Improving abdominal image segmentation with overcomplete shape priors - IMT Atlantique Accéder directement au contenu
Article Dans Une Revue Computerized Medical Imaging and Graphics Année : 2024

Improving abdominal image segmentation with overcomplete shape priors

Résumé

The extraction of abdominal structures using deep learning has recently experienced a widespread interest in medical image analysis. Automatic abdominal organ and vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy, or surgical planning. Despite a good ability to extract large organs, the capacity of U-Net inspired architectures to automatically delineate smaller structures remains a major issue, especially given the increase in receptive field size as we go deeper into the network. To deal with various abdominal structure sizes while exploiting efficient geometric constraints, we present a novel approach that integrates into deep segmentation shape priors from a semi-overcomplete convolutional auto-encoder (S-OCAE) embedding. Compared to standard convolutional auto-encoders (CAE), it exploits an over-complete branch that projects data onto higher dimensions to better characterize anatomical structures with a small spatial extent. Experiments on abdominal organs and vessel delineation performed on various publicly available datasets highlight the effectiveness of our method compared to state-of-the-art, including U-Net trained without and with shape priors from a traditional CAE. Exploiting a semi-overcomplete convolutional auto-encoder embedding as shape priors improves the ability of deep segmentation models to provide realistic and accurate abdominal structure contours.
Fichier principal
Vignette du fichier
1-s2.0-S0895611124000338-main.pdf (3.56 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Licence : CC BY NC ND - Paternité - Pas d'utilisation commerciale - Pas de modification

Dates et versions

hal-04443431 , version 1 (27-02-2024)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu, Vincent Noblet, Pascal Ballet, et al.. Improving abdominal image segmentation with overcomplete shape priors. Computerized Medical Imaging and Graphics, 2024, 113 (April), pp.102356. ⟨10.1016/j.compmedimag.2024.102356⟩. ⟨hal-04443431⟩
26 Consultations
2 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More