Accurate and fast mitotic detection using an anchor-free method based on full-scale connection with recurrent deep layer aggregation in 4D microscopy images

Accurate and fast mitotic detection using an anchor-free method based on full-scale connection with recurrent deep layer aggregation in 4D microscopy images

Background: To effectively detect and investigate various cell-related diseases, it is essential to understand cell behaviour. The ability to detection mitotic cells is a fundamental step in diagnosing cell-related diseases. Convolutional neural networks (CNNs) have been successfully applied to obje...

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Bibliographic Details
Main Authors: Chen, Y.-W (Author), Ipponjima, S. (Author), Iwamoto, Y. (Author), Kitrungrotsakul, T. (Author), Li, J. (Author), Lin, L. (Author), Nemoto, T. (Author), Takemoto, S. (Author), Tong, R. (Author), Yokota, H. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
4D image
cell
cell function
Cell Physiological Phenomena
Cells
Complex characteristics
Computational time
Convolutional neural networks
Cytology
detection
Detection accuracy
Diagnosis
Extraction
False positive rates
Feature extraction
Image enhancement
microscopic image
Microscopic image
microscopy
Microscopy
mitotic
Neural Networks, Computer
Object detection
Recurrent neural networks
Spatial informations
State-of-the-art methods
Temporal features
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