A Hybrid CPU-GPU System for Stitching Large Scale Optical Microscopy Images

2014 ◽  
Author(s):  
Timothy Blattner ◽  
Walid Keyrouz ◽  
Joe Chalfoun ◽  
Bertrand Stivalet ◽  
Mary Brady ◽  
...  
Keyword(s):  
Optical Microscopy ◽  
Large Scale ◽  
Microscopy Images

scholarly journals BigNeuron: Large-Scale 3D Neuron Reconstruction from Optical Microscopy Images

Neuron ◽  
2015 ◽  
Vol 87 (2) ◽  
pp. 252-256 ◽  
Author(s):  
Hanchuan Peng ◽  
Michael Hawrylycz ◽  
Jane Roskams ◽  
Sean Hill ◽  
Nelson Spruston ◽  
...  
Keyword(s):  
Optical Microscopy ◽  
Large Scale ◽  
Neuron Reconstruction ◽  
Microscopy Images

scholarly journals Towards pixel-to-pixel deep nucleus detection in microscopy images

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fuyong Xing ◽  
Yuanpu Xie ◽  
Xiaoshuang Shi ◽  
Pingjun Chen ◽  
Zizhao Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Image Data ◽  
Fine Tuning ◽  
Cell Detection ◽  
Imaging Protocol ◽  
Microscopy Image ◽  
Microscopy Images ◽  
Target Data

Abstract Background Nucleus or cell detection is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. Results We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. Conclusions We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

scholarly journals Automated Identification and Location Analysis of Marked Stem Cells Colonies in Optical Microscopy Images

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e80776 ◽  
Author(s):  
Vincenzo Paduano ◽  
Daniela Tagliaferri ◽  
Geppino Falco ◽  
Michele Ceccarelli
Keyword(s):  
Stem Cells ◽  
Optical Microscopy ◽  
Location Analysis ◽  
Automated Identification ◽  
Microscopy Images

Scanning near-field optical microscopy images of microradiographs stored in lithium fluoride films with an optical resolution of λ∕12

2006 ◽  
Vol 88 (14) ◽  
pp. 141107 ◽  
Author(s):  
Alessandro Ustione ◽  
Antonio Cricenti ◽  
Francesca Bonfigli ◽  
Francesco Flora ◽  
Antonella Lai ◽  
...  
Keyword(s):  
Optical Microscopy ◽  
Lithium Fluoride ◽  
Near Field ◽  
Optical Resolution ◽  
Microscopy Images

Neural Cell Segmentation in Large-Scale 3D Color Fluorescence Microscopy Images for Developemental Neuroscience

2018 ◽  
Author(s):  
F. Nourbakhsh ◽  
L. Abdeladim ◽  
S. Clavreul ◽  
K. Loulier ◽  
E. Beaurepaire ◽  
...  
Keyword(s):  
Fluorescence Microscopy ◽  
Large Scale ◽  
Neural Cell ◽  
Cell Segmentation ◽  
Microscopy Images

scholarly journals Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Dmitry S. Bulgarevich ◽  
Susumu Tsukamoto ◽  
Tadashi Kasuya ◽  
Masahiko Demura ◽  
Makoto Watanabe
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Optical Microscopy ◽  
Microscopy Images

scholarly journals A quantitative framework for the analysis of multimodal optical microscopy images

2017 ◽  
Vol 7 (1) ◽  
pp. 24-37 ◽  
Author(s):  
Andrew J. Bower ◽  
Benjamin Chidester ◽  
Joanne Li ◽  
Youbo Zhao ◽  
Marina Marjanovic ◽  
...  
Keyword(s):  
Optical Microscopy ◽  
Microscopy Images

Classification of degradation in oil-impregnated cellulose insulation using texture analysis of optical microscopy images

2016 ◽  
Vol 133 ◽  
pp. 104-112 ◽  
Author(s):  
Nathan D. Jacob ◽  
Hamid Hassanzadeh ◽  
Derek R. Oliver ◽  
Sherif S. Sherif ◽  
Behzad Kordi
Keyword(s):  
Optical Microscopy ◽  
Texture Analysis ◽  
Cellulose Insulation ◽  
Microscopy Images
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