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Lehmussola, A.,
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deconvolution
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1002
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1201
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Maska, M.,
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Segmentation and Shape Tracking of Whole Fluorescent Cells Based
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IEEE DOI
1307
biodiffusion; cancer; fluorescence; lung; Chan-Vese model;
graph cut frameworks
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Arslan, S.,
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Gunduz-Demir, C.,
Attributed Relational Graphs for Cell Nucleus Segmentation
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IEEE DOI
1307
fluorescence microscopy image; Attributed relational graph
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Huang, C.,
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AMPFLUID: Aggregation Magnified Post-Assay Fluorescence for
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PIEEE(103), No. 2, February 2015, pp. 225-235.
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1504
Charge coupled devices
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Zhang, G.L.[Guang-Lei],
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1506
Bayes methods
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Tominaga, S.[Shoji],
Hirai, K.[Keita],
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Estimation of bispectral Donaldson matrices of fluorescent objects by
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DOI Link
1506
Imaging systems; Multispectral and hyperspectral imaging
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Basset, A.,
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Adaptive Spot Detection With Optimal Scale Selection in Fluorescence
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1509
Computer architecture
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Huelsnitz, T.[Thomas],
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Interferometric imaging with three objectives,
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DOI Link
1602
Fluorescence microscopy
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Zhu, B.,
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Sevick-Muraca, E.M.,
Determining the Performance of Fluorescence Molecular Imaging Devices
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MedImg(35), No. 3, March 2016, pp. 802-811.
IEEE DOI
1603
Cameras
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Dimiccoli, M.[Mariella],
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1605
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Amiot, C.[Carole],
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Chanussot, J.[Jocelyn],
Pescatore, J.,
Desvignes, M.,
Spatio-Temporal Multiscale Denoising of Fluoroscopic Sequence,
MedImg(35), No. 6, June 2016, pp. 1565-1574.
IEEE DOI
1606
Biomedical imaging
BibRef
Priego, B.[Blanca],
Veganzones, M.A.[Miguel A.],
Chanussot, J.[Jocelyn],
Amiot, C.[Carole],
Prieto, A.[Abraham],
Duro, R.[Richard],
Spatio-temporal cellular automata-based filtering for image sequence
denoising: Application to fluoroscopic sequences,
ICIP13(548-552)
IEEE DOI
1402
Automata
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Negash, A.[Awoke],
Labouesse, S.[Simon],
Sandeau, N.[Nicolas],
Allain, M.[Marc],
Giovannini, H.[Hugues],
Idier, J.[Jerome],
Heintzmann, R.[Rainer],
Chaumet, P.C.[Patrick C.],
Belkebir, K.[Kamal],
Sentenac, A.[Anne],
Improving the axial and lateral resolution of three-dimensional
fluorescence microscopy using random speckle illuminations,
JOSA-A(33), No. 6, June 2016, pp. 1089-1094.
DOI Link
1606
Speckle imaging; Three-dimensional microscopy
BibRef
Gorpas, D.,
Ma, D.,
Bec, J.,
Yankelevich, D.R.,
Marcu, L.,
Real-Time Visualization of Tissue Surface Biochemical Features
Derived From Fluorescence Lifetime Measurements,
MedImg(35), No. 8, August 2016, pp. 1802-1811.
IEEE DOI
1608
Fluorescence
BibRef
Small, A.[Alex],
Multifluorophore localization as a percolation problem:
limits to density and precision,
JOSA-A(33), No. 7, July 2016, pp. B21-B30.
DOI Link
1608
Probability theory, stochastic processes, and statistics
BibRef
Chakrova, N.[Nadya],
Rieger, B.[Bernd],
Stallinga, S.[Sjoerd],
Deconvolution methods for structured illumination microscopy,
JOSA-A(33), No. 7, July 2016, pp. B12-B20.
DOI Link
1608
Deconvolution; Inverse problems; Fluorescence microscopy
BibRef
Zhang, Y.[Yide],
Khan, A.A.[Aamir A.],
Vigil, G.D.[Genevieve D.],
Howard, S.S.[Scott S.],
Investigation of signal-to-noise ratio in frequency-domain
multiphoton fluorescence lifetime imaging microscopy,
JOSA-A(33), No. 7, July 2016, pp. B1-B11.
