Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation

Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation
Title:
Joint Optic Disc and Cup Segmentation Based on Multi-label Deep Network and Polar Transformation
Other Titles:
IEEE Transactions on Medical Imaging
DOI:
10.1109/TMI.2018.2791488
Keywords:
Publication Date:
09 January 2018
Citation:
H. Fu, J. Cheng, Y. Xu, D. W. K. Wong, J. Liu and X. Cao, "Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation," in IEEE Transactions on Medical Imaging, vol. 37, no. 7, pp. 1597-1605, July 2018.
Abstract:
Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The U-shape convolutional network is employed as the main body network structure to learn the rich hierarchical representation, while the side-output layer acts as an early classifier that produces a companion local prediction map for different scale layers. Finally, a multi-label loss function is proposed to generate the final segmentation map. For improving the segmentation performance further, we also introduce the polar transformation, which provides the representation of the original image in the polar coordinate system. The experiments show that our M-Net system achieves state-of-the-art OD and OC segmentation result on ORIGA data set. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated CDR value on both ORIGA and SCES datasets.
License type:
PublisherCopyrights
Funding Info:
Description:
© 2018 IEEE
ISSN:
0278-0062
1558-254X
Files uploaded:

File Size Format Action
tmi-deepcdr-cr.pdf 2.38 MB PDF Open