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DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction
[article]
2020
arXiv
pre-print
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The massive size of high-throughput microscopy data necessitates fast and largely unsupervised algorithms. ...
Densely packed cells combined with interconnected microvascular networks are a challenge for current segmentation algorithms. ...
Acknowledgment This work was funded in part by the National Institutes of Health / National Library of Medicine #4 R00 LM011390-02 and the Cancer Prevention and Research Institute of Texas (CPRIT) #RR140013 ...
arXiv:2002.01568v1
fatcat:4jswc6kbizd7nezxoceg5ujfke
Deep Learning in Image Cytometry: A Review
2018
Cytometry Part A
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Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. ...
on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. ...
Ewert Bengtsson, and Petter Ranefall for their appreciative suggestions.
LITERATURE CITED ...
doi:10.1002/cyto.a.23701
pmid:30565841
pmcid:PMC6590257
fatcat:dszbcsfncrhxnazsxopjkbe3ju
Biosensors and Machine Learning for Enhanced Detection, Stratification, and Classification of Cells: A Review
[article]
2021
arXiv
pre-print
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Sensors focusing on the detection and stratification of cells have gained popularity as technological advancements have allowed for the miniaturization of various components inching us closer to Point-of-Care ...
Understanding how they function and differentiating cells from one another therefore is of paramount importance for disease diagnostics as well as therapeutics. ...
Convolutional Neural Networks A specific form of ANNs is called Convolutional Neural Nets (CNNs). ...
arXiv:2101.01866v1
fatcat:rws7k3yp6ndmnlkqcvafmkgphi
Abstracts from USCAP 2020: Informatics (1522-1590)
2020
Laboratory Investigation
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Area was used instead of cells for (2) due to challenges in CD138-cell segmentation. ...
Two methods to calculate PC% were used: 1) # CD138+ cells on IHC/# nucleated cells on H&E, and 2) total area of CD138+ cells/total area of CD138+ and CD138 negative cells on IHC. (1) is the most direct ...
DIA can provide an efficient and standardized approach for quantifying stromal CD8+ TIL density. ...
doi:10.1038/s41374-020-0393-8
pmid:32139865
fatcat:wecibsafmzhzjjgxqvxalr3xn4
Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology
2020
Frontiers in Pharmacology
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The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). ...
We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from "big" data. ...
al., 2019)
7
MR images
DL Faster region-based convolutional neural networks (Faster R-CNN)
Metastatic
lymph nodes
(Lu Y. et al., 2018)
8
Dermoscopic
images
DL Convolutional neural networks ...
doi:10.3389/fphar.2020.01177
pmid:32903628
pmcid:PMC7438594
fatcat:u7mdynhnwfazbn6jhvcagorp2a
Local Shape Descriptors for Neuron Segmentation
[article]
2021
bioRxiv
pre-print
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Implementations of the new auxiliary learning task, network architectures, training, prediction, and evaluation code, as well as the datasets used in this study are publicly available as a benchmark for ...
Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (Flood-Filling Networks, FFN), while being two orders ...
Throughput In addition to being accurate, it is important for neuron segmentation methods to be fast and computationally inexpensive. ...
doi:10.1101/2021.01.18.427039
fatcat:2knyphv66rftzn2ptpxyrogx2m
Quantitative imaging of lipid droplets in single cells
2019
The Analyst
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Non-destructive spatial characterization of lipid droplets using coherent Raman scattering microscopy and computational image analysis algorithms at the single-cell level. ...
Finally, they demonstrated the implementation of this approach for automated segmentation of cells and nuclei in brain tumor samples. ...
This has been driven in large part by the success of convolutional neural networks (CNN), and their rapid development in the past decade. ...
doi:10.1039/c8an01525b
pmid:30357117
pmcid:PMC6375708
fatcat:jah3zcv2fnftfd5hz645lnyuqa
Optical aspects of a miniature fluorescence microscope for super-sensitive biomedical detection
2020
Zenodo
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We present optical design and the principle demonstrator of a miniature fluorescence microscope aiming for super-sensitive detection. ...
Current commercial fluorescence microscopes are typically sophisticated, bulky and expensive, not suitable for low-volume or clinic routine biomedical detection. ...
JTh2A.33 Mapping neural correlates to language and biological motion in school-age children with autism using high-density diffuse optical tomography, Alexandra M. ...
doi:10.5281/zenodo.3822435
fatcat:3eoome22a5grbmarbrktphfh7a
28th Annual Computational Neuroscience Meeting: CNS*2019
2019
BMC Neuroscience
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Bio-inspired central pattern generators have been widely used to control robot locomotion, see for review [1]. ...
