Challenge 1: 3-D Validation of Tractography with Experimental MRI (3D VoTEM)
Contact person: Bennett A Landman (firstname.lastname@example.org)
Challenge 2: Diabetic Retinopathy – Segmentation and Grading Challenge
Contact person: Prasanna Porwal (email@example.com)
Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting working age population in the world. Recent research has given a better understanding of requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease. The importance of diabetic retinopathy screening programs and difficulty in achieving reliable early diagnosis of diabetic retinopathy at a reasonable cost needs attention to develop computer-aided diagnosis tool. Computer-aided disease diagnosis in retinal image analysis could ease mass screening of population with diabetes mellitus and help clinicians in utilizing their time more efficiently. The recent technological advances in computing power, communication systems, and machine learning techniques provide opportunities to the biomedical engineers and computer scientists to meet the requirements of clinical practice. Diverse and representative retinal image sets are essential for developing and testing digital screening programs and the automated algorithms at their core. To the best of our knowledge, the database for this challenge, IDRiD (Indian Diabetic Retinopathy Image Dataset), is the first database representative of an Indian population. Moreover, it is the only dataset constituting typical diabetic retinopathy lesions and normal retinal structures annotated at a pixel level. This dataset provides information on the disease severity of diabetic retinopathy, and diabetic macular edema for each image. This makes it perfect for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy. An aim of this challenge is to evaluate algorithms for automated detection and grading of diabetic retinopathy and diabetic macular edema using retinal fundus images. The challenge is subdivided into three tasks as follows:
- Lesion Segmentation: Segmentation of retinal lesions associated with diabetic retinopathy as microaneurysms, hemorrhages, hard exudates and soft exudates.
- Disease Grading: Classification of fundus images according to the severity level of diabetic retinopathy and diabetic macular edema.
- Optic Disc and Fovea Detection: Automatic localization of optic disc and fovea center coordinates and also segmentation of optic disc.
Challenge 3: Lung Nodule Malignancy Prediction based on Sequential CT Scans