Accurate Cup-to-Disc Ratio Measurement with Tight Bounding Box Supervision in Fundus Photography

• URL: http://arxiv.org/abs/2110.00943v1
• Date: Sun, 3 Oct 2021 07:23:40 GMT
• Title: Accurate Cup-to-Disc Ratio Measurement with Tight Bounding Box Supervision in Fundus Photography
• Authors: Juan Wang and Bin Xia
• Abstract summary: The cup-to-disc ratio (CDR) is one of the most significant indicator for glaucoma diagnosis. This study investigates the feasibility of accurate CDR measurement in fundus images using only tight bounding box supervision.
• Score: 5.517632401040172
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The cup-to-disc ratio (CDR) is one of the most significant indicator for glaucoma diagnosis. Different from the use of costly fully supervised learning formulation with pixel-wise annotations in the literature, this study investigates the feasibility of accurate CDR measurement in fundus images using only tight bounding box supervision. For this purpose, we develop a two-task network for accurate CDR measurement, one for weakly supervised image segmentation, and the other for bounding-box regression. The weakly supervised image segmentation task is implemented based on generalized multiple instance learning formulation and smooth maximum approximation, and the bounding-box regression task outputs class-specific bounding box prediction in a single scale at the original image resolution. To get accurate bounding box prediction, a class-specific bounding-box normalizer and an expected intersection-over-union are proposed. In the experiments, the proposed approach was evaluated by a testing set with 1200 images using CDR error and F1 score for CDR measurement and dice coefficient for image segmentation. A grader study was conducted to compare the performance of the proposed approach with those of individual graders. The results demonstrate that the proposed approach outperforms the state-of-the-art performance obtained from the fully supervised image segmentation (FSIS) approach using pixel-wise annotation for CDR measurement, which is also better than those of individual graders. It also gets performance close to the state-of-the-art obtained from FSIS for optic cup and disc segmentation, similar to those of individual graders. The codes are available at \url{https://github.com/wangjuan313/CDRNet}.

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