cGAN-Based Lacquer Cracks Segmentation in ICGA Image

The increasing prevalence of high myopia has raised concern worldwide. In high myopia, myopia macular degeneration (MMD) is a major cause of vision impairment and lacquer crack (LC) is one of the main signs of MMD. Since the development of LC can reflect the severity of MMD, it is important and meaningful to segment LCs. Indocyanine green angiography (ICGA) has been used for visualizing LCs and is considered to be superior to fluorescein angiography (FA). However, LCs segmentation is difficult due to the image blurring and the confusion between LCs and the background. In this paper, we propose an automatic LCs segmentation method based on the improved conditional generative adversarial nets (cGAN). To apply the advanced cGAN on ICGA images, Dice loss function is added to improve the accuracy of segmentation. Experiments on the ICGA images of high myopia denoted that the proposed method can successfully segment LCs with the trained model and achieve better performance than other popular nets.

[1]  Alexei A. Efros,et al.  Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  José García Rodríguez,et al.  A survey on deep learning techniques for image and video semantic segmentation , 2018, Appl. Soft Comput..

[3]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[4]  G Coscas,et al.  Indocyanine green angiographic features of pathologic myopia. , 1996, American journal of ophthalmology.

[5]  Y. Tano,et al.  LACQUER CRACK FORMATION AND CHOROIDAL NEOVASCULARIZATION IN PATHOLOGIC MYOPIA , 2008, Retina.

[6]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Kuan-Jen Chen,et al.  Choroidal Thickness and Biometric Markers for the Screening of Lacquer Cracks in Patients with High Myopia , 2013, PloS one.

[8]  Alexei A. Efros,et al.  Generative Visual Manipulation on the Natural Image Manifold , 2016, ECCV.

[9]  K. Ohno-Matsui,et al.  Indocyanine green angiographic findings of lacquer cracks in pathologic myopia. , 1998, Japanese journal of ophthalmology.

[10]  Abhinav Gupta,et al.  Generative Image Modeling Using Style and Structure Adversarial Networks , 2016, ECCV.

[11]  Chuan Li,et al.  Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks , 2016, ECCV.

[12]  Seyed-Ahmad Ahmadi,et al.  V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation , 2016, 2016 Fourth International Conference on 3D Vision (3DV).

[13]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[14]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  K. Ohno-Matsui,et al.  THE PROGRESSION OF LACQUER CRACKS IN PATHOLOGIC MYOPIA , 1996, Retina.

[16]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[17]  Yann LeCun,et al.  Deep multi-scale video prediction beyond mean square error , 2015, ICLR.