  We compare a set of convolutional neural network (CNN) architectures for the
task of segmenting and detecting human sperm cells in an image taken from a
semen sample. In contrast to previous work, samples are not stained or washed
to allow for full sperm quality analysis, making analysis harder due to
clutter. Our results indicate that training on full images is superior to
training on patches when class-skew is properly handled. Full image training
including up-sampling during training proves to be beneficial in deep CNNs for
pixel wise accuracy and detection performance. Predicted sperm cells are found
by using connected components on the CNN predictions. We investigate
optimization of a threshold parameter on the size of detected components. Our
best network achieves 93.87% precision and 91.89% recall on our test dataset
after thresholding outperforming a classical mage analysis approach.
