  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.
