Optic disc segmentation by U-net and probability bubble in abnormal fundus images

Highlights：

• The deep learning architecture fused with the model-driven probability bubble approach is proposed to segment OD in abnormal fundus images to improve the performance when lack sufficient training samples in medical community.

• A brand-new unsupervised probability bubble technique is figured out according to the position relationship between retinal vessels an OD, by which the main blood vessels are fitted by line segments through hough transform, and the density of intersection points of the lines indicates the probability of OD.

• The joint probability of data-driven U-net and model-driven probability bubble approach is calculated to locate the OD. The localization based on the joint probability is more robust than each independent method, which ensures effectiveness of the proposed method.

• The work in this paper provides a reference for the application of deep learning in medical community that the model-driven approach can be fused into the architecture and promote the performance when there are insufficient training samples available and deep learning can’t provide an accurate result.