2019/09/25 by Si-Baek Seong, Seong, Si-Baek, Hae‐Jeong Park +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Neuroscience · #Cell Image Analysis Techniques #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Neuroscience and Neuropharmacology Research #Systems and Control (eess.SY) #cs.LG #cs.SY #eess.IV #eess.SY #electronic engineering #information engineering #q-bio.NC
paper · pdf · doi:10.48550/arxiv.1909.11269
8 pages, 4 figures and 2 tables
arxiv created 2019/09/25 · openalex publication_date 2019/09/25 · arxiv updated 2019/09/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.