  The advancement of the neuroscientific imaging techniques has produced an
unprecedented size of neural cell imaging data, which calls for automated
processing. In particular, identification of cells from two photon images
demands segmentation of neural cells out of various materials and
classification of the segmented cells according to their cell types. To
automatically segment neural cells, we used U-Net model, followed by
classification of excitatory and inhibitory neurons and glia cells using a
transfer learning technique. For transfer learning, we tested three public
models of resnet18, resnet50 and inceptionv3, after replacing the fully
connected layer with that for three classes. The best classification
performance was found for the model with inceptionv3. The proposed application
of deep learning technique is expected to provide a critical way to cell
identification in the era of big neuroscience data.
