Cargando…
Deep learning-based segmentation of epithelial ovarian cancer on T2-weighted magnetic resonance images
BACKGROUND: Epithelial ovarian cancer (EOC) segmentation is an indispensable step in assessing the extent of disease and guiding the treatment plan that follows. Currently, manual segmentation is the most commonly used method, despite it being tedious, time-consuming and subject to inter- and intra-...
Autores principales: | Hu, Dingdu, Jian, Junming, Li, Yongai, Gao, Xin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006162/ https://www.ncbi.nlm.nih.gov/pubmed/36915355 http://dx.doi.org/10.21037/qims-22-494 |
Ejemplares similares
-
Deep learning magnetic resonance imaging predicts platinum sensitivity in patients with epithelial ovarian cancer
por: Lei, Ruilin, et al.
Publicado: (2022) -
Deep learning-based magnetic resonance image segmentation technique for application to glioma
por: Wan, Bing, et al.
Publicado: (2023) -
Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
por: Comelli, Albert, et al.
Publicado: (2021) -
Deep-Learning-Based Segmentation of Extraocular Muscles from Magnetic Resonance Images
por: Qureshi, Amad, et al.
Publicado: (2023) -
Deep learning-based automated lesion segmentation on mouse stroke magnetic resonance images
por: An, Jeehye, et al.
Publicado: (2023)