ライフサイエンスコーパス: image analysis

1 lution for digital pathology and whole slide image analysis.

2 lack of suitable algorithms for fetal brain image analysis.

3 ghlight areas for future growth of automated image analysis.

4 tomography angiography (SS-OCTA) and en-face image analysis.

5 can be quantitatively evaluated by automated image analysis.

6 vantage of volume electron microscopy and 3D image analysis.

7 uning are important processes in large-scale image analysis.

8 sives in cavities through 3-dimensional (3D) image analysis.

9 sis alternative to spot tests, which require image analysis.

10 istopathology visualization and quantitative image analysis.

11 et microscopy, and automated registration by image analysis.

12 n the droplet volume over time using digital image analysis.

13 cardiomyocytes by using real time live cell image analysis.

14 a TNM stage was assigned on the basis of the image analysis.

15 w were characterized by video microscopy and image analysis.

16 crylamide gel electrophoresis and subsequent image analysis.

17 ions and subsequently quantified via digital image analysis.

18 Observed comets were evaluated by image analysis.

19 mmary epithelium and scored by computational image analysis.

20 c activity was assessed by neurite outgrowth image analysis.

21 nanofibres was quantified using correlative image analysis.

22 s in live and fixed cells with computational image analysis.

23 as small as 0.1 kGy when it is combined with image analysis.

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

25 (AD) will depend on the practicality of PET image analysis.

26 chemistry, gene expression, and high-content image analysis.

27 ng a reference image database for subsequent image analysis.

28 bryos by combining time-lapse microscopy and image analysis.

29 portunity to achieve milestones in automated image analysis.

30 es to advance the goal of full automation in image analysis.

31 ed by transmission-electron-microscope (TEM) image analysis.

32 atform-independent software for quantitative image analysis.

33 This poses a challenge to single molecule image analysis.

34 characteristics were reviewed through direct image analysis.

35 istochemical identification and computerized image analysis.

36 active cells were quantified by computerized image analysis.

37 crease the high lateral resolution of the MS imaging analysis.

38 delineation of tumors, enabling quantitative imaging analysis.

39 EC series samples, using immunofluorescence imaging analysis.

40 diagnosed in 8 of 27 eyes (30%) based on FA imaging analysis.

41 Deep learning in retinal image analysis achieves excellent accuracy for the diffe

42 Adaptive hyperspectral image analysis achieves excellent detection sensitivity

43 Imaging and image analysis advances are yielding increasingly comple

44 RS) microscopy, together with a quantitative image analysis algorithm developed by us, to quantify th

45 We further developed an image analysis algorithm that automates the analysis of

46 We developed a high content image analysis algorithm to quantify changes in nuclear

47 logy platform augmented with custom-tailored image analysis algorithms developed in-house.

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

49 chemical and biochemical probes and improved image analysis algorithms for time-lapse microscopy to r

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

51 Image analysis allowed us to derive quantitative measure

52 Image-analysis allows fluorescent particles to be identi

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

54 We developed quantitative 3D image analysis and clonal analysis tools, which revealed

55 on and low bias between the in vivo software image analysis and ex vivo histopathologic quantitative

56 Using image analysis and machine learning approaches, we find

57 It combines image analysis and machine learning methods for automate

58 ed to the subclasses using computer-assisted image analysis and microarray-based reference mosaics.

59 tissue images that significantly facilitates image analysis and minimizes human bias.

60 High-content image analysis and parameterization of the in situ PLA s

61 of gel particle breakdown were quantified by image analysis and physico-chemical analyses of digesta.

62 cludes interactive modules for segmentation, image analysis and post-processing analysis, makes the s

63 nique seems to be more relevant than digital image analysis and promising for both research studies a

64 ional AAV2 in GFP expression based on fundus image analysis and qRT-PCR.

65 le image capture, computer-assisted unbiased image analysis and quantification, and further mathemati

66 duce the amount of data that is required for image analysis and reconstruction.

67 first time, we successfully applied digital image analysis and targeted machine learning to develop

68 plexes, distinct EM density maps obtained by image analysis and three-dimensional (3D) reconstruction

69 ll patients underwent (68)Ga-DOTATATE PET/CT image analysis and total (68)Ga-DOTATATE-Avid tumor volu

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

71 We used a standardized image-analysis and quality-control pipeline established

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

73 Using confocal imaging, custom automated image analysis, and myography, we show that the swine co

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

75 ved using a smartphone camera and integrated image analysis app.

