REFUGE2 Challenge: Treasure for Multi-Domain Learning in Glaucoma Assessment

Glaucoma is the second leading cause of blindness and is the leading cause of irreversible blindness disease in the world. Early screening for glaucoma in the population is significant. Color fundus photography is the most cost effective imaging modality to screen for ocular diseases. Deep learning network is often used in color fundus image analysis due to its powful feature extraction capability. However, the model training of deep learning method needs a large amount of data, and the distribution of data should be abundant for the robustness of model performance. To promote the research of deep learning in color fundus photography and help researchers further explore the clinical application signification of AI technology, we held a REFUGE2 challenge. This challenge released 2,000 color fundus images of four models, including Zeiss, Canon, Kowa and Topcon, which can validate the stabilization and generalization of algorithms on multi-domain. Moreover, three sub-tasks were designed in the challenge, including glaucoma classification, cup/optic disc segmentation, and macular fovea localization. These sub-tasks technically cover the three main problems of computer vision and clinicly cover the main researchs of glaucoma diagnosis. Over 1,300 international competitors joined the REFUGE2 challenge, 134 teams submitted more than 3,000 valid preliminary results, and 22 teams reached the final. This article summarizes the methods of some of the finalists and analyzes their results. In particular, we observed that the teams using domain adaptation strategies had high and robust performance on the dataset with multi-domain. This indicates that UDA and other multi-domain related researches will be the trend of deep learning field in the future, and our REFUGE2 datasets will play an important role in these researches.

ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance as the vision loss caused by AMD is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. \textcolor{red}{Recently, some algorithms based on deep learning had been developed for fundus image analysis and automatic AMD detection. However, a comprehensive annotated dataset and a standard evaluation benchmark are still missing.} To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM) for the first time, held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main topics in detecting AMD from fundus images, including classification of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. The ADAM challenge has released a comprehensive dataset of 1200 fundus images with the category labels of AMD, the pixel-wise segmentation masks of the full optic disc and lesions (drusen, exudate, hemorrhage, scar, and other), as well as the location coordinates of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models. During the ADAM challenge, 610 results were submitted for online evaluation, and finally, 11 teams participated in the onsite challenge. This paper introduces the challenge, dataset, and evaluation methods, as well as summarizes the methods and analyzes the results of the participating teams of each task. In particular, we observed that ensembling strategy and clinical prior knowledge can better improve the performances of the deep learning models.

Robust Retinal Vessel Segmentation from a Data Augmentation Perspective

Retinal vessel segmentation is a fundamental step in screening, diagnosis, and treatment of various cardiovascular and ophthalmic diseases. Robustness is one of the most critical requirements for practical utilization, since the test images may be captured using different fundus cameras, or be affected by various pathological changes. We investigate this problem from a data augmentation perspective, with the merits of no additional training data or inference time. In this paper, we propose two new data augmentation modules, namely, channel-wise random Gamma correction and channel-wise random vessel augmentation. Given a training color fundus image, the former applies random gamma correction on each color channel of the entire image, while the latter intentionally enhances or decreases only the fine-grained blood vessel regions using morphological transformations. With the additional training samples generated by applying these two modules sequentially, a model could learn more invariant and discriminating features against both global and local disturbances. Experimental results on both real-world and synthetic datasets demonstrate that our method can improve the performance and robustness of a classic convolutional neural network architecture. Source codes are available https://github.com/PaddlePaddle/Research/tree/master/CV/robust_vessel_segmentation

AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually leads to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a uniform way, which limits the progress in the development of automated techniques for angle closure detection and assessment. To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019. The AGE challenge consisted of two tasks: scleral spur localization and angle closure classification. For this challenge, we released a large data of 4800 annotated AS-OCT images from 199 patients, and also proposed an evaluation framework to benchmark and compare different models. During the AGE challenge, over 200 teams registered online, and more than 1100 results were submitted for online evaluation. Finally, eight teams participated in the onsite challenge. In this paper, we summarize these eight onsite challenge methods and analyze their corresponding results in the two tasks. We further discuss limitations and future directions. In the AGE challenge, the top-performing approach had an average Euclidean Distance of 10 pixel in scleral spur localization, while in the task of angle closure classification, all the algorithms achieved the satisfactory performances, especially, 100% accuracy rate for top-two performances.