DOI Link
1608
Fluorescence microscopy
BibRef
Svoboda, D.[David],
Ulman, V.[Vladimír],
MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences
of Cell Populations in Fluorescence Microscopy,
MedImg(36), No. 1, January 2017, pp. 310-321.
IEEE DOI
1701
BibRef
Earlier:
Generation of Synthetic Image Datasets for Time-Lapse Fluorescence
Microscopy,
ICIAR12(II: 473-482).
Springer DOI
1206
Algorithm design and analysis
BibRef
Ulman, V.[Vladimír],
Orému, Z.[Zoltán],
Svoboda, D.[David],
TRAgen: A Tool for Generation of Synthetic Time-Lapse Image Sequences
of Living Cells,
CIAP15(I:623-634).
Springer DOI
1511
BibRef
An, Y.,
Liu, J.,
Zhang, G.,
Jiang, S.,
Ye, J.,
Chi, C.,
Tian, J.,
Compactly Supported Radial Basis Function-Based Meshless Method for
Photon Propagation Model of Fluorescence Molecular Tomography,
MedImg(36), No. 2, February 2017, pp. 366-373.
IEEE DOI
1702
Finite element analysis
BibRef
Labouesse, S.,
Negash, A.,
Idier, J.,
Bourguignon, S.,
Mangeat, T.,
Liu, P.,
Sentenac, A.,
Allain, M.,
Joint Reconstruction Strategy for Structured Illumination Microscopy
With Unknown Illuminations,
IP(26), No. 5, May 2017, pp. 2480-2493.
IEEE DOI
1704
BibRef
Earlier: A1, A8, A3, A4, A2, A6, A7, Only:
Fluorescence blind structured illumination microscopy:
A new reconstruction strategy,
ICIP16(3166-3170)
IEEE DOI
1610
Image reconstruction
BibRef
Li, J.Z.[Ji-Zhou],
Xue, F.[Feng],
Blu, T.[Thierry],
Fast and accurate three-dimensional point spread function computation
for fluorescence microscopy,
JOSA-A(34), No. 6, June 2017, pp. 1029-1034.
DOI Link
1706
Fluorescence microscopy, Three-dimensional, microscopy
BibRef
Storath, M.,
Rickert, D.,
Unser, M.,
Weinmann, A.,
Fast Segmentation From Blurred Data in 3D Fluorescence Microscopy,
IP(26), No. 10, October 2017, pp. 4856-4870.
IEEE DOI
1708
deconvolution, fluorescence, graphics processing units,
image restoration, image segmentation, optical microscopy,
parallel processing, 3D Potts model, 3D image segmentation,
3D wide field fluorescence microscopy data, data blurring,
decoupled subproblem, noncubic grid,
piecewise constant Mumford-Shah model, sequential CPU version,
space discretization, splitting approach,
BibRef
Storath, M.,
Weinmann, A.,
Fast Median Filtering for Phase or Orientation Data,
PAMI(40), No. 3, March 2018, pp. 639-652.
IEEE DOI
1802
Complexity theory, Image edge detection, MATLAB,
Optical interferometry, Robustness, Runtime, Smoothing methods,
phase data
BibRef
Wang, S.,
Arena, E.T.,
Eliceiri, K.W.,
Yuan, M.,
Automated and Robust Quantification of Colocalization in Dual-Color
Fluorescence Microscopy: A Nonparametric Statistical Approach,
IP(27), No. 2, February 2018, pp. 622-636.
IEEE DOI
1712
Biology, Correlation, Measurement, Microscopy, Robustness,
Statistical analysis, Testing, colocalization,
scan statistics
BibRef
Wang, S.,
Arena, E.T.,
Becker, J.T.,
Bement, W.M.,
Sherer, N.M.,
Eliceiri, K.W.,
Yuan, M.,
Spatially Adaptive Colocalization Analysis in Dual-Color Fluorescence
Microscopy,
IP(28), No. 9, Sep. 2019, pp. 4471-4485.
IEEE DOI
1908
Microscopy, Indexes, Testing, Proteins, Kernel, Fluorescence,
Colocalization, fluorescence microscopy, hypothesis testing,
kernel method
BibRef
Grushnikov, A.[Andrey],
Niwayama, R.[Ritsuya],
Kanade, T.[Takeo],
Yagi, Y.S.[Yasu-Shi],
3D level set method for blastomere segmentation of preimplantation
embryos in fluorescence microscopy images,
MVA(29), No. 1, January 2018, pp. 125-134.
WWW Link.
1801
BibRef
Fortun, D.,
Guichard, P.,
Hamel, V.,
Sorzano, C.O.S.,
Banterle, N.,
Gönczy, P.,
Unser, M.,
Reconstruction From Multiple Particles for 3D Isotropic Resolution in
Fluorescence Microscopy,
MedImg(37), No. 5, May 2018, pp. 1235-1246.
IEEE DOI
1805
Estimation, Image reconstruction, Image resolution, Microscopy,
Proteins, Protein localization,
pose estimation
BibRef
Vivona, L.[Letizia],
Cascio, D.[Donato],
Taormina, V.[Vincenzo],
Raso, G.[Giuseppe],
Automated approach for indirect immunofluorescence images
classification based on unsupervised clustering method,
IET-CV(12), No. 7, October 2018, pp. 989-995.
DOI Link
1809
BibRef
Samuylov, D.K.,
Székely, G.,
Paul, G.,
A Bayesian Framework for the Analog Reconstruction of Kymographs From
Fluorescence Microscopy Data,
IP(28), No. 1, January 2019, pp. 410-425.
IEEE DOI
1810
Geometry, Image reconstruction, Photometry, Microscopy,
Image resolution, Inverse problems, Analog reconstruction,
virtual microscope
BibRef
Ahmed, M.F.,
Yasar, S.,
Cho, S.H.,
A Monte Carlo Model of a Benchtop X-Ray Fluorescence Computed
Tomography System and Its Application to Validate a
Deconvolution-Based X-Ray Fluorescence Signal Extraction Method,
MedImg(37), No. 11, November 2018, pp. 2483-2492.
IEEE DOI
1811
Detectors, Photonics, X-ray imaging, Economic indicators,
II-VI semiconductor materials, Cadmium compounds, Collimators,
X-ray detector response
BibRef
Samuylov, D.K.,
Purwar, P.,
Székely, G.,
Paul, G.,
Modeling Point Spread Function in Fluorescence Microscopy With a
Sparse Gaussian Mixture: Tradeoff Between Accuracy and Efficiency,
IP(28), No. 8, August 2019, pp. 3688-3702.
IEEE DOI
1907
calibration, fluorescence, Gaussian processes,
image reconstruction, image resolution, image restoration,
virtual microscope framework
BibRef
Bentz, B.Z.,
Lin, D.,
Patel, J.A.,
Webb, K.J.,
Multiresolution Localization With Temporal Scanning for
Super-Resolution Diffuse Optical Imaging of Fluorescence,
IP(29), No. 1, 2020, pp. 830-842.
IEEE DOI
1910
Optical imaging, Spatial resolution, Neurons, Optical scattering,
Mathematical model, Super-resolution, optical imaging,
matching pursuit
BibRef
Tominaga, S.[Shoji],
Hirai, K.[Keita],
Horiuchi, T.[Takahiko],
Spectral reconstruction of fluorescent objects with mutual
illumination effects,
JOSA-A(36), No. 9, September 2019, pp. 1512-1522.
DOI Link
1912
Fluorescent markers, Fluorescent materials, Light intensity,
Liquid crystal filters, Reconstruction algorithms, Spectral imaging
BibRef
Chandler, T.[Talon],
Shroff, H.[Hari],
Oldenbourg, R.[Rudolf],
La Riviere, P.[Patrick],
Spatio-angular fluorescence microscopy I. Basic theory,
JOSA-A(36), No. 8, August 2019, pp. 1334-1345.
DOI Link
1912
Electric fields, Image processing, Imaging systems,
Imaging techniques, Optical transfer functions, Point spread function
BibRef
Chandler, T.[Talon],
Shroff, H.[Hari],
Oldenbourg, R.[Rudolf],
La Riviere, P.[Patrick],
Spatio-angular fluorescence microscopy II. Paraxial 4f imaging,
JOSA-A(36), No. 8, August 2019, pp. 1346-1360.
DOI Link
1912
Electric fields, Fourier transforms, Imaging systems,
Point spread function, Pupil, Spatial frequency
BibRef
Yi, H.[Huangjian],
Jiao, P.[Pu],
Li, X.N.[Xiao-Nan],
Peng, J.Y.[Jin-Ye],
He, X.W.[Xiao-Wei],
Three-way decision based reconstruction frame for fluorescence
molecular tomography,
JOSA-A(35), No. 11, November 2018, pp. 1814-1822.
DOI Link
1912
Anisotropic diffusion, Finite element method, Image quality,
Imaging systems, Inverse problems, Numerical simulation
BibRef
Field, J.J.[Jeffrey J.],
Wernsing, K.A.[Keith A.],
Squier, J.A.[Jeff A.],
Bartels, R.A.[Randy A.],
Three-dimensional single-pixel imaging of incoherent light with
spatiotemporally modulated illumination,
JOSA-A(35), No. 8, August 2018, pp. 1438-1449.
DOI Link
1912
Aberration compensation, Microscopy, Image formation theory,
Fluorescence microscopy, Interference microscopy, Wave propagation
BibRef
Jung, S.,
Kim, T.,
Lee, W.,
Kim, H.,
Kim, H.S.,
Im, H.,
Ye, S.,
Dynamic In Vivo X-ray Fluorescence Imaging of Gold in Living Mice
Exposed to Gold Nanoparticles,
MedImg(39), No. 2, February 2020, pp. 526-533.
IEEE DOI
2002
Cameras, Economic indicators, Photonics, Collimators, X-ray imaging,
In vivo, X-ray fluorescence, pinhole, gold nanoparticles, in vivo,
molecular imaging
BibRef
Zhu, B.,
Kwon, S.,
Rasmussen, J.C.,
Litorja, M.,
Sevick-Muraca, E.M.,
Comparison of NIR Versus SWIR Fluorescence Image Device Performance
Using Working Standards Calibrated With SI Units,
MedImg(39), No. 4, April 2020, pp. 944-951.
IEEE DOI
2004
Molecular and cellular imaging, optical imaging/OCT/DOT,
evaluation and performance, system design, validation
BibRef
Chow, J.C.K.,
Boyd, S.K.,
Lichti, D.D.,
Ronsky, J.L.,
Robust Self-Supervised Learning of Deterministic Errors in
Single-Plane (Monoplanar) and Dual-Plane (Biplanar) X-Ray Fluoroscopy,
MedImg(39), No. 6, June 2020, pp. 2051-2060.
IEEE DOI
2006
Calibration, photogrammetry, X-ray, fluoroscopy
BibRef
Bentz, B.Z.,
Mahalingam, S.M.,
Ysselstein, D.,
Montenegro, P.C.,
Cannon, J.R.,
Rochet, J.,
Low, P.S.,
Webb, K.J.,
Localization of Fluorescent Targets in Deep Tissue With Expanded Beam
Illumination for Studies of Cancer and the Brain,
MedImg(39), No. 7, July 2020, pp. 2472-2481.
IEEE DOI
2007
Mathematical model, Radio frequency, Nonhomogeneous media, Imaging,
Detectors, Brain modeling, Tumors, Fluorescence imaging,
inverse problems
BibRef
Mukherjee, S.,
Gonzalez-Gomez, C.,
Danglot, L.,
Lagache, T.,
Olivo-Marin, J.,
Generalizing the Statistical Analysis of Objects' Spatial Coupling in
Bioimaging,
SPLetters(27), 2020, pp. 1085-1089.
IEEE DOI
2007
Spatial statistics, colocalization, Ripley's K-function, coupling,
fluorescence microscopy, level sets
BibRef
Dai, Q.Q.[Qi-Qin],
Chopp, H.,
Pouyet, E.[Emeline],
Cossairt, O.[Oliver],
Walton, M.[Marc],
Katsaggelos, A.K.[Aggelos K.],
Adaptive Image Sampling Using Deep Learning and Its Application on
X-Ray Fluorescence Image Reconstruction,
MultMed(22), No. 10, October 2020, pp. 2564-2578.
