State-of-the-art retinal vessel segmentation with minimalistic models

Galdran, Adrian, Anjos, André, Dolz, José, Chakor, Hadi, Lombaert, Hervé et Ben Ayed, Ismail. 2022. « State-of-the-art retinal vessel segmentation with minimalistic models ». Scientific Reports, vol. 12, nº 1.
Compte des citations dans Scopus : 29.

Prévisualisation

PDF Dolz-J-2022-24433.pdf - Version publiée Licence d'utilisation : Creative Commons CC BY. Télécharger (3MB) | Prévisualisation

Résumé

The segmentation of retinal vasculature from eye fundus images is a fundamental task in retinal image analysis. Over recent years, increasingly complex approaches based on sophisticated Convolutional Neural Network architectures have been pushing performance on well‑established benchmark datasets. In this paper, we take a step back and analyze the real need of such complexity. We first compile and review the performance of 20 different techniques on some popular databases, and we demonstrate that a minimalistic version of a standard U‑Net with several orders of magnitude less parameters, carefully trained and rigorously evaluated, closely approximates the performance of current best techniques. We then show that a cascaded extension (W‑Net) reaches outstanding performance on several popular datasets, still using orders of magnitude less learnable weights than any previously published work. Furthermore, we provide the most comprehensive cross‑dataset performance analysis to date, involving up to 10 different databases. Our analysis demonstrates that the retinal vessel segmentation is far from solved when considering test images that differ substantially from the training data, and that this task represents an ideal scenario for the exploration of domain adaptation techniques. In this context, we experiment with a simple self‑labeling strategy that enables moderate enhancement of cross‑dataset performance, indicating that there is still much room for improvement in this area. Finally, we test our approach on Artery/Vein and vessel segmentation from OCTA imaging problems, where we again achieve results well‑aligned with the state‑of‑the‑art, at a fraction of the model complexity available in recent literature. Code to reproduce the results in this paper is released.

Type de document:	Article publié dans une revue, révisé par les pairs
Informations complémentaires:	Erratum : Galdran, Adrian, André Anjos, José Dolz, Hadi Chakor, Hervé Lombaert et Ismail Ben Ayed. 2023. « Author Correction: State-of-the-art retinal vessel segmentation with minimalistic models (Scientific Reports, (2022), 12, 1, (6174), 10.1038/s41598-022-09675-y) ». Scientific Reports, vol. 13, no 1.
Professeur:	Professeur Dolz, José Lombaert, Hervé Ben Ayed, Ismail
Affiliation:	Génie logiciel et des technologies de l'information, Génie logiciel et des technologies de l'information, Génie des systèmes
Date de dépôt:	02 juin 2022 19:08
Dernière modification:	30 août 2023 19:44
URI:	https://espace2.etsmtl.ca/id/eprint/24433

Actions (Authentification requise)

Dernière vérification avant le dépôt