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

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 en

SUBMITTER: Sanchez S 

PROVIDER: S-EPMC11556704 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature