ライフサイエンスコーパス: image analysis

1 orimetric measurements requires quantitative image analysis.

2 were analyzed by immunostaining and digital image analysis.

3 ve scoring system for digital histopathology image analysis.

4 determined using a standardized protocol for image analysis.

5 ccelerated many new applications for medical image analysis.

6 ul for immunology applications by automating image analysis.

7 hree, supported by biochemical profiling and image analysis.

8 wever, by the need for time consuming manual image analysis.

9 ve the accuracy and efficiency of biological image analysis.

10 s coupled with digital imaging and automated image analysis.

11 cellular markers and complex time-consuming image analysis.

12 table for both single image and high content image analysis.

13 hydrodynamics, polymorphs, and comprehensive image analysis.

14 ackground or apply self-ratio methods before image analysis.

15 .5 and sympathetic marker TH by computerized image analysis.

16 lution for digital pathology and whole slide image analysis.

17 et microscopy, and automated registration by image analysis.

18 as performed by volume-of-interest and ratio image analysis.

19 portunity to achieve milestones in automated image analysis.

20 This poses a challenge to single molecule image analysis.

21 characteristics were reviewed through direct image analysis.

22 istochemical identification and computerized image analysis.

23 allenging in terms of observation method and image analysis.

24 ing these cancers can be canonical types for image analysis.

25 d body composition compared with traditional image analysis.

26 tional information compared with traditional image analysis.

27 osis, and differentiation using quantitative image analysis.

28 tron microscopy, and atomic force microscopy image analysis.

29 ntity and fiber length were determined using image analysis.

30 er an alternative, affordable path to robust image analysis.

31 modelling markers were evaluated by computed image analysis.

32 related regions identified in the handedness-imaging analysis.

33 0 nm was later achieved using TOF-SIMS MS/MS imaging analysis.

34 ntricular function and perfusion, and hybrid imaging analysis.

35 ose software-related questions about digital image analysis, acquisition, and data management.

36 this paper, we proposed an automatic Cryo-ET image analysis algorithm for localization and identifica

37 Here, we present an image analysis algorithm, "optical properties extraction

38 and sizes of corneal guttae by an automated image analysis algorithm.

39 r using limited enumeration employing simple image analysis algorithms based on image segmentation.

40 Image analysis algorithms can extract precise cell locat

41 has been interest in developing computerized image analysis algorithms for automated detection of dis

42 describe and share a device, methodology and image analysis algorithms, which allow up to 66 spheroid

43 and biochip surface, supported by automated image analysis algorithms.

44 Automated image-analysis algorithms were used to compare the regio

45 Image analysis allowed measurement of baseline FPA fores

46 Image analysis allows parameter variation to be tracked

47 cence microscopy with real-time quantitative image analysis and allows the unbiased acquisition of mu

48 microscopy (TEM) together with quantitative image analysis and blind scoring of 82 structural parame

49 Using image analysis and computational tools, we precisely qua

50 Here, based on high-resolution topography, image analysis and crater statistics, we have dated 35 d

51 The IMAGINE is a post hoc image analysis and cytokine expression assessment of the

52 ll imaging, combined with recent advances in image analysis and microfluidic technologies, have enabl

53 oaches with machine learning for brain tumor image analysis and prediction algorithm construction wil

54 ion can impact the outcome of location-based image analysis and present an approach to account for al

55 ersection between deep learning and cellular image analysis and provide an overview of both the mathe

56 hile semantic segmentation algorithms enable image analysis and quantification in many applications,

57 sts for pathological features or analysed by image analysis and stereology.

58 Nucleus is a fundamental task in microscopy image analysis and supports many other quantitative stud

59 Advances in computerized image analysis and the use of artificial intelligence-ba

60 ll resolution live imaging with quantitative image analysis and theoretical modeling.

61 or implementing deep learning into pathology image analysis and to provide some potential ways of fur

62 ctive doses were calculated via quantitative image analysis and using OLINDA/EXM software.

63 We developed and implemented 3-D image analysis and virtual reconstruction tools to chara

64 arrowed to companies using deep learning for imaging analysis and diagnosis.

65 Herein, electron microscope imaging analysis and proximity labeling revealed that EX

66 Confocal microscopy imaging analysis and SNAP-tag sucrose density fractionat

67 vivo, which combines live imaging, real-time image analysis, and automated optical perturbations.

68 zes fluorescence microscopy and unsupervised image analysis, and can operate at a sorting speed of up

69 on microscopy, high-resolution 3-dimensional image analysis, and computational fluid dynamics simulat

70 method using confocal microscopy, automated image analysis, and databases for fast quantitative anal

71 ading artificial intelligence technology for image analysis, and discuss its current capabilities, po

72 ative gene-expression microarray and in vivo imaging analysis, and identified novel molecular candida

73 s it suitable for a wide range of additional image analysis applications across biomedical research.

74 t is to develop and embed some commonly used image analysis applications into the Sedeen viewer to cr

75 l to improve many high-throughput microscopy image analysis applications.

76 Segmentation-free, whole image analysis applied to Fluopack images identifies pro

77 we proposed a simple yet effective automatic image analysis approach based on Faster-RCNN.

78 Purpose To investigate whether an image analysis approach that uses image registration for

79 We developed a fully automated image analysis approach to address the challenge of dete

80 Here, we developed an image analysis approach to identify individual cone cell

81 We use simple image analysis approaches to characterize nuclear state,

82 istribution analysis and fluorescence moment image analysis are established tools for measuring molec

83 Patients underwent cross-sectional imaging analysis at 3 months or later after their initia

84 ent with results obtained via semi-automated image analysis based on ImageJ.

85 Here, the goal was to use image analysis-based analysis of FSCV color plots for th

86 Both visual and image analysis-based assays are developed to assess the

87 pression, acquisition of microscopy data and image analysis can be completed within 5 d, requiring on

88 Thus we suggest high-content and automated image analysis can be used for fast phenotyping of funct

89 Automated image analysis can provide clinically-relevant vascular

90 Our augmented multimodal imaging analysis can deliver novel insights into neurobi

91 Quantitative three-dimensional image analysis, combined with a genome-wide screen for D

92 ing data extracted from images by humans and image-analysis computer algorithms, as well as the elect

93 Here we introduce computational image analysis concepts and terms and illustrate them wi

94 gemcitabine, assessed by immunostaining and image analysis, correlates with a poor prognosis, but th

95 High-content image analysis coupled to RNA interference screening off

96 Image analysis data strongly predicted ground truth meas

97 thod based on grid subsampling of microscopy image analysis data to extract the tumor-stroma interfac

98 role for artificial intelligence to improve image analysis, disease diagnosis, and risk prediction.

99 agnosis from pathology images and automating image analysis efficiently and accurately remain signifi

100 sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can spe

101 hmic- and convolutional neural network-based image analysis extract hundreds of features characterizi

102 that enable automated acquisition guided by image analysis for a variety of transmission electron mi

103 Computational image analysis for microscopy.

104 tical transitions, we establish quantitative image analysis for polarimetric maps of extended crystal

105 Here we present a facial image analysis framework, DeepGestalt, using computer vi

106 Our imaging-analysis framework can be applicable to other ph

107 Application of digital image analysis from hematoxylin and eosin (H&E) stain to

108 mammography radiomics plus quantitative 3CB image analysis had PPV(3) of 49% (34 of 70; 95% CI: 36.5

109 in combination with whole slide imaging and image analysis (IA) to quantitatively characterize tempo

110 stochemistry, tissue microarray, and digital image analysis in 141 BC patient samples (75 diagnosed-A

111 , are reliable reference regions for amyloid imaging analysis in SVaD.

112 Image analysis included the likelihood of COVID-19 infec

113 deep learning models have gained success in image analysis including semantic segmentation.

114 Image analysis indicated increased degree of melanosis a

115 Microscopy image analysis is a major bottleneck in quantification o

116 Deep learning use for quantitative image analysis is exponentially increasing.

117 An important step in brain image analysis is to precisely assign signal labels to s

118 We make our tool freely available in Brain Imaging Analysis Kit (BrainIAK).

119 formatics topics including network analysis, imaging analysis, machine learning, gene expression anal

120 Here we introduce this image analysis method by presenting its biophysical foun

121 lation spectroscopy (RICS) is a fluorescence image analysis method for extracting the mobility, conce

122 Our novel image analysis method led to extraction of over 20 featu

123 first place, we developed a fully automated image analysis method to measure quantitatively changes

124 -throughput, single-cell, fluorescence-based image analysis method utilizing the Amnis ImageStream(X)

125 ed fish extracts and quantified by a digital image analysis method.

