  Tracking cells in 3D at high speed continues to attract extensive attention
for many biomedical applications, such as monitoring immune cell migration and
observing tumor metastasis in flowing blood vessels. Here, we propose a deep
convolutional neural networks (CNNs) based method to retrieve the 3D locations
of the fluorophores from a single 2D image captured by a conventional
wide-field fluorescence microscope without any hardware modification. The
reported method converts the challenging 3D localization from an ill-posed
model-based fitting problem, especially with dense samples and low
signal-to-noise ratio, to a solvable multi-label classification problem through
two cascaded CNNs, where deep learning technique has a great advantage over
other algorithms. Compared with traditional kernel-fitting methods, the
proposed method achieves more accurate and robust localization of multiple
objects across a much larger axial range, which is validated by both simulation
and experimental results on 3D distributed fluorescent beads. Moreover, in vivo
3D tracking of multiple blood cells in zebrafish at 100 fps further verifies
the feasibility of our framework.
