4 research outputs found
From Hours to Seconds: Towards 100x Faster Quantitative Phase Imaging via Differentiable Microscopy
With applications ranging from metabolomics to histopathology, quantitative
phase microscopy (QPM) is a powerful label-free imaging modality. Despite
significant advances in fast multiplexed imaging sensors and
deep-learning-based inverse solvers, the throughput of QPM is currently limited
by the speed of electronic hardware. Complementarily, to improve throughput
further, here we propose to acquire images in a compressed form such that more
information can be transferred beyond the existing electronic hardware
bottleneck. To this end, we present a learnable optical
compression-decompression framework that learns content-specific features. The
proposed differentiable quantitative phase microscopy () first
uses learnable optical feature extractors as image compressors. The intensity
representation produced by these networks is then captured by the imaging
sensor. Finally, a reconstruction network running on electronic hardware
decompresses the QPM images. In numerical experiments, the proposed system
achieves compression of 64 while maintaining the SSIM of
and PSNR of dB on cells. The results demonstrated by our experiments
open up a new pathway for achieving end-to-end optimized (i.e., optics and
electronic) compact QPM systems that may provide unprecedented throughput
improvements
Differentiable Microscopy Designs an All Optical Quantitative Phase Microscope
Ever since the first microscope by Zacharias Janssen in the late 16th
century, scientists have been inventing new types of microscopes for various
tasks. Inventing a novel architecture demands years, if not decades, worth of
scientific experience and creativity. In this work, we introduce Differentiable
Microscopy (), a deep learning-based design paradigm, to aid
scientists design new interpretable microscope architectures. Differentiable
microscopy first models a common physics-based optical system however with
trainable optical elements at key locations on the optical path. Using
pre-acquired data, we then train the model end-to-end for a task of interest.
The learnt design proposal can then be simplified by interpreting the learnt
optical elements. As a first demonstration, based on the optical 4- system,
we present an all-optical quantitative phase microscope (QPM) design that
requires no computational post-reconstruction. A follow-up literature survey
suggested that the learnt architecture is similar to the generalized phase
contrast method developed two decades ago. Our extensive experiments on
multiple datasets that include biological samples show that our learnt
all-optical QPM designs consistently outperform existing methods. We
experimentally verify the functionality of the optical 4- system based QPM
design using a spatial light modulator. Furthermore, we also demonstrate that
similar results can be achieved by an uninterpretable learning based method,
namely diffractive deep neural networks (D2NN). The proposed differentiable
microscopy framework supplements the creative process of designing new optical
systems and would perhaps lead to unconventional but better optical designs