Robust Unsupervised Image to Template Registration Without Image Similarity Loss - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Robust Unsupervised Image to Template Registration Without Image Similarity Loss

Résumé

Although a giant step forward has been made in medical images analysis thanks to deep learning, good results still require a lot of tedious and costly annotations. For image registration, unsupervised methods usually consider the training of a network using classical registration dissimilarity metrics. In this paper, we focus on the case of affine registration and show that this approach is not robust when the transform to estimate is large. We propose an unsupervised method for the training of an affine image registration network without using dissimilarity metrics and show that we are able to robustly register images even when the field of view is significantly different in the image.
Fichier principal
Vignette du fichier
hachicha-le-2023-deepreg.pdf (609.29 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04183379 , version 1 (19-08-2023)

Identifiants

  • HAL Id : hal-04183379 , version 1

Citer

Slim Hachicha, Célia Le, Valentine Wargnier-Dauchelle, Michaël Sdika. Robust Unsupervised Image to Template Registration Without Image Similarity Loss. Medical Image Learning with Limited and Noisy Data, Second International Workshop, MILLanD 2023, Held in Conjunction with MICCAI 2023, Vancouver, Proceedings, Oct 2023, Vancouver, Canada. ⟨hal-04183379⟩
52 Consultations
67 Téléchargements

Partager

Gmail Facebook X LinkedIn More