@inproceedings{95bfeb800b2e46d79ffc5649ac930ae6,
title = "Deep Learning-Enabled Holographic Tomography: Cell Morphology Analysis and Diagnosis",
abstract = "The work describes deep learning-enabled holographic tomography for neuroblastoma cell processing, analysis, and diagnosis through three-dimensional (3-D) cell refractive index (RI) model. Deep learning-assisted approach is applied to execute effective segmentation of 3-D RI cell morphology for the different cellular states under normal, autophagy, and apoptosis. The biophysical parameters of 3-D RI cell morphology are analyzed and selected for learning-based classification to identify cell death pathways. The results show that the proposed approach achieve of 98% in identifying cell morphology through optimized biophysical parameters.",
keywords = "Biophysical parameters, Cell classification, Cell segmentation, Holographic tomography",
author = "Cheng, {Chau Jern} and Huang, {Chung Hsuan} and Chi, {Han Wen} and Chang, {Hui Ching} and Tu, {Han Yen}",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI 2023 ; Conference date: 26-06-2023 Through 29-06-2023",
year = "2023",
doi = "10.1117/12.2673911",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Pietro Ferraro and Demetri Psaltis and Simonetta Grilli",
booktitle = "Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI",
}