Paper Info
Title
A Cascade of 2.5D CNN and Bidirectional CLSTM Network for Mitotic Cell Detection in 4D Microscopy Image
Abstract
AbstractMitosis detection is one of the challenging steps in biomedical imaging research, which can be used to observe the cell behavior. Most of the already existing methods that are applied in detecting mitosis usually contain many nonmitotic events (normal cell and background) in the result (false positives, FPs). In order to address such a problem, in this study, we propose to apply 2.5-dimensional (2.5D) networks called CasDetNet_CLSTM, which can accurately detect mitotic events in 4D microscopic images. This CasDetNet_CLSTM involves a 2.5D faster region-based convolutional neural network (Faster R-CNN) as the first network, and a convolutional long short-term memory (CLSTM) network as the second network. The first network is used to select candidate cells using the information from nearby slices, whereas the second network uses temporal information to eliminate FPs and refine the result of the first network. Our experiment shows that the precision and recall of our networks yield better results than those of other state-of-the-art methods.
Year
DOI
Venue
2021
10.1109/TCBB.2019.2919015
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Keywords
DocType
Volume
Computer architecture, Microprocessors, Two dimensional displays, Three-dimensional displays, Microscopy, Training, Feature extraction, Mitosis detection, region proposal network, long short-term memory, 4D microscope data, 2, 5 dimensional
Journal
18
Issue
ISSN
Citations 
2
1545-5963
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Titinunt Kitrungrotsakul164.15
Xian-Hau Han200.68
Yutaro Iwamoto31317.95
Satoko Takemoto434.42
Hideo Yokota57816.87
Sari Ipponjima602.03
Tomomi Nemoto742.03
Wei Xiong8236.75
Yen-Wei Chen9720155.73