  Fundus photography has been routinely used to document the presence and
severity of various retinal degenerative diseases such as age-related macula
degeneration, glaucoma, and diabetic retinopathy, for which the fovea, optic
disc (OD), and optic cup (OC) are important anatomical landmarks.
Identification of those anatomical landmarks is of great clinical importance.
However, the presence of lesions, drusen, and other abnormalities during
retinal degeneration severely complicates automatic landmark detection and
segmentation. Most existing works treat the identification of each landmark as
a single task and typically do not make use of any clinical prior information.
In this paper, we present a novel method, named JOINED, for prior guided
multi-task learning for joint OD/OC segmentation and fovea detection. An
auxiliary branch for distance prediction, in addition to a segmentation branch
and a detection branch, is constructed to effectively utilize the distance
information from each image pixel to landmarks of interest. Our proposed JOINED
pipeline consists of a coarse stage and a fine stage. At the coarse stage, we
obtain the OD/OC coarse segmentation and the heatmap localization of fovea
through a joint segmentation and detection module. Afterwards, we crop the
regions of interest for subsequent fine processing and use predictions obtained
at the coarse stage as additional information for better performance and faster
convergence. Experimental results reveal that our proposed JOINED outperforms
existing state-of-the-art approaches on the publicly-available GAMMA, PALM, and
REFUGE datasets of fundus images. Furthermore, JOINED ranked the 5th on the
OD/OC segmentation and fovea detection tasks in the GAMMA challenge hosted by
the MICCAI2021 workshop OMIA8.