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

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

78 Image analysis approaches enable automated identificatio

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

80 Dark-field microscope (DFM) image analysis approaches used to quantify nanoparticles

81 The methods of image analysis are assessed in their ability to accurate

82 on imaging of the whole brain and subsequent image analysis are prerequisites for understanding anato

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

84 using a procedure borrowed from the field of image analysis based on scaled discrete Tchebichef momen

85 suppression of matrix effects is achieved by image analysis based on the template matching.

86 ally and automatically using the Brain Tumor Image Analysis (BraTumIA).

87 Multimodal hyperspectral image analysis by 2DCOS opens up new opportunities for s

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

89 We show that cross-platform imaging analysis can be readily achieved through DNA enc

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

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

92 Image analysis consisted of a morphological evaluation o

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

94 index (EDI), using fully automated histology image analysis coupled with statistical measures commonl

95 Compared with controls, in TASTPM mice PET image analysis demonstrated significantly increased (by

96 r or at cell membrane upon IFSS, and calcium imaging analysis demonstrated the transient increase of

97 Computational image analysis distinguishes between cell-autonomous (ac

98 response using volumetric magnetic resonance imaging analysis, durability of response, and imaging an

99 crop, Jersey, and Liberty) for hyperspectral imaging analysis, firmness measurement, and human evalua

100 ssment was broadly consistent among confocal image analysis, flow cytometry, and fluorescence quantif

101 High throughput single-cell flow cytometry image analysis following SCI revealed CD200L-dependent d

102 ce software tool that combines the automated image analysis for phase-contrast microscopy movies with

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

104 mbining different groups of textures in 2-DE image analysis for spot detection.

105 or date of first discovery of malignancy by imaging analysis for patients with unresectable tumors o

106 ll-shape Extractor (RACE), a high-throughput image analysis framework for automated three-dimensional

107 Quantitative imaging analysis further demonstrated that the ability o

108 iquantitative subjective analysis, and color image analysis has been developed to improve the reliabi

109 In the past decade, the field of medical image analysis has grown exponentially, with an increase

110 on; imaging procedure; image postprocessing; image analysis; image interpretation; archiving and dist

111 Fluorescence-based growth curves, 3D image analysis, immunohistochemistry and treatment respo

112 s markers, confocal microscopy, quantitative image analysis, immunoprecipitation, and RT-qPCR.

113 Imaging analysis in a subset of individuals (PD+ =43; PD

114 eceptive field functional magnetic resonance imaging analysis in participants with amblyopia and comp

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

116 Longitudinal magnetic resonance imaging analysis in the group receiving chemotherapy ind

117 e recognition but for other forms of medical image analysis, including sonography, computed tomograph

118 Image analysis indicated that when a potential of 1.5 V

119 the Vectra 3.0 multispectral microscopy and image analysis InForm 2.2.1 software (PerkinElmer).These

120 Since the current manual methodology for image analysis is a tedious and subjective approach, the

121 a produced by advanced microscopy, automated image analysis is crucial in modern biology.

122 Visual amyloid image analysis is highly standardized.

123 Through image analysis, it is possible to identify the free ging

124 In addition, by means of electron microscopy image analysis, it proposes a hypothesis for the pigment

125 In this paper, several image analysis methods are evaluated for their ability t

126 Correspondingly, high-throughput automated image analysis methods are necessary to work on par with

127 aging, electron microscopy, and quantitative image analysis methods are now providing some of the fir

128 It is shown that super-resolution image analysis methods can significantly improve countin

129 These image analysis methods describe how to virtually extract

130 s, the application of unbiased computational image analysis methods for morphodynamic quantification

131 We present image analysis methods that determine the order and geom

132 Image analysis methods were used to measure penetration

133 To develop and test single-synapse image analysis methods, we have used datasets from conju

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

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

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

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

138 urther testing in two HCS platforms based on image analysis of assay plates.

139 erning that are also seen in a multispectral image analysis of Balinese rice terraces.

140 today's single-cell studies is quantitative image analysis of cells and fluorescent signals.

141 Image analysis of CT scans was used to calculate Lumbar

142 reased in cancer by performing computational image analysis of epithelial and stromal protein express

143 Using semi-automated image analysis of high-resolution digital facial photogr

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

145 Computational image analysis of large microtubule populations reveals

146 istance between the beads was measured using image analysis of micrographs.

147 Through quantitative image analysis of monolayer disruption and subcellular p

148 n and registration practices, and downstream image analysis of nuclear structures and epigenetic mark

149 el predictions through detailed quantitative image analysis of phenotypic spatial distribution in his

150 in proteomics to improve the quality of the image analysis of proteins separated on a gel.

