Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models
Abstract Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much more strenuous than in 2D images. For...
Guardado en:
Autores principales: | Dimitrios Bellos, Mark Basham, Tony Pridmore, Andrew P. French |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/a20a76b6c52f42b2aa60d53e45e0824b |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.
por: Dan Zhu, et al.
Publicado: (2021) -
Undersampling bankruptcy prediction: Taiwan bankruptcy data.
por: Haoming Wang, et al.
Publicado: (2021) -
Annotation Tool and Urban Dataset for 3D Point Cloud Semantic Segmentation
por: Muhammad Ibrahim, et al.
Publicado: (2021) -
Refined Color Texture Classification Using CNN and Local Binary Pattern
por: Khalid M. Hosny, et al.
Publicado: (2021) -
Cross‐modal semantic correlation learning by Bi‐CNN network
por: Chaoyi Wang, et al.
Publicado: (2021)