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Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12265))

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Abstract

Precise characterization and analysis of iris shape from Anterior Segment OCT (AS-OCT) are of great importance in facilitating diagnosis of angle-closure-related diseases. Existing methods focus solely on analyzing structural properties identified from an individual 2D slice, while accurate characterization of morphological changes of iris shape in 3D AS-OCT may be able to reveal in addition the risk of disease progression. In this paper, we propose a novel framework for reconstruction and quantification of 3D iris surface from AS-OCT volume. We consider it to be the first work to detect angle-closure glaucoma by means of 3D representation. An iris segmentation network with wavelet refinement block (WRB) is first proposed to generate the initial shape of the iris from single AS-OCT slice. The 3D iris surface is then reconstructed using a guided optimization method with Poisson-disk sampling. Finally, a set of surface-based features are extracted, which are used in detecting of angle-closure glaucoma. Experimental results demonstrate that our method is highly effective in iris segmentation and surface reconstruction. Moreover, we show that 3D-based representation achieves better performance in angle-closure glaucoma detection than does 2D-based feature.

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Acknowledgment

This work was supported by Zhejiang Provincial Natural Science Foundation of China (LZ19F010001,LQ19H180001), Ningbo “2025 S&T Megaprojects” (2019B10033, 2019B10061).

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Correspondence to Huazhu Fu or Yitian Zhao .

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Hao, J. et al. (2020). Reconstruction and Quantification of 3D Iris Surface for Angle-Closure Glaucoma Detection in Anterior Segment OCT. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_68

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  • DOI: https://doi.org/10.1007/978-3-030-59722-1_68

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59721-4

  • Online ISBN: 978-3-030-59722-1

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