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

152 Proteomics tools based on image analysis of SDS-PAGE protein gels and protein iden

153 Quantitative image analysis of the fluorescently labeled Ter region s

154 h is based on the accurate quantification by image analysis of the integrated nuclear intensity of ce

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

156 Using computational image analysis of the resulting metastases, we generated

157 f different sizes, which was determined from image analysis of their fluorescence intensity when diff

158 ays and ELISA feasibility; additionally, SPR imaging analysis of a supported membrane microarray show

159 MALDI-MS imaging analysis of hair samples has recently been sugge

160 A detailed chemical imaging analysis of the litter revealed that fungi recru

161 The method is well suited to multi-well imaging, analysis of bacterial cultures with high cell d

162 allowed us to robustly perform quantitative image analysis on remodeling cardiac tissue after myocar

163 e an advanced three-dimensional chemical and imaging analysis on a model material, the nickel-rich la

164 ata information was analyzed using Radiomics Image Analysis package for the presence of 8 conventiona

165 Hue and saturation image analysis parameters are used to define threshold v

166 This stand-alone image analysis pipeline should be of broad practical uti

167 Here we use a quantitative image analysis pipeline to undertake a high-resolution,

168 rs-based 3D ReconsTruction), a multi-faceted image analysis pipeline, permitting quantitative interro

169 Conventional image analysis pipelines for phenotype identification co

170 nternational samples, using state-of-the-art image analysis pipelines optimized for both the cerebell

171 ress this issue, we describe a comprehensive image analysis procedure for structurally complex organo

172 nced data presentation tools facilitates the image analysis process and provides a robust way to veri

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

174 Master" (DSM) algorithm for the popular NIH image analysis program ImageJ, which can be readily adap

175 Image analysis proves that the arabinoxylan surrounds th

176 ity (ECD) at 3 years determined by a central image analysis reading center from clinical specular or

177 after surgery and were analyzed by a central image analysis reading center to determine ECD.

178 Automated image analysis reduced these variations as much as three

179 canning electron microscopy and fluorescence image analysis revealed cross-aligned and lamellar chara

180 Manual image analysis revealed measurement variations in percen

181 Surprisingly, image analysis revealed that although some HP1a interact

182 Particle image analysis revealed that particle formation/aggregat

183 Western blot and immunofluorescence image analysis revealed translocation of Drp1 to mitocho

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

185 The diffusion tensor imaging analysis revealed that fractional anisotropy of

186 biochemical, structural, and superresolution imaging analysis revealed that MCU oxidation promotes MC

187 The imaging analysis revealed that potential suppressor (FOX

188 l microscopy supported by new algorithms for image analysis reveals that lamin A/C knock-down leads t

189 However, image analysis reveals that many of them are not closed

190 Unexpectedly, precision quantitative image analysis reveals that the degree of functional ITH

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

192 Qualitative image analysis showed no difference between attenuation-

193 Qualitative image analysis showed no quality difference between FBP

194 The image analysis showed that the steel surface reactivity

195 Imaging analysis showed that JHC1-64-bound R60A mutant p

196 The pit imaging analysis showed that the P. aeruginosa biofilm c

197 Imaging analysis shows that this fusion and ribosomes ar

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

199 wed by relative quantification using digital image analysis software (P=0.0149).

200 ng custom optical coherence tomography (OCT) image analysis software (Topcon Advanced Boundary Segmen

201 -CHARM relies on the widely-used open-source image analysis software CellProfiler for feature extract

202 etric Tissue Exploration and Analysis (VTEA) image analysis software designed for efficient explorati

203 justed to 96 dots per inch and measured with image analysis software for vertical KT height labial to

204 Next, an automated image analysis software identifies individual cells and

205 Image analysis software is an essential tool used in neu

206 on of Posterior Capsule Opacification (EPCO) image analysis software was used to objectively grade PC

207 Using a CCD camera and image analysis software, H2 PAD senses the chemo-optical

208 icolor fluorescence microscopy and automated image analysis software.

209 automated microscope, camera, and commercial image analysis software.

210 betic eyes using Boston Image Reading Center image analysis software.

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

212 d using synchrotron X-ray micro-CT linked to image analysis software.

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

214 With the increased use of 3D image analysis, standards to ensure the accuracy and rep

215 In this article, we describe an image analysis strategy with improved power for tracking

216 Retrospective consecutive case series and image analysis study.

217 The metabolic imaging analysis supports the notion that there is a con

218 A computer-based image analysis system (Imaging and Informatics in ROP [i

219 oped a semiautomated (ArrayScan) imaging and image analysis system that we applied to quantify whole

220 It is feasible for a computer-based image analysis system to perform comparably with ROP exp

221 by correlation with the i-ROP computer-based image analysis system.

