An optic disk semantic segmentation method based on weakly supervised learning

Weakly supervised semantic segmentation has been widely used in the filed of computer vision. since it does not require expert annotations for training. Recently, some works use pseudo ground-truths which are generated by a classified network to train the model, however, this method is not suitable for medical image segmentation. To tackle this challenging problem, we use the GrabCut method to generate the coarse foreground segmentation map in this paper, and then we train the network based on a modified U-net model with the generated foreground map. Extensive experiments on the challenging RIM-ONE benchmarks strongly demonstrate the effectiveness of our algorithm. We obtain state-of-art results on RIM-ONE database.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  U. Rajendra Acharya,et al.  Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network , 2017, J. Comput. Sci..

[3]  Yueh-Yi Lai,et al.  67.5: Image Detail and Color Enhancement Based on Channel‐Wise Local Gamma Adjustment , 2009 .

[4]  Ieee Staff.  2014 IEEE 10th International Colloquium on Signal Processing and Its Applications (CSPA) , 2014 .

[5]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[6]  Giri Babu Kande,et al.  Segmentation of optic disk and optic cup from digital fundus images for the assessment of glaucoma , 2016, Biomed. Signal Process. Control..

[7]  Joachim M. Buhmann,et al.  A field of experts model for optic cup and disc segmentation from retinal fundus images , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[8]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[9]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[10]  A. Sevastopolsky,et al.  Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network , 2017, Pattern Recognition and Image Analysis.

[11]  Jayanthi Sivaswamy,et al.  Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment , 2011, IEEE Transactions on Medical Imaging.

[12]  Francisco Fumero,et al.  RIM-ONE: An open retinal image database for optic nerve evaluation , 2011, 2011 24th International Symposium on Computer-Based Medical Systems (CBMS).

[13]  Muhammad Moazam Fraz,et al.  Fast Optic Disc Segmentation in Retina Using Polar Transform , 2017, IEEE Access.

[14]  Junaidi Abdullah,et al.  Automated segmentation of optic disc in fundus images , 2014, 2014 IEEE 10th International Colloquium on Signal Processing and its Applications.

[15]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[16]  Christoph H. Lampert,et al.  Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation , 2016, ECCV.

[17]  Marios S. Pattichis,et al.  Fast Localization and Segmentation of Optic Disk in Retinal Images Using Directional Matched Filtering and Level Sets , 2012, IEEE Transactions on Information Technology in Biomedicine.

[18]  Tien Yin Wong,et al.  Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening , 2013, IEEE Transactions on Medical Imaging.

[19]  Luc Van Gool,et al.  Deep Retinal Image Understanding , 2016, MICCAI.

[20]  Muhammad Moazam Fraz,et al.  Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm , 2016, PeerJ.