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Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation

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  • Sergio Sanchez
  • Noelia Vallez
  • Gloria Bueno
  • Andres G Marrugo

Abstract

Image segmentation of the corneal endothelium with deep convolutional neural networks (CNN) is challenging due to the scarcity of expert-annotated data. This work proposes a data augmentation technique via warping to enhance the performance of semi-supervised training of CNNs for accurate segmentation. We use a unique augmentation process for images and masks involving keypoint extraction, Delaunay triangulation, local affine transformations, and mask refinement. This approach accurately captures the natural variability of the corneal endothelium, enriching the dataset with realistic and diverse images. The proposed method achieved an increase in the mean intersection over union (mIoU) and Dice coefficient (DC) metrics of 17.2% and 4.8% respectively, for the segmentation task in corneal endothelial images on multiple CNN architectures. Our data augmentation strategy successfully models the natural variability in corneal endothelial images, thereby enhancing the performance and generalization capabilities of semi-supervised CNNs in medical image cell segmentation tasks.

Suggested Citation

  • Sergio Sanchez & Noelia Vallez & Gloria Bueno & Andres G Marrugo, 2024. "Data augmentation via warping transforms for modeling natural variability in the corneal endothelium enhances semi-supervised segmentation," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-18, November.
  • Handle: RePEc:plo:pone00:0311849
    DOI: 10.1371/journal.pone.0311849
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    1. Alaa S. Al-Waisy & Abdulrahman Alruban & Shumoos Al-Fahdawi & Rami Qahwaji & Georgios Ponirakis & Rayaz A. Malik & Mazin Abed Mohammed & Seifedine Kadry, 2022. "CellsDeepNet: A Novel Deep Learning-Based Web Application for the Automated Morphometric Analysis of Corneal Endothelial Cells," Mathematics, MDPI, vol. 10(3), pages 1-26, January.
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