Bioimage Informatics for Big Data

Peng, Hanchuan; Zhou, Jie; Zhou, Zhi; Bria, Alessandro; Li, Yujie; Kleissas, Dean; Drenkow, Nathan; Long, Brian; Liu, Huan; Chen, Hanbo

doi:10.1007/978-3-319-28549-8_10

Cited by 9 publications

(5 citation statements)

References 44 publications

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“…The purpose of our review is to guide the reader into this multidisciplinary research, offering the tools to choose the techniques that are best suited for a specific application. For detailed recapitulation of the single topics, we refer to excellent reviews that have been published in the last years (Mayford and Reijmers, 2016;Peng et al, 2016;Sbalzarini, 2016;Tainaka et al, 2016;DeNardo and Luo, 2017;Power and Huisken, 2017;He et al, 2019;Ueda et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Dissecting Neuronal Activation on a Brain-Wide Scale With Immediate Early Genes

Franceschini¹,

Costantini

Pavone

et al. 2020

Front. Neurosci.

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Visualizing neuronal activation on a brain-wide scale yet with cellular resolution is a fundamental technical challenge for neuroscience. This would enable analyzing how different neuronal circuits are disrupted in pathology and how they could be rescued by pharmacological treatments. Although this goal would have appeared visionary a decade ago, recent technological advances make it eventually feasible. Here, we review the latest developments in the fields of genetics, sample preparation, imaging, and image analysis that could be combined to afford whole-brain cell-resolution activation mapping. We show how the different biochemical and optical methods have been coupled to study neuronal circuits at different spatial and temporal scales, and with celltype specificity. The inventory of techniques presented here could be useful to find the tools best suited for a specific experiment. We envision that in the next years, mapping of neuronal activation could become routine in many laboratories, allowing dissecting the neuronal counterpart of behavior.

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Section: Introductionmentioning

confidence: 99%

Dissecting Neuronal Activation on a Brain-Wide Scale With Immediate Early Genes

Franceschini¹,

Costantini

Pavone

et al. 2020

Front. Neurosci.

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show abstract

“…Of all biomedical imaging fields, bioimaging arguably faces the biggest challenges in automating visual image interpretation tasks, due to the lack of standard imaging protocols, the high variability of experimental conditions, and the sheer volume of the data produced. Whereas (pre)clinical medical imaging systems typically generate data sets of dozens of megabytes (MB), and digital pathology scanners yield data sets of tens to hundreds of gigabytes (GB), automated microscopes may easily produce on the order of terabytes (TB) of image data in a single experiment [171] , [172] , [173] , [174] . Here, the power of deep learning is increasingly leveraged not only to improve image formation [175] , [176] , [177] , [178] , [179] , but also subsequent image analysis, discussed next.…”

Section: Deep Learning In Biomedical Imagingmentioning

confidence: 99%

A bird’s-eye view of deep learning in bioimage analysis

Meijering

2020

Computational and Structural Biotechnology Journal

115

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Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird’s-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.

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“…It is merely impossible for humans to analyze the data quickly and comprehensively without the help of computers. Therefore, integration of AI in these computers will enable them to analyze these data sets quickly and comprehensively and predict clinical conditions (39)(40). More importantly, as computers don't forget what they have learned and they don't have inherent biases, they are more likely to produce objective diagnoses which is key in advanced medical care (2).…”

Section: Application Of Ai In Healthcarementioning

confidence: 99%

The Future of Health and Healthcare in a World of Artificial Intelligence

Kar

Dash²

2018

ABEB

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Artificial Intelligence (AI) consists a set of computational technologies that designed to sense, learn, reason, and take action. AI has already been integrated in many applications including automating the business processes, gaining insight through data analysis, and engaging with customers and employees. AI enabled machines using deep learning algorithms has been shown to exceed human performance, particularly in visual tasks like playing Atari games, strategic board games like Go and object recognition etc. Furthermore, with the technological advancement in mobile computing, artificial neural networks, robotics, storage of huge data in internet, cloud-based machine learning and information processing algorithms etc. application of AI has been integrated in many sectors including transportation, service robots, health care, education, low-resource communities, public safety and security, employment and workplace, and entertainment etc. In this review, we have highlighted the recent trends and applications of AI in healthcare i.e. mining medical records (EHR), designing treatment plans, robotics mediated surgeries, medical management and supporting hospital operations etc. There are many AI platform has been designed which has also been proved to be instrumental in the development of innovative drugs. There is no doubt that AI will enhance the cooperation between humans and machines in years to come and will play an integral role in the improvement of the health index and quality of life.This work is licensed under Creative Commons Attribution 4.0 License ABEB.MS.ID.000503.

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Bioimage Informatics for Big Data

Cited by 9 publications

References 44 publications

Dissecting Neuronal Activation on a Brain-Wide Scale With Immediate Early Genes

Dissecting Neuronal Activation on a Brain-Wide Scale With Immediate Early Genes

A bird’s-eye view of deep learning in bioimage analysis

The Future of Health and Healthcare in a World of Artificial Intelligence

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