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11-16 April 2026
Conference 13006 > Paper 13006-40
Paper 13006-40

Learning-based optical diffraction tomography for label-free 3D cell imaging

10 April 2024 • 11:30 - 11:50 CEST | Etoile B, Niveau/Level 1

Abstract

Optical diffraction tomography (ODT) is a powerful 3D imaging technique with immense potential in fields like cancer diagnosis and drug treatment. However, traditional ODT systems face limitations like the "missing cone" problem, affecting 3D resolution and cancer classification. To address this, fiber-optic dual-beam technology employs controlled laser beams for stable cell rotation, improving tomographic imaging. This improvement is further enhanced by a novel tomographic workflow that incorporates optical flow and deep learning, replacing manual interventions with automated processes. This novel method is validated by reconstructing 3D images of simulated cell phantoms, HL60 human cancer cells, and artificial cell phantoms. Its adaptability extends to diverse imaging techniques, promising advancements in cell biology, innovative therapeutics, and enhanced early-stage cancer diagnostics.

Presenter

Bin Yang
TU Dresden (Germany)
Mr. BinYang is a student of the Competence Center for Biomedical Computational Laser Systems (BIOLAS) and Laboratory of Measurement and Sensor System Technique (MST) at TU Dresden, Germany. He is a co-author of the paper titled 'AI-driven Projection Tomography with Multicore Fiber-Optic Cell Rotation' which has already been pre-accepted by Nature Communications.
Application tracks: AI/ML
Presenter/Author
Bin Yang
TU Dresden (Germany)
Author
TU Dresden (Germany)
Author
TU Dresden (Germany)
Author
TU Dresden (Germany)