126 Image analysis methods were used to measure penetration

127 cquisition speed, together with more complex image analysis methods, facilitate tackling biological p

128 and (ii) in situ hybridization and spectral imaging analysis methods that allow simultaneous detecti

129 Advanced brain imaging analysis methods, including multivariate pattern

130 ng (HCS) with U-Net based deep learning (DL) image analysis models.

131 e analyzed with microfluidic photo treatment-image analysis (muPIA) and were found to have a Deming-r

132 human performance, with applications to bio-image analysis now starting to emerge.

133 We used object-based image analysis (OBIA) method to estimate the Adelie peng

134 Image analysis of (18)F-mFBG PET data showed correlation

135 (i) sparse feature tracking computer vision image analysis of 200 Hz video, (ii) load platform, (iii

136 eported filtration-assisted approach enables image analysis of aggregates formed via interaction betw

137 Image analysis of around 50,000 cells reveals a clear an

138 nto existing tools and methods for automated image analysis of behavior to further augment its output

139 Image analysis of both scans was performed by a rater bl

140 mography radiomics alone, and (c) a combined image analysis of both.

141 lusion Quantitative three-compartment breast image analysis of breast masses combined with mammograph

142 In conclusion, our work shows that detailed image analysis of complex endothelial phenotypes can rev

143 penia can be evaluated using cross-sectional image analysis of CT-scans, at the level of the third lu

144 Monitoring yogurt fermentation by the image analysis of diffraction patterns generated by the

145 quantitative three-compartment breast (3CB) image analysis of dual-energy mammography to limit unnec

146 Image analysis of fluorescent labeled exosomes derived f

147 Multiparameter flow cytometry and automated image analysis of immunostaining were applied to liver t

148 Image analysis of individual Fe oxide particles revealed

149 Lipid absorption was quantified by digital image analysis of lipid droplets, by measurement of baso

150 Image analysis of motile cell populations, both primary

151 Object-based image analysis of photogrammetric data show that coral h

152 We demonstrate the utility of the tool in image analysis of progenitor cells in mouse bone healing

153 his work, a novel preprocessing strategy for image analysis of saffron thin layer chromatographic (TL

154 pharmacological tools in vivo, together with image analysis of single embryos and pluripotent cell cu

155 al Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the

156 cent conjugate, phalloidin, and high-content image analysis of the complementary labels showed clear

157 changes are benchmarked against label-based image analysis of the mitochondrial network.

158 he diffusion width of the ions and the pH by image analysis of the pH indicator color change.

159 The image analysis of the photos was performed using appropr

160 lular contractility and can be obtained from image analysis of the recorded patterning process.

161 (UTE) MRI at breath holding for quantitative image analysis of ventilation inhomogeneity and hyperinf

162 Unsupervised segmentation imaging analysis of acquired DESI-IMS data reveals disti

163 In the third step, the influence of the image analysis on the reproducibility and comparability

164 cally conducted off-line through microscopic image analysis, or by first collecting cells from each o

165 for correlation, segmentation, and computer image analysis, our method, called "MultiCLEM," allows u

166 y ctDNA detection and 57% for only metabolic imaging analysis (P < .001 for comparison of either tech

167 Image analysis performed colony counts on the 24-hour im

168 We developed an image analysis pipeline for 3D imaging of GEMs in the co

169 ess this, we developed a deep learning-based image analysis pipeline that performs segmentation, trac

170 We apply the same image analysis pipeline to the experimental and simulate

171 ton tomography (STPT) and a custom-developed image analysis pipeline visualized and quantified postst

172 Here, we have designed a quantitative image-analysis pipeline for decoding organ-level calcium

173 Here, we describe the ultrafast imaging, analysis pipeline and automated measurement of

174 Here, we develop a clearing-imaging-analysis pipeline to visualize innervation of in

175 Conventional image analysis pipelines for phenotype identification co

176 ces with SerialEM to enact specimen-specific image-analysis pipelines that enable feedback microscopy

177 esponse to Microbe Analysis), an open-source image analysis platform based on machine learning algori

178 ing combined with a novel customized digital image analysis platform.

179 thus making this a valuable extension to the image analysis portfolio already available for fission y

180 ithout surgery, combined ctDNA and metabolic imaging analysis predicted progression in 100% of patien

181 (DL) allow automating time-consuming manual image analysis processes based on annotated training dat

182 ImageJ is an image analysis program extensively used in the biologica

183 Here, we describe an automated, interactive image analysis program that facilitates the accurate gen

184 An intuitive intelligent image analysis program to assess host protein recruitmen

185 Image analysis proves that the arabinoxylan surrounds th

186 Deep learning approaches for image analysis provide an opportunity to develop user-fr

187 t tumour budding, quantified using automated image analysis provides prognostic value for muscle inva

188 ntral donor ECD were determined by a central image analysis reading center.

