  We propose a 3D convolutional neural network to simultaneously segment and
detect cell nuclei in confocal microscopy images. Mirroring the co-dependency
of these tasks, our proposed model consists of two serial components: the first
part computes a segmentation of cell bodies, while the second module identifies
the centers of these cells. Our model is trained end-to-end from scratch on a
mouse parotid salivary gland stem cell nuclei dataset comprising 107 image
stacks from three independent cell preparations, each containing several
hundred individual cell nuclei in 3D. In our experiments, we conduct a thorough
evaluation of both detection accuracy and segmentation quality, on two
different datasets. The results show that the proposed method provides
significantly improved detection and segmentation accuracy compared to
state-of-the-art and benchmark algorithms. Finally, we use a previously
described test-time drop-out strategy to obtain uncertainty estimates on our
predictions and validate these estimates by demonstrating that they are
strongly correlated with accuracy.
