Abstract
Retinal vessel segmentation in ophthalmic images is an essential task to support the computer-aided diagnosis of eye-related diseases. As a non-invasive imaging technique, ultra-wide-field (UWF) fundus imaging provides a large field-of-view (FOV) of \(200^{\circ }\) with full coverage of the retinal territory, making it a suitable modality for vessel analysis. However, imaging the large FOV may result in low-contrast vascular details and background artifacts, which pose challenges to the accurate segmentation of retinal microvasculature. To address these issues, a privileged modality guided multi-scale location-aware fusion network is proposed for vessel segmentation in UWF images. We first perform style transfer on the UWF images to generate the corresponding FFA image with higher contrast. Afterwards, we employ cross-modal coherence loss to segment the vessels guided by the FFA image. Additionally, a multi-scale location-aware fusion module is proposed and embedded into the segmentation network for reducing the boundary artifacts. Finally, experiments are performed on a dedicated UWF dataset, and the evaluation results demonstrate that our method achieves competitive vessel segmentation performance with a Dice score of around \(78.13\%\). This indicates that our method is potentially valuable for subsequent vessel analysis to support disease diangosis.
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References
Kashani, A.H., et al.: Optical coherence tomography angiography: a comprehensive review of current methods and clinical applications. Prog. Retin. Eye Res. 60, 66–100 (2017)
Dashtbozorg, B., Zhang, J., Huang, F., ter Haar Romeny, B.M.: Retinal microaneurysms detection using local convergence index features. IEEE Trans. Image Process. 27(7), 3300–3315 (2018)
Zhang, J., Dashtbozorg, B., Bekkers, E.J., Pluim, J.P.W., Duits, R., Romeny, B.M.T.H.: Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans. Med. Imaging (2016)
Ju, L., Wang, X., Zhao, X., Bonnington, P., Drummond, T., Ge, Z.: Leveraging regular fundus images for training UWF fundus diagnosis models via adversarial learning and pseudo-labeling. IEEE Trans. Med. Imaging 40(10), 2911–2925 (2021)
Fraz, M.M., Basit, A., Barman, S.: Application of morphological bit planes in retinal blood vessel extraction. J. Dig. Imaging 26, 274–286 (2013)
Orlando, J.I., Prokofyeva, E., Blaschko, M.B.: A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images. IEEE Trans. Biomed. Eng. 64, 16–27 (2017)
Wu, Y., Xia, Y., Song, Y., Zhang, Y., Cai, W.: Multiscale network followed network model for retinal vessel segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11071, pp. 119–126. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00934-2_14
Pellegrini, E., et al.: Blood vessel segmentation and width estimation in ultra-wide field scanning laser ophthalmoscopy. Biomed. Opt. Express 5(12), 4329–4337 (2014)
Ding, L., Kuriyan, A.E., Ramchandran, R.S., Wykoff, C.C., Sharma, G.: Weakly-supervised vessel detection in ultra-widefield fundus photography via iterative multi-modal registration and learning. IEEE Trans. Med. Imaging 40(10), 2748–2758 (2020)
Peng, L., Lin, L., Cheng, P., Huang, Z., Tang, X.: Unsupervised domain adaptation for cross-modality retinal vessel segmentation via disentangling representation style transfer and collaborative consistency learning. In: IEEE 19th International Symposium on Biomedical Imaging (ISBI). IEEE 2022, pp. 1–5 (2022)
Chen, W., Jiang, Z., Wang, Z., Cui, K., Qian, X.: Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8924–8933 (2019)
Li, Q., Yang, W., Liu, W., Yu, Y., He, S.: From contexts to locality: ultra-high resolution image segmentation via locality-aware contextual correlation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7252–7261 (2021)
Zhang, Z., Liu, Q., Wang, Y.: Road extraction by deep residual u-net. IEEE Geosci. Remote Sens. Lett. 15(5), 749–753 (2018)
Wu, Y., Xu, M., Ge, Z., Cai, J., Zhang, L.: Semi-supervised left atrium segmentation with mutual consistency training. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 297–306. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87196-3_28
Verma, V., et al.: Interpolation consistency training for semi-supervised learning. Neural Netw. 145, 90–106 (2022)
Zhao, X., Fang, C., Fan, D.J., Lin, X., Gao, F., Li, G.: Cross-level contrastive learning and consistency constraint for semi-supervised medical image segmentation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE (2022)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448
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
Gu, Z., et al.: Ce-net: context encoder network for 2d medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)
Mou, L., Zhao, Y., Chen, L., Cheng, J., Gu, Z., Hao, H., Qi, H., Zheng, Y., Frangi, A., Liu, J.: CS-Net: Channel and Spatial Attention Network for Curvilinear Structure Segmentation. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 721–730. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32239-7_80
Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds.) ECCV 2022, Part III. LNCS, vol. 13803, pp. 205–218. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-25066-8_9
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Li, X. et al. (2023). Privileged Modality Guided Network for Retinal Vessel Segmentation in Ultra-Wide-Field Images. In: Antony, B., Chen, H., Fang, H., Fu, H., Lee, C.S., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2023. Lecture Notes in Computer Science, vol 14096. Springer, Cham. https://doi.org/10.1007/978-3-031-44013-7_9
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