Alexander Gomez Villa,Augusto Salazar,Igor Stefanini
Abstract URL: http://arxiv.org/abs/1801.10443v1
An automatic approach to counting any kind of cells could alleviate work of
the experts and boost the research in fields such as regenerative medicine. In
this paper, a method for microscopy cell counting using multiple frames (hence
temporal information) is proposed. Unlike previous approaches where the cell
counting is done independently in each frame (static cell counting), in this
work the cell counting prediction is done using multiple frames (dynamic cell
counting). A spatiotemporal model using ConvNets and long short term memory
(LSTM) recurrent neural networks is proposed to overcome temporal variations.
The model outperforms static cell counting in a publicly available dataset of
stem cells. The advantages, working conditions and limitations of the
ConvNet-LSTM method are discussed. Although our method is tested in cell
counting, it can be extrapolated to quantify in video (or correlated image
series) any kind of objects or volumes.