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Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis

Author

Listed:
  • Muhammad Arsalan

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Seoul 04620, Korea)

  • Adnan Haider

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Seoul 04620, Korea)

  • Ja Hyung Koo

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Seoul 04620, Korea)

  • Kang Ryoung Park

    (Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Seoul 04620, Korea)

Abstract

Retinal blood vessels possess a complex structure in the retina and are considered an important biomarker for several retinal diseases. Ophthalmic diseases result in specific changes in the retinal vasculature; for example, diabetic retinopathy causes the retinal vessels to swell, and depending upon disease severity, fluid or blood can leak. Similarly, hypertensive retinopathy causes a change in the retinal vasculature due to the thinning of these vessels. Central retinal vein occlusion (CRVO) is a phenomenon in which the main vein causes drainage of the blood from the retina and this main vein can close completely or partially with symptoms of blurred vision and similar eye problems. Considering the importance of the retinal vasculature as an ophthalmic disease biomarker, ophthalmologists manually analyze retinal vascular changes. Manual analysis is a tedious task that requires constant observation to detect changes. The deep learning-based methods can ease the problem by learning from the annotations provided by an expert ophthalmologist. However, current deep learning-based methods are relatively inaccurate, computationally expensive, complex, and require image preprocessing for final detection. Moreover, existing methods are unable to provide a better true positive rate (sensitivity), which shows that the model can predict most of the vessel pixels. Therefore, this study presents the so-called vessel segmentation ultra-lite network (VSUL-Net) to accurately extract the retinal vasculature from the background. The proposed VSUL-Net comprises only 0.37 million trainable parameters and uses an original image as input without preprocessing. The VSUL-Net uses a retention block that specifically maintains the larger feature map size and low-level spatial information transfer. This retention block results in better sensitivity of the proposed VSUL-Net without using expensive preprocessing schemes. The proposed method was tested on three publicly available datasets: digital retinal images for vessel extraction (DRIVE), structured analysis of retina (STARE), and children’s heart health study in England database (CHASE-DB1) for retinal vasculature segmentation. The experimental results demonstrated that VSUL-Net provides robust segmentation of retinal vasculature with sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under the curve (AUC) values of 83.80%, 98.21%, 96.95%, and 98.54%, respectively, for DRIVE, 81.73%, 98.35%, 97.17%, and 98.69%, respectively, for CHASE-DB1, and 86.64%, 98.13%, 97.27%, and 99.01%, respectively, for STARE datasets. The proposed method provides an accurate segmentation mask for deep ophthalmic analysis.

Suggested Citation

  • Muhammad Arsalan & Adnan Haider & Ja Hyung Koo & Kang Ryoung Park, 2022. "Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis," Mathematics, MDPI, vol. 10(9), pages 1-25, May.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1536-:d:807973
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    References listed on IDEAS

    as
    1. Ramakrishnan Sundaram & Ravichandran KS & Premaladha Jayaraman & Venkatraman B, 2019. "Extraction of Blood Vessels in Fundus Images of Retina through Hybrid Segmentation Approach," Mathematics, MDPI, vol. 7(2), pages 1-17, February.
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    Cited by:

    1. Carmen Lacave & Ana Isabel Molina, 2023. "Advances in Artificial Intelligence and Statistical Techniques with Applications to Health and Education," Mathematics, MDPI, vol. 11(6), pages 1-4, March.
    2. Ali Raza & Sharjeel Adnan & Muhammad Ishaq & Hyung Seok Kim & Rizwan Ali Naqvi & Seung-Won Lee, 2023. "Assisting Glaucoma Screening Process Using Feature Excitation and Information Aggregation Techniques in Retinal Fundus Images," Mathematics, MDPI, vol. 11(2), pages 1-20, January.
    3. Muhammad Ishaq & Salman Raza & Hunza Rehar & Shan e Zain ul Abadeen & Dildar Hussain & Rizwan Ali Naqvi & Seung-Won Lee, 2023. "Assisting the Human Embryo Viability Assessment by Deep Learning for In Vitro Fertilization," Mathematics, MDPI, vol. 11(9), pages 1-17, April.

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