A neural lens for super-resolution biological imaging

Grant-Jacob, James; MacKay, Benita; Baker, James A.; Xie, Yunhui; Heath, Daniel J.; Loxham, Matthew; Eason, R.W.; Mills, B.

doi:10.1088/2399-6528/ab267d

Cited by 22 publications

(13 citation statements)

References 35 publications

(35 reference statements)

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“…As DNNs represent an emerging field with an everincreasing utility in image analytics, numerous current studies are focused on developing DNN models which seek to extend the current functionality of deconvolution algorithms for image processing. Notably, a similar trend has been observed in optical microscopical imaging, with some prominent research efforts in this respect being exemplified in [117], [118] and [119]. This is in addition to other recent in silico (albeit non-DNN) approaches (such as [120] and [121]) to achieve superresolution nanoscopy.…”

Section: Swish ( ) = + −supporting

confidence: 66%

Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy

et al. 2020

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The field of optical nanoscopy, a paradigm referring to the recent cutting-edge developments aimed at surpassing the widely acknowledged 200nm-diffraction limit in traditional optical microscopy, has gained recent prominence & traction in the 21 st century. Numerous optical implementations allowing for a new frontier in traditional confocal laser scanning fluorescence microscopy to be explored (termed super-resolution fluorescence microscopy) have been realized through the development of techniques such as stimulated emission and depletion (STED) microscopy, photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), amongst others. Nonetheless, it would be apt to mention at this juncture that optical nanoscopy has been explored since the mid-late 20 th century, through several computational techniques such as deblurring and deconvolution algorithms. In this review, we take a step back in the field, evaluating the various in silico methods used to achieve optical nanoscopy today, ranging from traditional deconvolution algorithms (such as the Nearest Neighbors algorithm) to the latest developments in the field of computational nanoscopy, founded on artificial intelligence (AI). An insight is provided into some of the commercial applications of AI-based super-resolution imaging, prior to delving into the potentially promising future implications of computational nanoscopy. This is facilitated by recent advancements in the field of AI, deep learning (DL) and convolutional neural network (CNN) architectures, coupled with the growing size of data sources and rapid improvements in computing hardware, such as multi-core CPUs & GPUs, low-latency RAM and hard-drive capacities.

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Section: Swish ( ) = + −supporting

confidence: 66%

Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy

et al. 2020

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“…The application of machine learning to support the use of a laser for sample observation includes feature detection in wood [212], surface roughness measurements [213], laser-based odometry [214] and laser-induced breakdown spectroscopy [215]. Machine learning has also repeatedly shown the capability to enhance computational imaging and microscopy [216][217][218][219][220][221][222]. In summary, machine learning is being applied to the entire spectrum of laser machining, from lasers to material interaction, imaging and sample analysis.…”

Section: Deep Learningmentioning

confidence: 99%

Lasers that learn: The interface of laser machining and machine learning

Mills

Grant-Jacob

2021

IET Optoelectronics

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Laser machining is a highly flexible non-contact fabrication method used extensively across academia and industry. Whilst simulations based on fundamental understanding offer some insight into the processes, both the highly non-linear interactions between laser light and matter and the variety of materials involved mean that theoretical modelling is not particularly applicable to practical experimentation. However, recent breakthroughs in machine learning have resulted in neural networks that are capable of accurate and rapid modelling of laser machining at a scale, speed, and precision well beyond those of existing theoretical approaches with applications including 3-D surface visualisation and real-time error correction. A perspective at the intersection of laser machining and machine learning is presented, followed by a discussion of future milestones and challenges for this field.10 steps each, that corresponds to a total of 10^5 experimental trials. Because of the often highly non-linear relationships amongst parameters, the standard practice is generally the systematic collection of laser-machining data for all parameter combinations to identify the optimal combination. However, this process is both time-consuming and unfocussed and can take days or weeks, hence costing unnecessary effort, time, and money. Even when the optimal parameters have been determined, small changes during manufacturing, for example in laser power or beam shape, can result in final product quality that is below the required standard, again with associated costs in time and money. What is needed, therefore, is a set of modelling methodologies for identifying optimal parameters and providing real-time monitoring and error correction during manufacturing.However, the processes that describe laser machining, such as the light-matter interaction, heat conduction, phase change of material, and material removal, are particularly complex and hence challenging to model precisely and directly from a theoretical standpoint. The exact details of these processes depend on many factors, including laser parameters (e.g. wavelength, fluence, and pulse length), associated diffraction effects, and sample properties (e.g. absorption, reflectivity, melting temperature, and ablation threshold). In addition, different interaction effects become dominant as the temporal interaction length varies from continuous wave to ultrafast (i.e. femtosecondThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Goodfellow et al 2014). GANs have been used extensively for a wide range of tasks such as creating faces (Karras et al 2019) domain-changing image-to-image translation (Grant-Jacob et al 2019;Isola et al 2017;Ledig et al 2017;Ronneberger et al 2015;Zhu et al 2017) and even video synthesis (Ma Fig. 8.…”

Section: Predictive Visualisation Of Laser Machiningmentioning

confidence: 99%

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

et al. 2021

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Interactions between light and matter during short-pulse laser materials processing are highly nonlinear, and hence acutely sensitive to laser parameters such as the pulse energy, repetition rate, and number of pulses used. Due to this complexity, simulation approaches based on calculation of the underlying physical principles can often only provide a qualitative understanding of the inter-relationships between these parameters. An alternative approach such as parameter optimisation, often requires a systematic and hence time-consuming experimental exploration over the available parameter space. Here, we apply neural networks for parameter optimisation and for predictive visualisation of expected outcomes in laser surface texturing with blind vias for tribology control applications. Critically, this method greatly reduces the amount of experimental laser machining data that is needed and associated development time, without negatively impacting accuracy or performance. The techniques presented here could be applied in a wide range of fields and have the potential to significantly reduce the time, and the costs associated with laser process optimisation.

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A neural lens for super-resolution biological imaging

Cited by 22 publications

References 35 publications

Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy

Smart Nanoscopy: A Review of Computational Approaches to Achieve Super-Resolved Optical Microscopy

Lasers that learn: The interface of laser machining and machine learning

Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining

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