189 Central ECD was determined by a central image analysis reading center.

190 (e.g., smartphone applications) for advanced image analysis requires complex, custom-written processi

191 be useful for geo-spatial and manufacturing image analysis researchers.

192 However, image analysis revealed localised fillet pigment-depleti

193 super-resolution microscopy and quantitative image analysis revealed reorganization of Na(V)1.5 away

194 Diffusion tensor imaging analysis revealed a significant increase in FA i

195 Live imaging analysis revealed a strong correlation between C

196 The diffusion tensor imaging analysis revealed that fractional anisotropy of

197 Localization and time-lapse imaging analysis reveals that MAP7 is enriched at branch

198 issue sections (MILAN technique) followed by image analysis, RT-PCR and shotgun proteomics.

199 Consistent with this prediction, imaging analysis show that CXCL13 binds to extracellular

200 Quantitative image analysis shows that the basal foot is organized in

201 he slit lamp and objectively using automated image analysis software (AQUA).

202 ected by the naked eye and analysed using an image analysis software (ImageJ) for the purpose of quan

203 is available as a macro for the open-source image analysis software Fiji/ImageJ.

204 ytoCensus outperforms other freely available image analysis software in accuracy and speed of cell de

205 Overall, the image analysis software proved to be highly sensitive an

206 e dye Nile red, fluorescence microscopy, and image analysis software.

207 GC number and area were measured with image analysis software.

208 ment and Risk Yield (CANARY) is quantitative imaging analysis software that predicts the histopatholo

209 s is challenging for researchers who are not image analysis specialists.

210 Retrospective consecutive case series and image analysis study.

211 rithms have shown great promise in pathology image analysis, such as in tumor region identification,

212 both techniques are suitable for functional image analysis.Supplemental material is available for th

213 Quantitative image analysis, supported by (13)C NMR, scanning electro

214 coefficient as high as 0.963, represent that image analysis system is an accurate and highly consiste

215 n fresh cut tender jackfruit slices by using image analysis technique and justify the results by comp

216 Recently, a diffusion magnetic resonance imaging analysis technique using a bitensor model was in

217 Using a novel magnetic resonance imaging analysis technique, based on the ratio of T1- an

218 ish automated quantitative and pattern-based image analysis techniques of Lipiodol deposition on 24 h

219 atomic resolution using standard imaging and image analysis techniques.

220 d that combines electron microscopy (EM) and image-analysis techniques and allows both visualization

221 Specifically, we adapt modern image analysis technology to determine the parcel-specif

222 onal (3-D) reaction-diffusion models and 3-D image analysis that are providing new insights into how

223 ac1 activity assays and spatio-temporal FRET image analysis, the extracellular and cytoplasmic Cdh3 d

224 Combined with image analysis, the mouthguards successfully uncover the

225 From sample preparation through image analysis, the protocol can be executed within one

226 achine learning methods in digital pathology image analysis, this is the first in-depth review of the

227 Computer vision image analysis thus offers a practical approach to measu

228 hat combines deep learning with mathematical image analysis to accurately segment and classify single

229 nd 3D microscopy with machine learning-based image analysis to assess the physiology of micrometastas

230 d time-lapse videomicrosopy and quantitative image analysis to characterize cell motility phenotypes

231 We use image analysis to compare the fraction of each phase at

232 d a nanoscale imaging approach with advanced image analysis to detect individual vesicle fusion event

233 in sickle trait cells and robust, automated image analysis to detect the precise time at which fiber

234 tool that brings machine-learning-based (bio)image analysis to end users without substantial computat

235 we used immunofluorescence and quantitative image analysis to examine cochlear innervation in mature

236 sed multiplex immunofluorescence and digital image analysis to examine the topography of PD-L1(+) and

237 velop a smartphone application for automated image analysis to facilitate accurate and robust countin

238 We used automated image analysis to identify F-actin bundles and crossover

239 t rotation workflow that utilizes on-the-fly image analysis to identify the optimal light sheet imagi

240 We also performed volumetric image analysis to quantify the number of bacteria residi

241 s in 96 well microplates, and uses automatic image analysis to quantify the number of colocalized mat

242 oscopy and deep learning-based computational image analysis to quantify the uptake of specific drug m

