Cargando…
Leveraging multimodal microscopy to optimize deep learning models for cell segmentation
Deep learning provides an opportunity to automatically segment and extract cellular features from high-throughput microscopy images. Many labeling strategies have been developed for this purpose, ranging from the use of fluorescent markers to label-free approaches. However, differences in the channe...
Autores principales: | Cameron, William D., Bennett, Alex M., Bui, Cindy V., Chang, Huntley H., Rocheleau, Jonathan V. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AIP Publishing LLC
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7785326/ https://www.ncbi.nlm.nih.gov/pubmed/33415313 http://dx.doi.org/10.1063/5.0027993 |
Ejemplares similares
-
Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy
por: Tseng, Vincent S., et al.
Publicado: (2020) -
Geographic Atrophy Segmentation Using Multimodal Deep Learning
por: Spaide, Theodore, et al.
Publicado: (2023) -
Targeting Apollo-NADP(+) to Image NADPH Generation
in Pancreatic Beta-Cell Organelles
por: Chang, Huntley H., et al.
Publicado: (2022) -
Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning
por: Gesperger, Johanna, et al.
Publicado: (2020) -
Towards fully automated segmentation of rat cardiac MRI by leveraging deep learning frameworks
por: Fernández-Llaneza, Daniel, et al.
Publicado: (2022)