Activity from both the LP and PD neurons of the stomatogastric ganglion of a crab was recorded using intracellular electrodes and sent to a computer through a DAQ device. ...
Deep convolutional spiking neural networks (DCSNNs) are the next generation of neural networks that are hardware-friendly and energyefficient. ...
doi:10.1186/s12868-019-0538-0
fatcat:3pt5qvsh45awzbpwhqwbzrg4su
27th Annual Computational Neuroscience Meeting (CNS*2018): Part One
2018
BMC Neuroscience
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Networking Fund of the Helmholtz Association and the Helmholtz Portfolio theme "Supercomputing and Modeling for the Human Brain" and the European Union Seventh Framework Programme (FP7/2007-2013) under ...
This research was supported by the Spanish Government projects TIN2014-54580-R and TIN2017-84452-R.
Acknowledgements We thank Ramón Huerta for his useful discussions on this work. ...
Recent advancements in high-throughput analysis of brain connectivity have been revealing. ...
doi:10.1186/s12868-018-0452-x
pmid:30373544
pmcid:PMC6205781
fatcat:xv7pgbp76zbdfksl545xof2vzy
29th Annual Computational Neuroscience Meeting: CNS*2020
2020
BMC Neuroscience
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Investigations of this question have, to date, focused largely on deep neural networks trained using supervised learning, in tasks such as image classification. ...
I'll provide a high-level introduction to deep RL, discuss some recent neuroscience-oriented investigations from my group at DeepMind, and survey some wider implications for research on brain and behavior ...
Institute (Challenge grants to SJ), the Research Corporation for Science Advancement (a Cottrell SEED Award to TV), and the German Research Foundation (DFG grant #ME 1535/7-1 to RM), and the Foundation ...
doi:10.1186/s12868-020-00593-1
pmid:33342424
fatcat:edosycf35zfifm552a2aogis7a
Abstracts of the 24th and the 25th Scientific Meeting of the Hong Kong Society of Neurosciences
2006
Neurosignals
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mouse embryos (E8.5 to E16.5) using an immunohistochemical method and a specific antibody to DOC-2/ Dab2 proteins. ...
brain including the cerebellar cortex and nuclei. ...
Similar results were observed in mouse C2C12 muscle cells. These evidences suggest that PRiMA is a critical and essential factor for directing the assembly of G 4 AChE. ...
doi:10.1159/000095356
fatcat:ieuk3f47obaefedon26dsp6jae
From Seeing to Simulating: A Survey of Imaging Techniques and Spatially-Resolved Data for Developing Multiscale Computational Models of Liver Regeneration
2022
Frontiers in Systems Biology
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For instance, microscopy-based imaging methods provide detailed histological information at the tissue and cellular scales. ...
Incorporation of tissue and organ scale data using noninvasive imaging methods can extend these computational models towards a comprehensive accounting of multiscale dynamics of liver regeneration. ...
FUNDING This work was financially supported by the National Institute of Biomedical Imaging and Bioengineering U01 EB023224, and National Institute on Alcohol Abuse and Alcoholism R01 AA018873. ...
doi:10.3389/fsysb.2022.917191
fatcat:23lfnotgszdk3frd6oxtdcgggu
Developing Techniques for Quantitative Renal Magnetic Resonance Imaging
2021
Zenodo
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Here a Convolutional Neural Network (CNN) is developed to generate fully automated masks of the kidneys to compute TKV with better than human precision. ...
This is done without the need for ionising radiation and often without exogenous contrast agents, thus making MRI an ideal tool for both clinical and research use. ...
Neural Networks for Image Segmentation
Methods This architecture is known as a Convolutional Neural Network (CNN) [36, 37] . ...
doi:10.5281/zenodo.5524888
fatcat:ba4f7zyabfckjlbtdyi6p5u3oy
30th Annual Computational Neuroscience Meeting: CNS*2021–Meeting Abstracts
2021
Journal of Computational Neuroscience
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Currently, most functional models of neural activity are based on firing rates, while the most relevant signals for inter-neuron communication are spikes. ...
within the constraints of biological networks. ...
The extended network model measured 4.00 mm × 0.40 mm × 0.51 mm along the transversal, sagittal, and vertical axes, respectively, and contained 491,520 GrCs, 1,228 Golgi cells (GoCs), 1 PC, and 16,158 ...
doi:10.1007/s10827-021-00801-9
pmid:34931275
pmcid:PMC8687879
fatcat:evpmmfpaivgpxdqpive5xdgmwu
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