Abstract
Ultra-wide (UW) optical coherence tomography angiography (OCTA) imaging provides new opportunities for diagnosing medical diseases. To further support doctors in the recognition of diseases, automated image analysis pipelines would be helpful. Therefore the MICCAI DRAC 2022 challenge was carried out, which provided a standardized UW (swept-source) OCTA data set for testing the effectiveness of various algorithms on a diabetic retinopathy (DR) dataset. Our team tried to train well-performing segmentation models for UW-OCTA analysis and was finally ranked under the three top-performing teams for segmenting DR lesions. This paper, therefore, summarizes our proposed strategy for this task and further describes our approach for image quality assessment and DR Grading.
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Acknowledgements
The authors would like to thank the project AIRI FG 9-N (FWF-36284, FWF-36235) for helpful support. Further, the authors would like to thank ELLIS Unit Linz, the LIT AI Lab, and the Institute for Machine Learning for providing computing resources to participate in the challenge.
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Krause, F., Heindl, D., Jebril, H., Karner, M., Unterdechler, M. (2023). nnU-Net Pre- and Postprocessing Strategies for UW-OCTA Segmentation Tasks in Diabetic Retinopathy Analysis. 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_1
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DOI: https://doi.org/10.1007/978-3-031-33658-4_1
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