Joint Extraction of Retinal Vessels and Centerlines Based on Deep Semantics and Multi-Scaled Cross-Task Aggregation

Retinal vessel segmentation and centerline extraction are crucial steps in building a computer-aided diagnosis system on retinal images. Previous works treat them as two isolated tasks, while ignoring their tight association. In this paper, we propose a deep semantics and multi-scaled cross-task aggregation network that takes advantage of the association to jointly improve their performances. Our network is featured by two sub-networks. The forepart is a deep semantics aggregation sub-network that aggregates strong semantic information to produce more powerful features for both tasks, and the tail is a multi-scaled cross-task aggregation sub-network that explores complementary information to refine the results. We evaluate the proposed method on three public databases, which are DRIVE, STARE and CHASE_DB1. Experimental results show that our method can not only simultaneously extract retinal vessels and their centerlines but also achieve the state-of-the-art performances on both tasks.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:25

Enthalten in:

IEEE journal of biomedical and health informatics - 25(2021), 7 vom: 15. Juli, Seite 2722-2732

Sprache:

Englisch

Beteiligte Personen:

Xu, Rui [VerfasserIn]
Liu, Tiantian [VerfasserIn]
Ye, Xinchen [VerfasserIn]
Liu, Fei [VerfasserIn]
Lin, Lin [VerfasserIn]
Li, Liang [VerfasserIn]
Tanaka, Satoshi [VerfasserIn]
Chen, Yen-Wei [VerfasserIn]

Links:

Volltext

Themen:

Journal Article
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 24.09.2021

Date Revised 24.09.2021

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1109/JBHI.2020.3044957

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM318859599