Multi-index Optic Disc Quantification via MultiTask Ensemble Learning

Abstract

Accurate quantification of optic disc (OD) is clinically significant for the assessment and diagnosis of ophthalmic disease. Multi-index OD quantification, i.e., to simultaneously quantify a set of clinical indices including 2 vertical diameters (cup and disc), 2 whole areas (disc and rim), and 16 regional areas, is an untouched challenge due to its complexity of the multi-dimensional nonlinear mapping and various visual appearance across patients. In this paper, we propose a novel multitask ensemble learning framework (DMTFs) to automatically achieve accurate multi-types multi-index OD quantification. DMTFs creates an ensemble of multiple OD quantification tasks (OD segmentation and indices estimation) that are individually accurate and mutually complementary, and then learns the ensemble under a multi-task learning framework which is formed as a tree structure with a root network for shared feature representation, two branches for task-specific prediction, and a multitask ensemble module for aggregation of multi-index OD quantification. DMTFs models the consistency correlation between OD segmentation and indices estimation tasks to conform to the accurate multi-index OD quantification. Experiments on the ORIGA datasets show that the proposed method achieves impressive performance with the average mean absolute error on 20 indices of $0.99\,\pm \,0.20$, $0.73\,\pm \,0.14$ and $1.23\,\pm \,0.24$ for diameters, whole areas and regional area, respectively. Besides, the obtained quantitative indices achieve competitive performance (AUC = 0.8623) on glaucoma diagnosis. As the first multi-index OD quantification, the proposed DMTFs demonstrates great potential in clinical application.