Communication Dans Un Congrès Année : 2024

Self super-resolution of anisotropic volumes in prostate MRI with normalized edge priors

Résumé

Due to involuntary organ motion, prostate magnetic resonance images are often acquired in 2D with thick through plane spacings and reconstructed into anisotropic voxelized volumes. In prostate cancer (PCa) radiotherapy, annotations such as zonal or lesion segmentation are then performed on the few axial slices in which the prostate appears, leading to coarse and often inconsistent delineations across slices, which are nevertheless used as ground truth for deep segmentation model training and performance reports. Recently, deep self-supervised super resolution (SSR) was proposed in brain MRI to remove the need for calibrated training data, by learning the low-resolution to high-resolution mapping from the anisotropic image itself. In this work, we propose a new training objective (loss function) for SSR based on the alignment of normalized image gradient vectors, a technique normally used for image registration. Preliminary experiments on the public Prostate158 dataset suggest that automatic PCa detection could be improved using SSR with the proposed training objective. However, the low quality of lesion annotations obtained on a few slices is a major bottleneck to confirm these findings quantitatively.

Fichier principal
Vignette du fichier
Jaouen_iccr2024.pdf (560.24 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Licence

Dates et versions

hal-04714832 , version 1 (30-09-2024)

Licence

Identifiants

  • HAL Id : hal-04714832 , version 1

Citer

Vincent Jaouen, Pierre-Henri Conze, Julien Bert, Dimitris Visvikis. Self super-resolution of anisotropic volumes in prostate MRI with normalized edge priors. ICCR 2024: XXth International Conference on the use of Computers in Radiation therapy, Jul 2024, Lyon, France. ⟨hal-04714832⟩
181 Consultations
189 Téléchargements

Partager

  • More