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 this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.

Vascular tracking of coronary angiographic image sequences is one of the most clinically important tasks in the diagnostic assessment and interventional guidance of cardiac disease. It is difficult to automate this application because the vascular structure is complex; moreover, unsatisfactory angiography image quality may exacerbate the difficulty of vasculature extraction. This study converts vascular tracking into branch matching and proposes a novel and automatic greedy soft match algorithm. Our method is based on a graph framework. A graph model building module is proposed to represent the vascular structure. Then, a greedy branch searching method is adopted to acquire all possible paths in the graph that may match the reference vessel. Finally, a soft batch matching method that combines branch descriptor and dynamic time warping is presented to select the best matching branch. The solution to the problem takes advantage of both spatial and temporal continuity between successive frames. Experimental results demonstrate that the proposed algorithm is effective and robust for vascular tracking. The F1 score of a single branch dataset, which contains 12 angiographic image sequences with 77 angiograms of contrast agent-filled vessels, is 0.89 ± 0.06 and of a vessel tree dataset which contains 9 sequences with 58 angiograms is 0.88 ± 0.05. Extensive experimental results well demonstrate the superior performance of the algorithm. In addition, it provides a universal solution to address the problem of filamentary structure tracking.

Vascular centerline extraction from angiography images plays an important role in computer-aided diagnosis of vascular disease. To solve the common problems related to noise and inconsistent vasculatures from uneven perfusion, this paper proposes an automatic framework for accurate vascular centerline extraction from angiograms that uses multi-probability fusion-based topology optimization. In this framework, vascular region is first segmented using a learning-based method. Then, initial centerlines are obtained by applying iterative filtering operation and multi-direction indexed non-maximum suppression. Topology optimization is achieved by gap filling. A connection probability map is constructed utilizing the information of initial centerlines, texture, and orientation of vasculatures. Shortest path tracking is employed to search for optimal connections around gaps in the initial centerlines. The proposed framework is evaluated using simulative and clinical coronary angiographies. The experimental results demonstrate that the proposed method can extract centerlines with F1 score of 97.28% ± 1.2% for vasculatures in 12 clinical angiographic images. It is evident that the proposed method can extract complete and accurate vascular centerlines from angiograms and can be used to repair gaps in other filamentary structures, such as roads and retinal blood vessels. This endows our method a great potential in the analysis of filamentary structures.