Learning fully-connected CRFs for blood vessel segmentation in retinal images - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2014

Learning fully-connected CRFs for blood vessel segmentation in retinal images

Résumé

In this work, we present a novel method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Retinal image analysis is greatly aided by blood vessel segmentation as the vessel structure may be considered both a key source of signal, e.g. in the diagnosis of diabetic retinopathy, or a nuisance, e.g. in the analysis of pigment epithelium or choroid related abnormalities. Blood vessel segmentation in fundus images has been considered extensively in the literature, but remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors such as a Potts model or total variation. In this work, we overcome this difficulty using a discriminatively trained conditional random field model with more expressive potentials. In particular, we employ recent results enabling extremely fast inference in a fully connected model. We find that this rich but computationally efficient model family, combined with principled discriminative training based on a structured output support vector machine yields a fully automated system that achieves results statistically indistinguishable from an expert human annotator. Implementation details are available at http://pages.saclay.inria.fr/matthew.blaschko/projects/retina/.
Fichier principal
Vignette du fichier
OrlandoMICCAI2014.pdf (1.47 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01024226 , version 1 (17-07-2014)

Identifiants

Citer

José Ignacio Orlando, Matthew Blaschko. Learning fully-connected CRFs for blood vessel segmentation in retinal images. Medical Image Computing and Computer Assisted Intervention (MICCAI), Sep 2014, Boston, United States. ⟨10.1007/978-3-319-10404-1_79⟩. ⟨hal-01024226⟩
366 Consultations
2799 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More