  In recent years, an enormous amount of fluorescence microscopy images were
collected in high-throughput lab settings. Analyzing and extracting relevant
information from all images in a short time is almost impossible. Detecting
tiny individual cell compartments is one of many challenges faced by
biologists. This paper aims at solving this problem by building an end-to-end
process that employs methods from the deep learning field to automatically
segment, detect and classify cell compartments of fluorescence microscopy
images of yeast cells. With this intention we used Mask R-CNN to automatically
segment and label a large amount of yeast cell data, and YOLOv4 to
automatically detect and classify individual yeast cell compartments from these
images. This fully automated end-to-end process is intended to be integrated
into an interactive e-Science server in the PerICo1 project, which can be used
by biologists with minimized human effort in training and operation to complete
their various classification tasks. In addition, we evaluated the detection and
classification performance of state-of-the-art YOLOv4 on data from the
NOP1pr-GFP-SWAT yeast-cell data library. Experimental results show that by
dividing original images into 4 quadrants YOLOv4 outputs good detection and
classification results with an F1-score of 98% in terms of accuracy and speed,
which is optimally suited for the native resolution of the microscope and
current GPU memory sizes. Although the application domain is optical microscopy
in yeast cells, the method is also applicable to multiple-cell images in
medical applications
