Open-access database for digital lensless holographic microscopy and its application on the improvement of deep-learning-based autofocusing models

• Published in: Applied optics. Optical technology and biomedical optics Vol. 63; no. 7; p. B49
• Main Authors: Buitrago-Duque, Carlos, Tobón-Maya, Heberley, Gómez-Ramírez, Alejandra, Zapata-Valencia, Samuel I, Lopera, Maria J, Trujillo, Carlos, Garcia-Sucerquia, Jorge
• Format: Journal Article
• Language: English
• Published: United States 01.03.2024
• ISSN: 2155-3165
• DOI: 10.1364/AO.507412

Among modern optical microscopy techniques, digital lensless holographic microscopy (DLHM) is one of the simplest label-free coherent imaging approaches. However, the hardware simplicity provided by the lensless configuration is often offset by the demanding computational postprocessing required to match the retrieved sample information to the user's expectations. A promising avenue to simplify this stage is the integration of artificial intelligence and machine learning (ML) solutions into the DLHM workflow. The biggest challenge to do so is the preparation of an extensive and high-quality experimental dataset of curated DLHM recordings to train ML models. In this work, a diverse, open-access dataset of DLHM recordings is presented as support for future research, contributing to the data needs of the applied research community. The database comprises 11,760 experimental DLHM holograms of bio and non-bio samples with diversity on the main recording parameters of the DLHM architecture. The database is divided into two datasets of 10 independent imaged samples. The first group, named multi-wavelength dataset, includes 8160 holograms and was recorded using laser diodes emitting at 654 nm, 510 nm, and 405 nm; the second group, named single-wavelength dataset, is composed of 3600 recordings and was acquired using a 633 nm He-Ne laser. All the experimental parameters related to the dataset acquisition, preparation, and calibration are described in this paper. The advantages of this large dataset are validated by re-training an existing autofocusing model for DLHM and as the training set for a simpler architecture that achieves comparable performance, proving its feasibility for improving existing ML-based models and the development of new ones.