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Semi-supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment

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Mitosis Domain Generalization and Diabetic Retinopathy Analysis (MIDOG 2022, DRAC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13597))

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Abstract

People with diabetes are more likely to develop diabetic retinopathy (DR) than healthy people. However, DR is the leading cause of blindness. At present, the diagnosis of diabetic retinopathy mainly relies on the experienced clinician to recognize the fine features in color fundus images. This is a time-consuming task. Therefore, in this paper, to promote the development of UW-OCTA DR automatic detection, we propose a novel semi-supervised semantic segmentation method for UW-OCTA DR image grade assessment. This method, first, uses the MAE algorithm to perform semi-supervised pre-training on the UW-OCTA DR grade assessment dataset to mine the supervised information in the UW-OCTA images, thereby alleviating the need for labeled data. Secondly, to more fully mine the lesion features of each region in the UW-OCTA image, this paper constructs a cross-algorithm ensemble DR tissue segmentation algorithm by deploying three algorithms with different visual feature processing strategies. The algorithm contains three sub-algorithms, namely pre-trained MAE, ConvNeXt, and SegFormer. Based on the initials of these three sub-algorithms, the algorithm can be named MCS-DRNet. Finally, we use the MCS-DRNet algorithm as an inspector to check and revise the results of the preliminary evaluation of the DR grade evaluation algorithm. The experimental results show that the mean dice similarity coefficient of MCS-DRNet v1 and v2 are 0.5161 and 0.5544, respectively. The quadratic weighted kappa of the DR grading evaluation is 0.7559. Our code is available at https://github.com/SupCodeTech/DRAC2022.

Supported by Ministry of Higher Education, Malaysia.

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, 43400, Malaysia

    Zhuoyi Tan, Hizmawati Madzin & Zeyu Ding

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  1. Zhuoyi Tan
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  2. Hizmawati Madzin
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  3. Zeyu Ding
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Corresponding author

Correspondence to Hizmawati Madzin .

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Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Technische Hochschule Ingolstadt, Ingolstadt, Germany

    Marc Aubreville

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Tan, Z., Madzin, H., Ding, Z. (2023). Semi-supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment. In: Sheng, B., Aubreville, M. (eds) Mitosis Domain Generalization and Diabetic Retinopathy Analysis. MIDOG DRAC 2022 2022. Lecture Notes in Computer Science, vol 13597. Springer, Cham. https://doi.org/10.1007/978-3-031-33658-4_10

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  • DOI: https://doi.org/10.1007/978-3-031-33658-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33657-7

  • Online ISBN: 978-3-031-33658-4

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