Dual consistency enabled weakly and semi-supervised optic disc and cup segmentation with dual adaptive graph convolutional networks

Yanda Meng, Hongrun Zhang, Yitian Zhao, Dongxu Gao, Barbra Hamill, Godhuli Patri, Tunde Peto, Savita Madhusudhan, Yalin Zheng

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Abstract

Glaucoma is a progressive eye disease that results in permanent vision loss, and the vertical cup to disc ratio ( vCDR ) in colour fundus images is essential in glaucoma screening and assessment. Previous fully supervised convolution neural networks segment the optic disc ( OD ) and optic cup ( OC ) from color fundus images and then calculate the vCDR offline. However, they rely on a large set of labeled masks for training, which is expensive and time-consuming to acquire. To address this, we propose a weakly and semi-supervised graph-based network that investigates geometric associations and domain knowledge between segmentation probability maps ( PM ), modified signed distance function representations ( mSDF ), and boundary region of interest characteristics ( B-ROI ) in three aspects. Firstly, we propose a novel Dual Adaptive Graph Convolutional Network ( DAGCN ) to reason the long-range features of the PM and the mSDF w.r.t . the regional uniformity. Secondly, we propose a dual consistency regularization-based semi-supervised learning paradigm. The regional consistency between the PM and the mSDF , and the marginal consistency between the derived B-ROI from each of them boost the proposed model’s performance due to the inherent geometric associations. Thirdly, we exploit the task-specific domain knowledge via the oval shapes of OD & OC , where a differentiable vCDR estimating layer is proposed. Furthermore, without additional annotations, the supervision on vCDR serves as weakly-supervisions for segmentation tasks. Experiments on six large-scale datasets demonstrate our model’s superior performance on OD & OC segmentation and vCDR estimation. The implementation code has been made available.

Original languageEnglish
JournalIEEE Transactions on Medical Imaging
Early online date31 Aug 2022
DOIs
Publication statusEarly online - 31 Aug 2022

Keywords

  • Annotations
  • Biomedical optical imaging
  • Feature extraction
  • Graph Convolutional Network
  • Image segmentation
  • Optic Disc and Cup Segmentation
  • Optical imaging
  • Semantics
  • Task analysis
  • Weakly and Semi-supervised Learning

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