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

BMC Bioinformatics

Satoko Takemoto¹  Hideo Yokota¹  Lanfen Lin²  Yutaro Iwamoto³  Jingsong Li⁴  Ruofeng Tong⁵  Titinunt Kitrungrotsakul⁶  Yen-Wei Chen⁷  Tomomi Nemoto⁸  Sari Ipponjima⁸ 

[1] Center for Advanced Photonics, RIKEN, Wako, Saitama, Japan;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China;College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China;Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China;Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China;Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;Research Institute for Electronic Science, Hokkaido University, Sapporo, Hokkaido, Japan;

关键词: 4D image;    cell;    detection;    microscopic image;    mitotic;   

DOI  :  10.1186/s12859-021-04014-w

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY