An Efficient and Comprehensive Labeling Tool for Large-Scale Annotation of Fundus Images

Jaemin Son, Sangkeun Kim, Sang Jun Park, Kyu-Hwan Jung

Abstract: Computerized labeling tools are often used to systematically record the assessment for fundus images. Carefully designed labeling tools not only save time and enable comprehensive and thorough assessment at clinics, but also economize large-scale data collection processes for the development of automatic algorithms. To realize efficient and thorough fundus assessment, we developed a new labeling tool with novel schemes - stepwise labeling and regional encoding. We have used our tool in a large-scale data annotation project in which 318,376 annotations for 109,885 fundus images were gathered with a total duration of 421 h. We believe that the fundamental concepts in our tool would inspire other data collection processes and annotation procedure in different domains.