Glaucoma Oct Analysis and Layer Segmentation
Creators: name: Huazhu Fu
name: Yanwu Xu
name: Orlando, José Ignacio
name: Bogunovi, Hrvoje
name: Li, Fei
name: Xiulan Zhang
Publisher: name: Zenodo
Year: 2022
Subjects: OCT analysis
Glaucoma diagnosis
OCT layer segmentation
MICCAI
Challenge

description: Optical Coherence Tomography (OCT) is a powerful tool for the diagnosis of ocular diseases, since the image acquisition consists in a contactless, non-invasive method which gives a set of images of the main retinal structures in real time[1,2]. Compared with color fundus images, which can only provide retinal surface information, OCT images can provide a cross-sectional information of the retina, so it can be more accurate analysis of the retinal structure. Segmentation and quantification of layer thickness is useful in the diagnosis of many retinal and optic nerve disorders, for example, glaucoma[3], macular degeneration[4,5] or diabetic retinopathy[6]. In the diagnosis of glaucoma, it is easier to detect early cases using OCT than using fundus color images. In this challenge, we design two tasks around OCT images: 1). a segmentation task to determine the retinal nerve fiber layer, ganglion cellinner plexiform layer, and choroidal layer, which are helpful for diagnosis and differentiation of glaucoma; 2). an automatic diagnosis task of glaucoma. It is believed that many participants will use the deep learning method due to its high performance in OCT image analysis. To meet the requirement of widely distributed dataset in the training process of the deep learning methods, we provide 300 circumpapillary OCT images collected by two different devices. Our dataset can not only supports research on the application of deep learning methods in OCT image analysis, but also on the adaptive multi-domain algorithms.[7,8]. References [1] O. Puzyeyeva et al., “High-resolution optical coherence tomography retinal imaging: a case series illustrating<br> potential and limitations,” J. Ophthalmol., vol. 2011, 2011.<br> [2] Z. Yaqoob, J. Wu, and C. Yang, “Spectral domain optical coherence tomography: a better OCT imaging<br> strategy,” Biotechniques, vol. 39, no. 6, pp. S6–S13, 2005.<br> [3] C. Bowd, R. N. Weinreb, J. M. Williams, and L. M. Zangwill, “The retinal nerve fiber layer thickness in ocular<br> hypertensive, normal, and glaucomatous eyes with optical coherence tomography,” Arch. Ophthalmol., vol. 118,<br> no. 1, pp. 22–26, 2000.<br> [4] P. A. Keane, P. J. Patel, S. Liakopoulos, F. M. Heussen, S. R. Sadda, and A. Tufail, “Evaluation of age-related<br> macular degeneration with optical coherence tomography,” Surv. Ophthalmol., vol. 57, no. 5, pp. 389–414, 2012.<br> [5] J. de Moura, J. Novo, J. Rouco, M. G. Penedo, and M. Ortega, “Automatic identification of intraretinal cystoid<br> regions in optical coherence tomography,” in Conference on artificial intelligence in medicine in Europe, 2017, pp.<br> 305–315.<br> [6] G. Samagaio, A. Estévez, J. de Moura, J. Novo, M. I. Fernández, and M. Ortega, “Automatic macular edema<br> identification and characterization using OCT images,” Comput. Methods Programs Biomed., vol. 163, pp. 47–63,<br> 2018.<br> [7] P. Liu, B. Kong, Z. Li, S. Zhang, and R. Fang, “CFEA: collaborative feature ensembling adaptation for domain<br> adaptation in unsupervised optic disc and cup segmentation,” in International conference on medical image<br> computing and computer-assisted intervention, 2019, pp. 521–529.<br> [8] S. Wang, L. Yu, K. Li, X. Yang, C.-W. Fu, and P.-A. Heng, “Boundary and entropy-driven adversarial learning for<br> fundus image segmentation,” in International conference on medical image computing and computer-assisted<br> intervention, 2019, pp. 102–110.