Transfer Learning for Automatic Cornea Segmentation based on Ocular Staining Images

Lyu，Junyan; Qiu，Jiaming; Deng，Lijie; Zhang，Yue; Ye，Terry Tao Tao; Tang，Xiaoying

10.1145/3451421.3451445

2020-12-05

ACM International Conference Proceeding Series

108-111

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.

Convolutional Neural Networks Image Segmentation Transfer Learning

第一 ; 通讯

英语

EI

20213610870631

Convolutional neural networks ; Decoding ; Deep learning ; Image segmentation ; Large dataset ; Learning systems

Data Processing and Image Processing:723.2

2-s2.0-85114275657

Scopus

Department of Electrical and Electronic Engineering,Southern University of Science and Technology,Shenzhen,China

Lyu，Junyan,Qiu，Jiaming,Deng，Lijie,et al. Transfer Learning for Automatic Cornea Segmentation based on Ocular Staining Images[C],2020:108-111.