Cargando…
Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels
Accurate segmentation of lungs in pathological thoracic computed tomography (CT) scans plays an important role in pulmonary disease diagnosis. However, it is still a challenging task due to the variability of pathological lung appearances and shapes. In this paper, we proposed a novel segmentation a...
Autores principales: | Liu, Caixia, Zhao, Ruibin, Xie, Wangli, Pang, Mingyong |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7413019/ https://www.ncbi.nlm.nih.gov/pubmed/32837245 http://dx.doi.org/10.1007/s11063-020-10330-8 |
Ejemplares similares
-
Superpixel-based segmentation of muscle fibers in multi-channel microscopy
por: Nguyen, Binh P., et al.
Publicado: (2016) -
Semantic characteristic grading of pulmonary nodules based on deep neural networks
por: Liu, Caixia, et al.
Publicado: (2023) -
A Segmentation Method for Lung Parenchyma Image Sequences Based on Superpixels and a Self-Generating Neural Forest
por: Liao, Xiaolei, et al.
Publicado: (2016) -
Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification
por: Wang, Nanlan, et al.
Publicado: (2022) -
Automatic glioma segmentation based on adaptive superpixel
por: Wu, Yaping, et al.
Publicado: (2019)