| Titre : | Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy (2022) |
| Auteurs : | M. Chen ; S. Wu ; W. Zhao ; Y. Zhou ; Y. Zhou ; G. Wang |
| Type de document : | Article |
| Dans : | Cancer/Radiothérapie (Vol. 26, n° 3, Mai 2022) |
| Article en page(s) : | p. 494-501 |
| Note générale : | https://doi.org/10.1016/j.canrad.2021.08.020 |
| Langues: | Anglais |
| Descripteurs : |
HE Vinci Apprentissage profond ; Intelligence artificielle (IA) ; Organes à risque ; Radiothérapie |
| Mots-clés: | Volumes cibles |
| Résumé : | The technological advancement heralded the arrival of precision radiotherapy (RT), thereby increasing the therapeutic ratio and decreasing the side effects from treatment. Contour of target volumes (TV) and organs at risk (OARs) in RT is a complicated process. In recent years, automatic contouring of TV and OARs has rapidly developed due to the advances in deep learning (DL). This technology has the potential to save time and to reduce intra- or inter-observer variability. In this paper, the authors provide an overview of RT, introduce the concept of DL, summarize the data characteristics of the included literature, summarize the possible challenges for DL in the future, and discuss the possible research directions. |
| Disponible en ligne : | Oui |
| En ligne : | https://login.ezproxy.vinci.be/login?url=https://www.sciencedirect.com/science/article/pii/S1278321821002547 |
Bibliothèques HE Vinci
