Deep learning-enabled holographic tomography: cell morphology analysis and diagnosis

Chau-Jern Cheng; Chung-Hsuan Huang; Han-Wen Chi; Hui-Ching Chang; Han-Yen Tu

doi:10.1117/12.2673911

11 August 2023 Deep learning-enabled holographic tomography: cell morphology analysis and diagnosis

Chau-Jern Cheng, Chung-Hsuan Huang, Han-Wen Chi, Hui-Ching Chang, Han-Yen Tu

Proceedings Volume 12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI; 1262202 (2023) https://doi.org/10.1117/12.2673911
Event: SPIE Optical Metrology, 2023, Munich, Germany

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.

Conference Presentation

Citation Download Citation

Chau-Jern Cheng, Chung-Hsuan Huang, Han-Wen Chi, Hui-Ching Chang, and Han-Yen Tu "Deep learning-enabled holographic tomography: cell morphology analysis and diagnosis", Proc. SPIE 12622, Optical Methods for Inspection, Characterization, and Imaging of Biomaterials VI, 1262202 (11 August 2023); https://doi.org/10.1117/12.2673911

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