Protocol for live cell image segmentation to profile cellular morphodynamics using MARS-Net.
STAR Protoc
; 3(3): 101469, 2022 09 16.
Article
en En
| MEDLINE
| ID: mdl-35733606
Quantitative studies of cellular morphodynamics rely on accurate cell segmentation in live cell images. However, fluorescence and phase contrast imaging hinder accurate edge localization. To address this challenge, we developed MARS-Net, a deep learning model integrating ImageNet-pretrained VGG19 encoder and U-Net decoder trained on the datasets from multiple types of microscopy images. Here, we provide the protocol for installing MARS-Net, labeling images, training MARS-Net for edge localization, evaluating the trained models' performance, and performing the quantitative profiling of cellular morphodynamics. For complete details on the use and execution of this protocol, please refer to Jang et al. (2021).
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Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
/
Redes Neurales de la Computación
Idioma:
En
Revista:
STAR Protoc
Año:
2022
Tipo del documento:
Article
Pais de publicación:
Estados Unidos