IEEE DOI
2009
Image reconstruction, Image sampling, Imaging, X-ray imaging,
Machine learning, Spatial resolution, Adaptive sampling, inpainting
BibRef
Dai, Q.Q.[Qi-Qin],
Pouyet, E.[Emeline],
Cossairt, O.[Oliver],
Walton, M.[Marc],
Casadio, F.[Francesca],
Katsaggelos, A.K.[Aggelos K.],
X-Ray fluorescence image super-resolution using dictionary learning,
IVMSP16(1-5)
IEEE DOI
1608
Dictionaries
BibRef
Meng, H.,
Gao, Y.,
Yang, X.,
Wang, K.,
Tian, J.,
K-Nearest Neighbor Based Locally Connected Network for Fast
Morphological Reconstruction in Fluorescence Molecular Tomography,
MedImg(39), No. 10, October 2020, pp. 3019-3028.
IEEE DOI
2010
Image reconstruction, Photonics, Fluorescence, Inverse problems,
Surface reconstruction, In vivo, Surface morphology,
brain
BibRef
Kim, D.S.[Dong Sik],
Lee, E.[Eunae],
Measurement of the Lag Correction Factor in Low-Dose Fluoroscopic
Imaging,
MedImg(40), No. 6, June 2021, pp. 1661-1672.
IEEE DOI
2106
Detectors, X-rays, Logic gates, Heuristic algorithms,
Thin film transistors, Electron tubes, Imaging, Exposure leak,
lag correction factor
BibRef
Andrle, A.[Anna],
Farchmin, N.[Nando],
Hagemann, P.[Paul],
Heidenreich, S.[Sebastian],
Soltwisch, V.[Victor],
Steidl, G.[Gabriele],
Invertible Neural Networks Versus MCMC for Posterior Reconstruction in
Grazing Incidence X-ray Fluorescence,
SSVM21(528-539).
Springer DOI
2106
BibRef
Kromp, F.[Florian],
Fischer, L.[Lukas],
Bozsaky, E.[Eva],
Ambros, I.M.[Inge M.],
Dörr, W.[Wolfgang],
Beiske, K.[Klaus],
Ambros, P.F.[Peter F.],
Hanbury, A.[Allan],
Taschner-Mandl, S.[Sabine],
Evaluation of Deep Learning Architectures for Complex
Immunofluorescence Nuclear Image Segmentation,
MedImg(40), No. 7, July 2021, pp. 1934-1949.
IEEE DOI
2107
Image segmentation, Computer architecture, Deep learning,
Microscopy, Microprocessors, Training, Task analysis,
nuclear image segmentation
BibRef
Li, R.Q.[Rui-Qi],
Xie, X.L.[Xiao-Liang],
Zhou, X.H.[Xiao-Hu],
Liu, S.Q.[Shi-Qi],
Ni, Z.L.[Zhen-Liang],
Zhou, Y.J.[Yan-Jie],
Bian, G.B.[Gui-Bin],
Hou, Z.G.[Zeng-Guang],
Real-Time Multi-Guidewire Endpoint Localization in Fluoroscopy Images,
MedImg(40), No. 8, August 2021, pp. 2002-2014.
IEEE DOI
2108
Location awareness, Surgery, Instruments, Task analysis, Detectors,
Real-time systems, Retina, Guidewire endpoint,
surgical instrument
BibRef
Lee, J.B.[Jung-Bin],
Kim, S.H.[Seong-Han],
Kim, J.[Jeongho],
Son, B.J.[Byeong Jae],
Yoon, C.H.[Chang Ho],
Kim, H.K.[Hong Kyun],
Kim, K.H.[Ki Hean],
Moxifloxacin-Based Extended Depth-of-Field Fluorescence Microscopy
for Real-Time Conjunctival Goblet Cell Examination,
MedImg(41), No. 8, August 2022, pp. 2004-2008.
