  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 dataset.
Simultaneously, the proposed method also obtains the satisfactory glaucoma
screening performances with calculated CDR value on both ORIGA and SCES
datasets.
