Junyan Lyu
ABSTRACT
In this work, we proposed and validated a novel transfer learning based method for automatic cornea segmentation from ocular staining images. An encoder-decoder structure was used, containing an Xception feature extractor, Atrous Spatial Pyramid Pooling and a simple but effective decoder. The proposed method successfully solved the problem of training a large network with limited data and capturing contextual information with a relatively simple network by fine-tuning a large network that had been pretrained on large-scale common object datasets. A total of 712 ocular staining images were used in our experiments. Evaluations were conducted both quantitatively and qualitatively through 5-fold cross-validation. Compared with several other deep learning based segmentation techniques, including U-Net, Fully Convolutional Network (FCN), and DeepLab, the proposed method was found to exhibit superior performance, with a Dice score of 95.82%, sensitivity of 95.37%, and accuracy of 97.63%. We also evaluated the method with different transfer learning strategies and found that fine-tuning the whole network worked the best in terms of segmentation accuracy.
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Digital Library
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