243 One recent advancement is the use of digital image analysis to rapidly distinguish between chromogeni

244 Here we apply advanced image analysis to reveal extracellular matrix-responsive

245 We use quantitative image analysis to show that N-cadherin promotes neural d

246 staining, CRISPR interference, RNAscope, and image analysis to validate cell-type-specific cis-regula

247 a direct high-speed atomic force microscopy imaging analysis to visualize the constriction of single

248 Here we present EpiGraph, an image analysis tool that quantifies epithelial organizat

249 raphy (muCT) imaging techniques with bespoke image analysis tools and mathematical modelling to inves

250 Image analysis tools automate detection of cell bodies a

251 We developed image analysis tools employing steerable filtering and i

252 in large part to the limitations of current image analysis tools that cannot process astrocyte image

253 al parameters, easily acquired with existing image analysis tools.

254 tists have been working for decades to build image analysis tools.

255 croscopy of large tissue volumes, as well as image analysis using advanced platforms such as volumetr

256 aration, droplet creation, image capture and image analysis using Axisymmetric Drop Shape Analysis co

257 planning research in the field of radiologic image analysis using convolutional neural networks.

258 For the first time 3D image analysis was carried out by synchrotron radiation

259 The mineral dissolution from image analysis was comparable to that measured from effl

260 Image analysis was performed automatically using a novel

261 Image analysis was performed by 2 reviewers according to

262 Image analysis was performed by 3 independent nuclear me

263 Image analysis was performed by three independent nuclea

264 Image analysis was performed independently by two blinde

265 Digital image analysis was performed to measure the average immu

266 Quantitative image analysis was used to measure the temporal change i

267 Materials and Methods Computerized image analysis was used to quantify breast density and e

268 mbination of dye extraction and fluorescence image analysis was used to quantify the total amount of

269 The imaging analysis was corroborated by post-mortem histolo

270 he utility of this new platform for N-glycan imaging analysis was demonstrated with a variety of FFPE

271 Imaging analysis was performed from June 15, 2015, to Au

272 In situ imaging analysis was performed on Global Initiative for

273 ing fluorescence microscopy and quantitative image analysis we find that stretch-induced nuclear elon

274 Chemists live off them." Thus, the detailed image analysis we present simultaneously provides quanti

275 Using time-lapse microscopy and quantitative image analysis, we discovered calcium spikes both at the

276 Using confocal imaging and image analysis, we evaluate embryo location along the lo

277 Using quantitative pathology image analysis, we extracted morphological features from

278 Using quantitative image analysis, we find that CAM is significantly reduce

279 ling procedures, and standardized FreeSurfer image analysis, we found negative dose-response associat

280 x immunofluorescence microscopy with digital image analysis, we found that cHL is highly enriched for

281 (CD63-GFP(f/f)) mice and immuno-EM/confocal image analysis, we found that neuronal CD63-GFP(+) ILVs

282 Using fluorescence video microscopy and image analysis, we investigated the architectural dynami

283 redator (bird and fish) vision modelling and image analysis, we quantified background matching and di

284 itro and in vivo infections and quantitative image analysis, we show that the lysosomal content and a

285 using extensive new psychophysical data and image analysis, we show that this hypothesis accounts fo

286 rameters of native gels, electron microscopy image analysis were performed and qualitatively related

287 s comparison was then followed by Free Water Imaging analysis, where two parameters, the fractional v

288 expanded by machine-learning algorithms for image analysis, which can be applied to the task of synd

289 escribe an enhanced fluorescence fluctuation imaging analysis, which employs statistical resampling t

290 us photography followed by automated retinal image analysis with human supervision.

291 We combined traditional image analysis with modern machine learning to efficient

292 rapidly emerging as a new method for cancer image analysis, with significantly enhanced predictive p

293 package for performing number and brightness image analysis, with the implementation of a novel autom

294 We implemented an image-analysis work flow to analyze the capacity of both

295 rthermore, the high-throughput nature of our image analysis workflow allowed us to profile the physio

296 ility Map Viewer accelerates and informs the image analysis workflow by providing a tool for experime

297 nt Bacterial Cell Morphometry 3D (BCM3D), an image analysis workflow that combines deep learning with

298 Here, we developed an imaging and image analysis workflow to analyze nanoparticle-cell int

299 They are very costly because the image analysis workflows are required to be executed sev

300 Quantitative image analysis workflows were developed to assess ectoce