Cargando…
Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images
Magnetic resonance imaging (MRI) has a growing role in the clinical workup of prostate cancer. However, manual three-dimensional (3D) segmentation of the prostate is a laborious and time-consuming task. In this scenario, the use of automated algorithms for prostate segmentation allows us to bypass t...
Autores principales: | Salvi, Massimo, De Santi, Bruno, Pop, Bianca, Bosco, Martino, Giannini, Valentina, Regge, Daniele, Molinari, Filippo, Meiburger, Kristen M. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9146644/ https://www.ncbi.nlm.nih.gov/pubmed/35621897 http://dx.doi.org/10.3390/jimaging8050133 |
Ejemplares similares
-
Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images
por: Salvi, Massimo, et al.
Publicado: (2018) -
The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks
por: Veronese, Federica, et al.
Publicado: (2021) -
A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation
por: Giannini, Valentina, et al.
Publicado: (2021) -
Quantitative ultrasound and photoacoustic imaging for the assessment of vascular parameters
por: Meiburger, Kristen M
Publicado: (2017) -
Segmentation of abdomen MR images using kernel graph cuts with shape priors
por: Luo, Qing, et al.
Publicado: (2013)