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Cost-effective broad learning-based ultrasound biomicroscopy with 3D reconstruction for ocular anterior segmentation

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Abstract

Anterior Chamber Angle (ACA) assessment plays an important role for the diagnosis of glaucoma. Most of the existing techniques relied on Anterior Segment Optical Coherence Tomography (AS-OCT) or Swept Source Optical Coherence Tomography (SS-OCT). We proposed a system for 360° overview of iridocorneal angle of anterior chamber (ICAAC) via Ultrasound Biomicroscopy (UBM). UBM approach acquires the visualization of anterior segment components as well as diseased structures (glaucoma). Our system consists of a new pairing scheme of feature descriptors, i.e. (FREAK, BRISK), (SURF, BRISK) and Broad Learning System (BLS) for 3D reconstruction and segmentation of ICAAC. The 360° overview of 2D ICAAC gives global conception for ACA assessment. 3D images provide a detailed assessment with the amount of opposition’s and synechiae in angle-closure suspects, angle-closure and angle-closure glaucoma in bright light conditions. Extensive evaluations are performed on dataset consists of 650 ICAAC images in five directions of 65 subjects with 10 samples per subject (5 left eye and 5 right eye) from Shanghai Sixth People’s Hospital. Experiments showed that our approach achieves an overall accuracy of 98.72% with training and testing time 29.26(s), 1.232(s) respectively.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant 61872241 and Grant 61572316, in part by the Science and Technology Commission of Shanghai Municipality under Grant 18410750700, Grant 17411952600, and Grant 16DZ0501100.

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    Authors and Affiliations

    1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

      Saba Ghazanfar Ali & Bin Sheng

    2. Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, 200233, China

      Yan Chen, Huating Li & Qiang Wu

    3. Department of Computer Science, Sheffield University, Sheffield, UK

      Po Yang

    4. Department of Software, Sejong University, Seoul, 143-747, South Korea

      Khan Muhammad

    5. Department of Mechanical Engineering, Zhejiang Universitpy (ZJU), Hangzhou, China

      Geng Yang

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    1. Saba Ghazanfar Ali
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    Correspondence to Bin Sheng, Qiang Wu or Khan Muhammad.

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    Saba Ghazanfar Ali and Yan Chen contributed equally to this work.

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    Ali, S.G., Chen, Y., Sheng, B. et al. Cost-effective broad learning-based ultrasound biomicroscopy with 3D reconstruction for ocular anterior segmentation. Multimed Tools Appl 80, 35105–35122 (2021). https://doi.org/10.1007/s11042-020-09303-9

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