Deep learning for single-shot autofocus microscopy

Pinkard, Henry; Phillips, Zachary F.; Babakhani, Arman; Fletcher, Daniel A.; Waller, Laura

doi:10.1364/optica.6.000794

Cited by 119 publications

(76 citation statements)

References 21 publications

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“…Utilizing the dependence of focus measure on z position, we implemented contrast-based auto-focus. Alternatively, with small modifications in illumination, different single-shot focus-finding approaches [34, 35] can be used for faster focus.…”

Section: Resultsmentioning

confidence: 99%

“…The diffuser is placed at the focal plane of the condenser to make the illumination Khler-like. Dark-field illumination for low magnification can be provided simply by a ring of LED, while an LED matrix can be used for quantitative phase imaging, fourier ptychography [15, 36] and single-shot auto-focus [34, 35]. For fluorescence excitation, we make use of oblique angle laser illumination [9].…”

Section: Resultsmentioning

confidence: 99%

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Octopi: Open configurable high-throughput imaging platform for infectious disease diagnosis in the field

Soto-Montoya

Voisin

et al. 2019

Preprint

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Access to quantitative, robust, yet affordable diagnostic tools is necessary to reduce global infectious disease burden. Manual microscopy has served as a bedrock for diagnostics with wide adaptability, although at a cost of tedious labor and human errors. Automated robotic microscopes are poised to enable a new era of smart field microscopy but current platforms remain cost prohibitive and largely inflexible, especially for resource poor and field settings. Here we present Octopi, a low-cost ($250-$500) and reconfigurable autonomous microscopy platform capable of automated slide scanning and correlated bright-field and fluorescence imaging. Being highly modular, it also provides a framework for new disease-specific modules to be developed.We demonstrate the power of the platform by applying it to automated detection of malaria parasites in blood smears. Specifically, we discovered a spectral shift on the order of 10 nm for DAPI-stained Plasmodium falciparum malaria parasites. This shift allowed us to detect the parasites with a low magnification (equivalent to 10x) large field of view (2.56 mm 2 ) module.Combined with automated slide scanning, real time computer vision and machine learningbased classification, Octopi is able to screen more than 1.5 million red blood cells per minute for parasitemia quantification, with estimated diagnostic sensitivity and specificity exceeding 90% at parasitemia of 50/ul and 100% for parasitemia higher than 150/l. With different modules, we further showed imaging of tissue slice and sputum sample on the platform. With roughly two orders of magnitude in cost reduction, Octopi opens up the possibility of a large robotic microscope network for improved disease diagnosis while providing an avenue for collective efforts for development of modular instruments.One sentence summary: We developed a low-cost ($250-$500) automated imaging platform that can quantify malaria parasitemia by scanning 1.5 million red blood cells per minute.Lack of cost-effective diagnostics is a major hurdle in global fight against infectious disease, 2 specially in resource poor settings [1]. This leaves our world in a highly vulnerable position 3 with therapeutic drugs being either overused, leading to drug resistant strains or not acces-4 sible to people who actually need these treatments [2]. Since health care is delivered around 5 the world in a tiered structure, local context such as high cost, lack of trained personal or 6 low throughput of many available diagnostics tests plays a large detrimental role on quality 7 of delivered health-care [3]. 8 Because of the versatility and wide adoption of manual microscopy [4] and its role in 9 direct visual identification of parasites [5], it remains a WHO gold standard for numerous 10 diseases [1]. Despite technological advancements in related fields, the practice of conventional 11 manual microscopy has remained largely unchanged over the last half century and suffers 12 from several drawbacks. With an average lab technician spending 6 to 8 hours imaging and 13 ...

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Octopi: Open configurable high-throughput imaging platform for infectious disease diagnosis in the field

Soto-Montoya

Voisin

et al. 2019

Preprint

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“…Supervised learning based on convolutional nets (Box 1) has enhanced video microscopy by improving its spatial resolution 39 , extending its depth of field 40 and retaining objects in focus 41 . More generally, detecting and following microscopic active particles in video-microscopy recordings poses enormous challenges, especially for heterogeneous samples with multiple species, varying contrast and low signal-to-noise ratio.…”

Section: Machine Learning For Active Mattermentioning

confidence: 99%

Machine learning for active matter

et al. 2020

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“…Because the depth-of-field (DOF) was relatively large (8.5 µm) compared to our sample, we were able to level the sample manually prior to acquisition using adjustment screws on the motion stage, to ensure the sample remained in focus across an approximately 20mm movement range. For larger areas or shallower DOF, we anticipate a more sophisticated leveling technique will be necessary, such as active autofocus [40,41] or illumination-based autofocusing [42].…”

Section: Methodsmentioning

confidence: 99%

High-throughput fluorescence microscopy using multi-frame motion deblurring

Phillips

Dean

Recht

et al. 2019

Biomed. Opt. Express

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We demonstrate multi-frame motion deblurring for gigapixel wide-field fluorescence microscopy using fast slide scanning with coded illumination. Our method illuminates the sample with multiple pulses within each exposure, in order to introduce structured motion blur. By deconvolving this known motion sequence from the set of acquired measurements, we recover the object with up to 10× higher SNR than when illuminated with a single pulse (strobed illumination), while performing acquisition at 5× higher frame-rate than a comparable stop-and-stare method. Our coded illumination sequence is optimized to maximize the reconstruction SNR. We also derive a framework for determining when coded illumination is SNR-optimal in terms of system parameters such as source illuminance, noise, and motion stage specifications. This helps system designers to choose the ideal technique for high-throughput microscopy of very large samples.

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Deep learning for single-shot autofocus microscopy

Cited by 119 publications

References 21 publications

Octopi: Open configurable high-throughput imaging platform for infectious disease diagnosis in the field

Octopi: Open configurable high-throughput imaging platform for infectious disease diagnosis in the field

Machine learning for active matter

High-throughput fluorescence microscopy using multi-frame motion deblurring

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