Quantitative differential phase contrast (DPC) microscopy with computational aberration correction

Chen, Michael; Phillips, Zachary F.; Waller, Laura

doi:10.1364/oe.26.032888

Cited by 44 publications

(23 citation statements)

References 34 publications

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“…This provides us with great flexibility and allows phase reconstruction from measurements that are not fully characterized. In designing self-calibrating algorithms, the need for regularization (i.e., prior models for phase) is emphasized [17] since one simultaneously decouples the individual contributions of phase and aberrations to the measured images. However, typical regularization techniques are hand-crafted and require manual tuning of parameters even after the model is constructed.…”

Section: Introductionmentioning

confidence: 99%

Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network

Bostan

Heckel

Chen

et al. 2020

Optica

Self Cite

125

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Deep neural networks have emerged as effective tools for computational imaging including quantitative phase microscopy of transparent samples. To reconstruct phase from intensity, current approaches rely on supervised learning with training examples; consequently, their performance is sensitive to a match of training and imaging settings. Here we propose a new approach to phase microscopy by using an untrained deep neural network for measurement formation, encapsulating the image prior and imaging physics. Our approach does not require any training data and simultaneously reconstructs the sought phase and pupil-plane aberrations by fitting the weights of the network to the captured images. To demonstrate experimentally, we reconstruct quantitative phase from through-focus images blindly (i.e. no explicit knowledge of the aberrations).

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Section: Introductionmentioning

confidence: 99%

Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network

Bostan

Heckel

Chen

et al. 2020

Optica

Self Cite

125

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show abstract

“…This generates a final image that shares properties with a large class of differential phase contrast illumination techniques [43], which capture and subtract two images under angled illumination. There are a large variety of differential phase contrast techniques that use more than two captured images [44][45][46][47], which are, as noted above, challenging to directly compare to this limited learned sensing network. Second, the PC Ring illumination scheme consists of using a ring of 20 LEDs within the dark-field channel to illuminate and capture one image, and a set of 8 LEDs within the bright field channel to illuminate and capture a second image, and subtracts the two images to create phase contrast.…”

Section: Experimental Results Thin Smearmentioning

confidence: 99%

Learned sensing: jointly optimized microscope hardware for accurate image classification

Muthumbi

Chaware

Kim

et al. 2019

Biomed. Opt. Express

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Since its invention, the microscope has been optimized for interpretation by a human observer. With the recent development of deep learning algorithms for automated image analysis, there is now a clear need to redesign the microscope's hardware for specific interpretation tasks. To increase the speed and accuracy of automated image classification, this work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network. By adding a "physical layer" to a deep classification network, we are able to jointly optimize for specific illumination patterns that highlight the most important sample features for the particular learning task at hand, which may not be obvious under standard illumination. We demonstrate how our learned sensing approach for illumination design can automatically identify malaria-infected cells with up to 5-10% greater accuracy than standard and alternative microscope lighting designs. We show that this joint hardware-software design procedure generalizes to offer accurate diagnoses for two different blood smear types, and experimentally show how our new procedure can translate across different experimental setups while maintaining high accuracy.

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“…We show that the unknown defocus distance can be blindly estimated by minimizing the FPM reconstruction residual with the linear search algorithm. This additional step is particularly important for accurate phase image recovery, as the embedded pupil recovery algorithm alone is less likely to converge to a global minimum 13 when the defocus distance is beyond the depth of focus (10 μm), at which the phase difference in the wavefront error can exceed 2 π . With the two-stage computational refocusing method, the cell images are successfully brought back to focus.…”

Section: Resultsmentioning

confidence: 99%

Parallel Fourier ptychographic microscopy for high-throughput screening with 96 cameras (96 Eyes)

Chan

Kim

Pan

et al. 2019

Sci Rep

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We report the implementation of a parallel microscopy system (96 Eyes) that is capable of simultaneous imaging of all wells on a 96-well plate. The optical system consists of 96 microscopy units, where each unit is made out of a four element objective, made through a molded injection process, and a low cost CMOS camera chip. By illuminating the sample with angle varying light and applying Fourier Ptychography, we can improve the effective brightfield imaging numerical aperture of the objectives from 0.23 to 0.3, and extend the depth of field from ±5 μm to ±15 μm. The use of Fourier Ptychography additionally allows us to computationally correct the objectives’ aberrations out of the rendered images, and provides us with the ability to render phase images. The 96 Eyes acquires raw data at a rate of 0.7 frame per second (all wells) and the data are processed with 4 cores of graphical processing units (GPUs; GK210, Nvidia Tesla K80, USA). The system is also capable of fluorescence imaging (excitation = 465 nm, emission = 510 nm) at the native resolution of the objectives. We demonstrate the capability of this system by imaging S1P 1 -eGFP-Human bone osteosarcoma epithelial (U2OS) cells.

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Quantitative differential phase contrast (DPC) microscopy with computational aberration correction

Cited by 44 publications

References 34 publications

Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network

Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network

Learned sensing: jointly optimized microscope hardware for accurate image classification

Parallel Fourier ptychographic microscopy for high-throughput screening with 96 cameras (96 Eyes)

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