Automated identification of neural cells in the multi-photon images using deep-neural networks
This provides an automated solution for neuroscientists to process large-scale imaging data, though it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackled the problem of automatically identifying neural cells in two-photon images by segmenting them with U-Net and classifying cell types using transfer learning with models like ResNet and InceptionV3, achieving the best performance with InceptionV3.
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.