Automatic cell counting from stimulated Raman imaging using deep learning.
In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor diagnosis and surgery by mapping lipids and proteins...
Guardado en:
Autores principales: | Qianqian Zhang, Kyung Keun Yun, Hao Wang, Sang Won Yoon, Fake Lu, Daehan Won |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/77bdde8192a5427592974467cc74c400 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Automatic Localization and Count of Agricultural Crop Pests Based on an Improved Deep Learning Pipeline
por: Weilu Li, et al.
Publicado: (2019) -
Annotation-efficient deep learning for automatic medical image segmentation
por: Shanshan Wang, et al.
Publicado: (2021) -
Deep Learning for Automatic Image Captioning in Poor Training Conditions
por: Caterina Masotti, et al.
Publicado: (2018) -
Assessing fatty acid-induced lipotoxicity and its therapeutic potential in glioblastoma using stimulated Raman microscopy
por: Yuhao Yuan, et al.
Publicado: (2021) -
Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning
por: Haonan Lin, et al.
Publicado: (2021)