IEEE DOI
2208
Imaging, Microscopy, Lenses, Mice, Mirrors, Fluorescence, Standards,
Extended depth-of-field, fluorescence microscopy,
ocular surface diseases
BibRef
Berker, Y.[Yannick],
ElHarouni, D.[Dina],
Peterziel, H.[Heike],
Fiesel, P.[Petra],
Witt, O.[Olaf],
Oehme, I.[Ina],
Schlesner, M.[Matthias],
Oppermann, S.[Sina],
Patient-by-Patient Deep Transfer Learning for Drug-Response Profiling
Using Confocal Fluorescence Microscopy of Pediatric Patient-Derived
Tumor-Cell Spheroids,
MedImg(41), No. 12, December 2022, pp. 3981-3999.
IEEE DOI
2212
Drugs, Cancer, Image segmentation, Computer architecture,
Microprocessors, Tumors, Molecular and cellular imaging, translation
BibRef
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First Demonstration of Compton Camera Used for X-Ray Fluorescence
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MedImg(42), No. 5, May 2023, pp. 1314-1324.
IEEE DOI
2305
Photonics, Cameras, Imaging, X-ray imaging, Scattering, Detectors,
Image reconstruction, Compton camera, X-ray fluorescence (XRF),
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Chen, G.[Ge],
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Fluorescence Microscopy Images Denoising Via Deep Convolutional
Sparse Coding,
SP:IC(117), 2023, pp. 117003.
Elsevier DOI
2308
Deep convolutional neural network,
Fluorescence microscopy images denoising, Deep learning
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Wu, Y.S.[Yan-Song],
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model for dynamic fluorescence molecular tomography,
JOSA-A(41), No. 10, October 2024, pp. 1846-1855.
DOI Link
2503
Biomedical imaging, Imaging systems, Imaging techniques,
Numerical simulation, Optical imaging, Three dimensional reconstruction
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2503
Absorption coefficient, Biomedical imaging,
Image reconstruction, Imaging techniques, Inverse design, Light propagation
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Directional and Topological Transformer with Topology Priors for 4D
Cellular Image Segmentation,
ICIP24(2902-2908)
IEEE DOI
2411
Image segmentation, Embryo, Shape, Manuals, Fluorescence, Transformers,
cell segmentation, 4D image, transformer,
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2401
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Springer DOI
2307
BibRef
Huang, L.J.[Luo-Jie],
Liu, Y.K.[Yi-Kang],
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Robust Landmark-Based Stent Tracking in X-ray Fluoroscopy,
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Springer DOI
2211
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Ahmad, A.[Ali],
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Sorting cells from fluorescent markers organization in confocal
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IPTA20(1-6)
IEEE DOI
2206
Microscopy, Organizations, Fluorescence, Task analysis, Lenses,
Confocal microscopy, fluorescent markers organization, texture,
point spread function
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Cooke, C.L.[Colin L.],
Kong, F.[Fanjie],
Chaware, A.[Amey],
Zhou, K.C.[Kevin C.],
Kim, K.[Kanghyun],
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Ando, D.M.[D. Michael],
Yang, S.J.[Samuel J.],
Konda, P.C.[Pavan Chandra],
Horstmeyer, R.[Roarke],
Physics-Enhanced Machine Learning for Virtual Fluorescence Microscopy,
ICCV21(3783-3793)
IEEE DOI
2203
Training, Optical microscopy, Microscopy, Lighting, Fluorescence,
Optical fiber networks, Optical imaging, Medical, biological,
Vision applications and systems
BibRef
Maksoud, S.,
Zhao, K.,
Hobson, P.,
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SOS: Selective Objective Switch for Rapid Immunofluorescence Whole
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CVPR20(3861-3870)
IEEE DOI
2008
Feature extraction, Spatial resolution, Switches, Task analysis,
Training, Protocols
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Sekh, A.A.,
