In recent years, intravital skin imaging has been increasingly used in mammalian skin research to investigate cell behaviors. A fundamental step of the investigation is mitotic cell (cell division) detection. Because of the complex backgrounds (normal cells), the majority of the existing methods cause several false positives. In this paper, we proposed a 2.5D cascaded end-to-end convolutional neural network (CasDetNet) with temporal information to accurately detect automatic mitotic cell in 4D microscopic images with few training data. The CasDetNet consists of two 2.5D networks. The first one is used for detecting candidate cells with only volume information and the second one, containing temporal information, for reducing false positive and adding mitotic cells that were missed in the first step. The experimental results show that our CasDetNet can achieve higher precision and recall compared to other state-of-the-art methods.
@article{arxiv.1806.01018,
title = {A 2.5D Cascaded Convolutional Neural Network with Temporal Information for Automatic Mitotic Cell Detection in 4D Microscopic Images},
author = {Titinunt Kitrungrotsakul and Xian-Hau Han and Yutaro Iwamoto and Satoko Takemoto and Hideo Yokota and Sari Ipponjima and Tomomi Nemoto and Xiong Wei and Yen-Wei Chen},
journal= {arXiv preprint arXiv:1806.01018},
year = {2018}
}
Comments
4 pages, 4 figures, conference paper (submitted to arxiv then update version and submitted to conference. Finally update in arxiv for newest version)