222 e, we report a time-lapse-based bright-field imaging analysis system that allows us to implement a la

223 empowered to select the best tool for their image-analysis tasks.

224 allowing for an objective and user-friendly image analysis technique for detection.

225 Additionally, a new image analysis technique is described to detect particle

226 nt tester, which is based on gravimetric and image analysis technique, for characterising the transpl

227 To achieve this, we apply a quantitative image analysis technique-spatial intensity distribution

228 operties of fabrics based on gravimetric and image analysis technique.

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

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

231 We developed specific tracking and image analysis techniques to analyze cell motion and com

232 We first used high-throughput image analysis techniques to quantify the rate of prunin

233 We explored a range of image analysis techniques, including estimation of blood

234 limitations might be addressed through novel image analysis techniques, up-and-coming CT-based and MR

235 ate an open source zero-footprint viewer for image analysis that is designed to be extensible as well

236 By image analysis, the length and width of MF-CNPs were mea

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

238 g digestion was performed using an automated image analysis throughout the digestion process.

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

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

241 croscopy, using dedicated optical scheme and image analysis to determine both molecular localization

242 used time-lapse 3D imaging and quantitative image analysis to determine how the actin cytoskeleton i

243 With image analysis to directly segment and simultaneously tr

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

245 nsive mutational dissection and accompanying image analysis to identify the sequence elements within

246 and research into genomics and quantitative image analysis to improve diagnostics while also serving

247 methods have been described for large scale image analysis to learn a complex protein regulatory net

248 e, we used live imaging and quantitative, 4D image analysis to measure the sources of cell-size varia

249 Here, we use electron microscopy and image analysis to show that SEPT9 binds to F-actin in a

250 traditional linkage mapping and multivariate image analysis to study the evolution of the architectur

251 tored by using volumetric magnetic resonance imaging analysis to measure the change in size of the pl

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

253 activation of upper airway remodeling using image analysis, together with reticular basement membran

254 d for a versatile, computationally efficient image analysis tool capable of extracting the desired re

255 ere, we present a powerful and user-friendly image analysis tool named phenoVein.

256 itations, we have designed a new open-source image analysis toolkit called EpiTools.

257 We developed image analysis tools employing steerable filtering and i

258 The number of image analysis tools supporting the extraction of archit

259 f their complexities is limited by automated image analysis tools to extract quantitative data.

260 3D reconstruction, followed by a method for image analysis using the freeware SPIERS.

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

262 Positron emission tomography image analysis was completed in 2015.

263 Digital image analysis was employed to measure fat proportionate

264 ectral imaging with object-wise multivariate image analysis was evaluated for its potential to grade

265 Digital image analysis was performed around one incisional tooth

266 Image analysis was performed by 2 observers, based on th

267 Image analysis was performed by 2 reviewers according to

268 Semiautomated image analysis was performed by 2 specialist reviewers b

269 Image analysis was performed by the Philips Integris 3D

270 Quantitative image analysis was performed for 30 randomly selected sp

271 copy followed by single particle and helical image analysis was used to reconstruct three-dimensional

272 Magnetic resonance imaging analysis was performed from 10 right atria and 1

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

274 ed illumination microscopy and computational image analysis, we characterized the supramolecular stru

275 ninvasive optical monitoring and nonspecific image analysis, we determined IDRs of a diverse set of s

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

277 In addition to traditional 3D image analysis, we have developed algorithms to operate

278 , coupled with new computational methods for image analysis, we investigated septin function during p

279 ng histochemistry, immunohistochemistry, and image analysis, we investigated the expression of Factor

280 Using quantitative image analysis, we trace oocyte symmetry breaking in zeb

281 By combining live imaging and quantitative image analysis, we track the behavior of E-cadherin-rich

282 a fractal shape channel design and automated image analysis, we were able to identify inhibitors of C

283 Through use of quantitative imaging analysis, we identified the necessary design par

284 sional confocal microscopy and computational image analysis were applied to assess t-system structure

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

286 MR elastography and anatomic image analysis were performed by two observers.

287 tative assessment of regional tau load using image analysis were performed.

288 Conventional imaging analysis, where smoothing and averaging are empl

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

290 ion method based on CCD imaging and software image analysis, which can measure the resonance frequenc

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

292 on reconstruction machine integrating online image analysis with automated multiphoton imaging.

293 study describes a novel approach, combining image analysis with spatial statistical techniques.

294 ets generally rely on digital microscopy and image analysis, with intrinsically low throughput and re

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

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

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

298 PaCeQuant, which provides a fully automatic image analysis workflow for PC shape quantification.

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

300 ta from electrostatic force microscopy (EFM) image analysis, zeta potential measurements, and charged