Opstad, I.S.[I. Sundvor],
Birgisdottir, Ĺ.B.,
Myrmel, T.,
Ahluwalia, B.S.,
Agarwal, K.,
Prasad, D.K.,
Learning Nanoscale Motion Patterns of Vesicles in Living Cells,
CVPR20(14011-14020)
IEEE DOI
2008
Microscopy, Nanoscale devices, Nanobioscience, Videos, Fluorescence,
Tracking, Optical microscopy
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Zhang, Y.[Yide],
Zhu, Y.[Yinhao],
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A Poisson-Gaussian Denoising Dataset With Real Fluorescence Microscopy
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IEEE DOI
2002
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Koyamatsu, K.[Koji],
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Reflective and Fluorescent Separation Under Narrow-Band Illumination,
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IEEE DOI
2002
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Li, D.,
Barbu, A.,
Training a CNN for Guidewire Detection,
ICIP19(2214-2218)
IEEE DOI
1910
guidewire detection, fluoroscopy, convo-lutional neural networks,
spherical quadrature filters
BibRef
Sluzewski, M.F.[M. Filip],
Tvrdik, P.[Petr],
Acton, S.T.[Scott T.],
Segmentation of Cortical Spreading Depression Wavefronts Through
Local Similarity Metric,
ICIP19(1485-1489)
IEEE DOI
1910
Biomedical Image Analysis, Image and Video Segmentation,
Microscopy, Neuroimaging
BibRef
Maka, M.,
Necasová, T.,
Wiesner, D.,
Sorokin, D.V.,
Peterlík, I.,
Ulman, V.,
Svoboda, D.,
Toward Robust Fully 3D Filopodium Segmentation and Tracking in
Time-Lapse Fluorescence Microscopy,
ICIP19(819-823)
IEEE DOI
1910
Benchmark dataset, synthetic image data, filopodium segmentation,
filopodium tracking
BibRef
Shao, H.,
Wang, Y.,
Chen, Y.,
A Two-Phase Segmentation Method for Drosophila Olfactory Glomeruli,
ICIP19(265-269)
IEEE DOI
1910
olfactory glomerulus, confocal fluorescence image,
pseudo-background, segmentation.
BibRef
Abreu, A.,
Frenoist, F.X.,
Valitutti, S.,
Brousset, P.,
Denčfle, P.,
Naegel, B.,
Wemmert, C.,
Model-based graph segmentation in 2-D fluorescence microsecopy images,
ICPR18(3844-3849)
IEEE DOI
1812
Image segmentation, Vegetation, Clustering algorithms,
Task analysis, Image edge detection, Microscopy, Forestry
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Abousamra, S.,
Adar, S.,
Elia, N.,
Shilkrot, R.,
Localization and Tracking in 4D Fluorescence Microscopy Imagery,
Microscopy18(2371-23718)
IEEE DOI
1812
Microscopy, Bridges,
Task analysis, Embryo, Image segmentation
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Asano, Y.[Yuta],
Meguro, M.[Misaki],
Wang, C.[Chao],
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Coded Illumination and Imaging for Fluorescence Based Classification,
ECCV18(VIII: 511-526).
Springer DOI
1810
BibRef
On, V.,
Zahedi, A.,
Bhanu, B.,
3D Reconstruction of Phase Contrast Images Using Focus Measures,
ICIP18(101-105)
IEEE DOI
1809
Fluorescence, Microscopy,
Regression tree analysis, Lung, Image segmentation,
Regression
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Levine, J.,
Grangetto, M.,
Varrecchia, M.,
Olmo, G.,
Detection and Tracking of Astral Microtubules in Fluorescence
Microscopy Images,
ICIP18(361-365)
IEEE DOI
1809
Microscopy, Standards, Image segmentation, Signal to noise ratio,
Heuristic algorithms, Fluorescence, Drugs,
microtubules
BibRef
Nourbakhsh, F.,
Abdeladim, L.,
Clavreul, S.,
Loulier, K.,
Beaurepaire, E.,
Livet, J.,
Chessel, A.,
Neural Cell Segmentation in Large-Scale 3D Color Fluorescence
Microscopy Images for Developemental Neuroscience,
ICIP18(3828-3832)
IEEE DOI
1809
Image segmentation, Neurons,
Computer architecture, Microscopy, Microprocessors,
segmentation
BibRef
Feng, Y.,
Chai, X.,
Ba, Q.,
Yang, G.,
Quality Assessment of Synthetic Fluorescence Microscopy Images for
Image Segmentation,
ICIP19(814-818)
IEEE DOI
1910
Quality assessment, synthetic image, fluorescence microscopy,
generative adversarial network, image segmentation
