.: DeepLabv3+ _encoder-decoder with atrous separable convolution for semantic image segmentation. Pertanika J. Trop. Agric. Sci. 34(1), 137–143 (2011)
Fu, H., Cheng, J., Xu, Y., Wong, D.W.K., Liu, J., Cao, X.: Joint optic disc and cup segmentation based on multi-label deep network and polar transformation. TMI 37(7), 1597–1605 (2018)
Fumero, F., Alayón, S., Sanchez, J.L., Sigut, J., Gonzalez-Hernandez, M.: RIM-ONE: an open retinal image database for optic nerve evaluation. In: CBMS, pp. 1–6 (2011)
Gu, Z.: CE-Net: context encoder network for 2D medical image segmentation. TMI 38(10), 2281–2292 (2019)
He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: CVPR, pp. 9729–9738 (2020)
Huang, H., et al.: UNet 3+: a full-scale connected Unet for medical image segmentation. In: ICASSP, pp. 1055–1059 (2020)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Meth. 18(2), 203–211 (2021)
Jiang, Y., et al.: JointRCNN: a region-based convolutional neural network for optic disc and cup segmentation. TBE 67(2), 335–343 (2019)
Joshi, G.D., Sivaswamy, J., Krishnadas, S.: Optic disk and cup segmentation from monocular color retinal images for glaucoma assessment. TMI 30(6), 1192–1205 (2011)
Li, S., Sui, X., Luo, X., Xu, X., Liu, Y., Goh, R.: Medical image segmentation using squeeze-and-expansion transformers. arXiv preprint arXiv:2105.09511 (2021)
Liu, B., Pan, D., Shuai, Z., Song, H.: ECSD-Net: a joint optic disc and cup segmentation and glaucoma classification network based on unsupervised domain adaptation. Comput. Meth. Prog. Bio. 213, 106530 (2022)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)
Lu, S.: Accurate and efficient optic disc detection and segmentation by a circular transformation. TMI 30(12), 2126–2133 (2011)
Luo, L., Xue, D., Pan, F., Feng, X.: Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning. IJARS 16(6), 905–914 (2021)
Luthra, A., Sulakhe, H., Mittal, T., Iyer, A., Yadav, S.: Eformer: edge enhancement based transformer for medical image denoising. arXiv arXiv:2109.08044 (2021)
Misra, I., Maaten, L.: Self-supervised learning of pretext-invariant representations. In: CVPR, pp. 6707–6717 (2020)
Mittapalli, P.S., Kande, G.B.: Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma. Biomed. Sig. Process. 24, 34–46 (2016)
Oktay, O., et al.: Attention U-Net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Orlando, J.I., Fu, H., Breda, J.B., Van Keer, K., Bathula, D.R., et al.: Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Media 59, 101570 (2020)
Pachade, S., Porwal, P., Kokare, M., Giancardo, L., Mériaudeau, F.: NENet: Nested EfficientNe and adversarial learning for joint optic disc and cup segmentation. Media 74, 102253 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Sivaswamy, J., Krishnadas, S., Joshi, G.D., Jain, M., Tabish, A.U.S.: Drishti-GS: retinal image dataset for optic nerve head (ONH) segmentation. In: ISBI, pp. 53–56 (2014)
Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., Van Gool, L.: Exploring cross-image pixel contrast for semantic segmentation. In: ICCV, pp. 7303–7313 (2021)
Wang, X., Zhang, R., Shen, C., Kong, T., Li, L.: Dense contrastive learning for self-supervised visual pre-training. In: CVPR, pp. 3024–3033 (2021)
Zhao, X., et al.: Contrastive learning for label efficient semantic segmentation. In: ICCV, pp. 10623–10633 (2021)
Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR, pp. 6881–6890 (2021)
Acknowledgment
This work was supported in part by the National Science Foundation of China under Grants 62076142 and 62241603, in part by the National Key Research and Development Program of Ningxia under Grant 2023AAC05009, 2022BEG03158 and 2021BEB0406.
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Feng, Y., Zhou, S., Wang, Y., Li, Z., Liu, H. (2024). Edge-Prior Contrastive Transformer for Optic Cup and Optic Disc Segmentation. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14429. Springer, Singapore. https://doi.org/10.1007/978-981-99-8469-5_35
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DOI: https://doi.org/10.1007/978-981-99-8469-5_35
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