English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Fundus photography has been routinely used to document the presence andseverity of various retinal degenerative diseases such as age-related maculadegeneration, glaucoma, and diabetic retinopathy, for which the fovea, opticdisc (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 duringretinal degeneration severely complicates automatic landmark detection andsegmentation. Most existing works treat the identification of each landmark asa 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 guidedmulti-task learning for joint OD/OC segmentation and fovea detection. Anauxiliary branch for distance prediction, in addition to a segmentation branchand a detection branch, is constructed to effectively utilize the distanceinformation from each image pixel to landmarks of interest. Our proposed JOINEDpipeline consists of a coarse stage and a fine stage. At the coarse stage, weobtain the OD/OC coarse segmentation and the heatmap localization of foveathrough a joint segmentation and detection module. Afterwards, we crop theregions of interest for subsequent fine processing and use predictions obtainedat the coarse stage as additional information for better performance and fasterconvergence. Experimental results reveal that our proposed JOINED outperformsexisting state-of-the-art approaches on the publicly-available GAMMA, PALM, andREFUGE datasets of fundus images. Furthermore, JOINED ranked the 5th on theOD/OC segmentation and fovea detection tasks in the GAMMA challenge hosted bythe MICCAI2021 workshop OMIA8.

JOINED : Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection