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As a highly vascular eye part, the choroid is crucial in various eye disease diagnoses. However, limited research has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data, particularly for the choroidal vessels. Meanwhile, the existing direct choroidal vessel segmentation methods for the intelligent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data, while the synergistic segmentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks. Common cascaded structures grapple with error propagation during training. To address these challenges, we propose a cascade learning segmentation method for the inner vessel structures of the choroid in this paper. Specifically, we propose Transformer-Assisted Cascade Learning Network (TACLNet) for choroidal vessel segmentation, which comprises a two-stage training strategy: pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation. We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC, capturing differential and detailed information simultaneously. Additionally, we implement an auxiliary Transformer branch named ATB to integrate global features into the segmentation process. Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation. Besides, we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography (OCT) scans on a publicly available dataset. All these fully prove that our TACLNet contributes to the advancement of choroidal vessel segmentation and is of great significance for ophthalmic research and clinical application.
Nickla D L, Wallman J. The multifunctional choroid. Progress in Retinal and Eye Research, Mar. 2010, 29(2): 144–168. DOI: 10.1016/j.preteyeres.2009.12.002.
Singh S R, Vupparaboina K K, Goud A, Dansingani K K, Chhablani J. Choroidal imaging biomarkers. Survey of Ophthalmology, 2019, 64(3): 312–333. DOI: 10.1016/j.survophthal.2018.11.002.
Arrigo A, Bordato A, Romano F, Aragona E, Grazioli A, Bandello F, Parodi M B. Choroidal patterns in retinitis pigmentosa: Correlation with visual acuity and disease progression. Translational Vision Science & Technology, 2020, 9(4): 17. DOI: 10.1167/tvst.9.4.17.
Spaide R F, Fujimoto J G, Waheed N K, Sadda S R, Staurenghi G. Optical coherence tomography angiography. Progress in Retinal and Eye Research, May 2018, 64: 1–55. DOI: 10.1016/j.preteyeres.2017.11.003.
Fercher A F, Hitzenberger C K, Kamp G, El-Zaiat S Y. Measurement of intraocular distances by backscattering spectral interferometry. Optics Communications, May 1995, 117(1/2): 43–48. DOI: 10.1016/0030-4018(95)00119-S.
Lavinsky F, Lavinsky D. Novel perspectives on swept-source optical coherence tomography. International Journal of Retina and Vitreous, 2016, 2(1): Article No. 25. DOI: 10.1186/s40942-016-0050-y.
Sezer T, Altınışık M, Koytak İ A, Özdemir M H. The choroid and optical coherence tomography. Turkish Journal of Ophthalmology, 2016, 46(1): 30–37. DOI: 10.4274/tjo.10693.
Liu X X, Bi L, Xu Y P, Feng D G, Kim J, Xu X. Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images. Biomedical Optics Express, 2019, 10(4): 1601–1612. DOI: 10.1364/boe.10.001601.
Qiu B, Huang Z Y, Liu X et al. Noise reduction in optical coherence tomography images using a deep neural network with perceptually-sensitive loss function. Biomedical Optics Express, 2020, 11(2): 817–830. DOI: 10.1364/boe.379551.
Zhu L, Li J M, Zhu R L et al. Synergistically segmenting choroidal layer and vessel using deep learning for choroid structure analysis. Physics in Medicine & Biology, 2022, 67(8): 085001. DOI: 10.1088/ 1361-6560/ac5ed7.
Khaing T T, Okamoto T, Ye C et al. ChoroidNET: A dense dilated U-Net model for choroid layer and vessel segmentation in optical coherence tomography images. IEEE Access, 2021, 9: 150951–150965. DOI: 10.1109/ACCESS.2021.3124993.
Wang X H, Li R, Chen J Y et al. Choroidal vascularity index (CVI)-Net-based automatic assessment of diabetic retinopathy severity using CVI in optical coherence tomography images. Journal of Biophotonics, Jan. 2023, 16(6): e202200370. DOI: 10.1002/jbio.2022 00370.
Zhang L, Lee K, Niemeijer M, Mullins R F, Sonka M, Abràmoff M D. Automated segmentation of the choroid from clinical SD-OCT. Investigative Ophthalmology & Visual Science, 2012, 53(12): 7510–7519. DOI: 10.1167/iovs.12-10311.
Chen Q, Fan W, Niu S J, Shi J J, Shen H L, Yuan S T. Automated choroid segmentation based on gradual intensity distance in HD-OCT images. Optics Express, 2015, 23(7): 8974–8994. DOI: 10.1364/oe.23.008974.
Hussain M A, Bhuiyan A, Ishikawa H, Theodore Smith R, Schuman J S, Kotagiri R. An automated method for choroidal thickness measurement from enhanced depth imaging optical coherence tomography images. Computerized Medical Imaging and Graphics, Jan. 2018, 63: 41–51. DOI: 10.1016/j.compmedimag.2018.01.001.
Zheng G, Jiang Y F, Shi C et al. Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images. Journal of Innovative Optical Health Sciences, 2021, 14(01): 2140002. DOI: 10.1142/S179354582140 0022.
Zhang Z X, Liu Q J, Wang Y H. Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 749–753. DOI: 10.1109/LGRS.2018. 2802944.
Huang K, Su N, Ma X, Li M C, Yang J D, Yuan S T, Liu Y, Chen Q. Choroidal vessel segmentation in SD-OCT with 3D shape-aware adversarial networks. Biomedical Signal Processing and Control, 2023, 84: 104982. DOI: 10.1016/j.bspc.2023.104982.
Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481–2495. DOI: 10.1109/TPAMI.2016.2644615.
Zhang H H, Yang J L, Zhou K et al. Automatic segmentation and visualization of choroid in OCT with knowledge infused deep learning. IEEE Journal of Biomedical and Health Informatics, 2020, 24(12): 3408–3420. DOI: 10.1109/JBHI.2020.3023144.
Kullback S, Leibler R A. On information and sufficiency. The Annals of Mathematical Statistics, 1951, 22(1): 79–86. DOI: 10.1214/aoms/1177729694.
Bogunovic H, Venhuizen F, Klimscha S et al. RETOUCH: The retinal OCT fluid detection and segmentation benchmark and challenge. IEEE Trans. Medical Imaging, 2019, 38(8): 1858–1874. DOI: 10.1109/TMI.2019.2901398.