Paper Info
Title
Accurate And Fast Mitotic Detection Using An Anchor-Free Method Based On Full-Scale Connection With Recurrent Deep Layer Aggregation In 4d Microscopy Images
Abstract
BackgroundTo 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 object detection tasks, however, when applied to mitotic cell detection, most existing methods generate high false-positive rates due to the complex characteristics that differentiate normal cells from mitotic cells. Cell size and orientation variations in each stage make detecting mitotic cells difficult in 2D approaches. Therefore, effective extraction of the spatial and temporal features from mitotic data is an important and challenging task. The computational time required for detection is another major concern for mitotic detection in 4D microscopic images.ResultsIn this paper, we propose a backbone feature extraction network named full scale connected recurrent deep layer aggregation (RDLA++) for anchor-free mitotic detection. We utilize a 2.5D method that includes 3D spatial information extracted from several 2D images from neighbouring slices that form a multi-stream input.ConclusionsOur proposed technique addresses the scale variation problem and can efficiently extract spatial and temporal features from 4D microscopic images, resulting in improved detection accuracy and reduced computation time compared with those of other state-of-the-art methods.
Year
DOI
Venue
2021
10.1186/s12859-021-04014-w
BMC BIOINFORMATICS
Keywords
DocType
Volume
4D image, cell, detection, microscopic image, mitotic
Journal
22
Issue
ISSN
Citations 
1
1471-2105
0
PageRank 
References 
Authors
0.34
0
10
Name
Order
Citations
PageRank
Titinunt Kitrungrotsakul164.15
Yutaro Iwamoto21317.95
Satoko Takemoto334.42
Hideo Yokota47816.87
Sari Ipponjima502.03
Tomomi Nemoto620.70
Lanfen Lin748.67
Ruofeng Tong846649.69
Jingsong Li912.04
Yen-Wei Chen1000.34