20 September 2022
Neural network speeds holographic image reconstruction for biological samples
Generalizable deep learning approach holds promise for biomedical imaging and research
ROCHESTER - Researchers have developed a new end-to-end neural network that can speed up the reconstruction of holographic images. Unlike other deep learning techniques, the approach can be used on samples not encountered during training, making it particularly useful for label-free holographic biomedical imaging.
“With this framework, a well-trained neural network can be distributed anywhere, without finetuning, and perform rapid and high-quality holographic imaging of various samples,” explained research lead Hanlong Chen, University of California, Los Angeles (UCLA).
Hanlong Chen and Aydogan Ozcan will present the research at the Frontiers in Optics + Laser Science Conference (FiO LS) meeting being held in Rochester, New York and online 17 – 20 October 2022. The presentation is scheduled for Monday, 17 October at 16:30 EDT (UTC – 04:00).
A generalizable approach
Although various neural networks have been developed to achieve the data-heavy task of hologram reconstruction for biological research and biomedical applications, most of them are designed to be very specific. This means that they may not perform well if used with samples that differ from those initially used to train the network.
To solve this problem, Chen and colleagues developed an end-to-end neural network called Fourier Imager Network (FIN). This type of neural network is trained using a single model, bypassing some of the steps usually used by other deep learning methods. End-to-end neural networks are also faster and potentially more generalizable to a wide variety of samples.
Faster, more accurate results
The FIN framework takes a sequence of intensity-only raw holograms captured at different sample-to-sensor distances with a lens-free in-line holographic microscope and creates reconstructed images of the samples. To test the new approach, the researchers trained the network using lung tissue sections. They then used FIN to reconstruct holographic images of human salivary gland tissue and Pap smear samples not seen by the network during training.
FIN worked well on these new types of samples and delivered more accurately reconstructed images than an iterative algorithm and a state-of-the-art deep learning model. It also showed an approximately 50-fold improved speed compared to the deep learning model. The researchers say that these results demonstrate the strong external generalization of FIN while also showing the immense potential of building broadly generalizable deep neural networks for various microscopy and computational imaging tasks.
Chen added, “Our next step is to investigate autofocusing while retaining the benefits of our approach, such as superb image quality, unprecedented generalization to new types of samples, and enhanced computational speed, making holographic imaging possible with low-resource devices.”
About Frontiers in Optics + Laser Science
Frontiers in Optics, the annual meeting for Optica (formerly OSA) is presented with Laser Science, the annual meeting of the American Physical Society, Division of Laser Science. The two meetings unite communities from both societies for comprehensive and current research in a diverse collection of optics and photonics topics and across the disciplines of physics, biology and chemistry. The 2023 FiO LS Conference will be held as an in-person event featuring hundreds of live contributed and invited talks with additional on-demand content available for online viewing. Media registration is free with credential. Digital assets are available as requested.
About Optica
Optica (formerly OSA), Advancing Optics and Photonics Worldwide, is the society dedicated to promoting the generation, application, archiving and dissemination of knowledge in the field. Founded in 1916, it is the leading organization for scientists, engineers, business professionals, students and others interested in the science of light. Optica’s renowned publications, meetings, online resources and in-person activities fuel discoveries, shape real-life applications and accelerate scientific, technical and educational achievement. Discover more at: Optica.org
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