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.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (61702558, 61602527), the Hunan Natural Science Foundation (2017JJ3411), the Key Research and Development Projects in Hunan (2017WK2074), the National Key Research and Development Program of China (2017YFC0840104) and the China Scholarship Council (201806375006).
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Zhao, R., Chen, Z., Liu, X., Zou, B., Li, S. (2019). Multi-index Optic Disc Quantification via MultiTask Ensemble Learning. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_3
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DOI: https://doi.org/10.1007/978-3-030-32239-7_3
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