  Cell shape analysis is important in biomedical research. Deep learning
methods may perform to segment individual cells if they use sufficient training
data that the boundary of each cell is annotated. However, it is very
time-consuming for preparing such detailed annotation for many cell culture
conditions. In this paper, we propose a weakly supervised method that can
segment individual cell regions who touch each other with unclear boundaries in
dense conditions without the training data for cell regions. We demonstrated
the efficacy of our method using several data-set including multiple cell types
captured by several types of microscopy. Our method achieved the highest
accuracy compared with several conventional methods. In addition, we
demonstrated that our method can perform without any annotation by using
fluorescence images that cell nuclear were stained as training data. Code is
publicly available in "https://github.com/naivete5656/WSISPDR".