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Chai, X.,
Ba, Q.,
Yang, G.,
Characterizing Robustness and Sensitivity of Convolutional Neural
Networks in Segmentation of Fluorescence Microscopy Images,
ICIP18(3838-3842)
IEEE DOI
1809
Image segmentation, Robustness, Sensitivity, Microscopy,
Active appearance model, Biology, Training, Segmentation,
mitochondria
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Wu, L.M.[Li-Ming],
Han, S.[Shuo],
Chen, A.[Alain],
Salama, P.[Paul],
Dunn, K.W.[Kenneth W.],
Delp, E.J.[Edward J.],
RCNN-SliceNet: A Slice and Cluster Approach for Nuclei Centroid
Detection in Three-Dimensional Fluorescence Microscopy Images,
CVMI21(3750-3760)
IEEE DOI
2109
Location awareness, Training, Solid modeling, Image segmentation,
Microscopy, Object detection
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Fu, C.,
Lee, S.,
Ho, D.J.,
Han, S.[Shuo],
Salama, P.[Paul],
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Delp, E.J.[Edward J.],
Three Dimensional Fluorescence Microscopy Image Synthesis and
Segmentation,
Microscopy18(2302-23028)
IEEE DOI
1812
Image segmentation, Microscopy,
Solid modeling, Mathematical model
BibRef
Ho, D.J.,
Fu, C.,
Salama, P.[Paul],
Dunn, K.W.[Kenneth W.],
Delp, E.J.[Edward J.],
Nuclei Segmentation of Fluorescence Microscopy Images Using Three
Dimensional Convolutional Neural Networks,
Microscopy17(834-842)
IEEE DOI
1709
Electron microscopy, Fluorescence, Image segmentation,
Training, Two, dimensional, displays
BibRef
Lorenz, K.S.[Kevin S.],
Salama, P.[Paul],
Dunn, K.W.[Kenneth W.],
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Three dimensional segmentation of fluorescence microscopy images
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ICIP13(1153-1157)
IEEE DOI
1402
Active contours
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Osokin, A.[Anton],
Chessel, A.[Anatole],
Carazo Salas, R.E.[Rafael E.],
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GANs for Biological Image Synthesis,
ICCV17(2252-2261)
IEEE DOI
1802
Generative Adversarial Networks.
Synthesis of these images.
biomedical optical imaging, cellular biophysics,
medical image processing, optical microscopy, proteins, GAN,
BibRef
Aydin, A.S.,
Dubey, A.,
Dovrat, D.,
Aharoni, A.,
Shilkrot, R.,
CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent
Microscopy Data,
Microscopy17(753-759)
IEEE DOI
1709
Computer architecture, DNA, Image segmentation, Lighting,
Microprocessors, Microscopy
BibRef
Gupta, A.[Anindya],
Suveer, A.[Amit],
Lindblad, J.[Joakim],
Dragomir, A.[Anca],
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Convolutional Neural Networks for False Positive Reduction of
Automatically Detected Cilia in Low Magnification TEM Images,
SCIA17(I: 407-418).
Springer DOI
1706
BibRef
Kondratev, A.Y.[Alexandr Y.],
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Automatic detection of laser-induced structures in live cell
fluorescent microscopy images using snakes with geometric constraints,
ICPR16(331-336)
IEEE DOI
1705
Active contours, Algorithm design and analysis, Bleaching,
Feature extraction, Image registration, Image sequences, Microscopy
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Holden, M.[Mark],
Intramolecular FRET Efficiency Measures for Time-Lapse Fluorescence
Microscopy Images,
ACIVS16(105-116).
Springer DOI
1611
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Smal, I.[Ihor],
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Poisson Point Processes for Solving Stochastic Inverse Problems in
Fluorescence Microscopy,
BioImage16(I: 326-338).
Springer DOI
1611
BibRef
Puybareau, E.,
Talbot, H.,
Bequignon, E.,
Louis, B.,
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Papon, J.F.,
Coste, A.,
Najman, L.,
Automating the measurement of physiological parameters:
A case study in the image analysis of cilia motion,
ICIP16(1240-1244)
IEEE DOI
1610
Biomedical measurement
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Yu, Y.,
Wang, J.,
Acton, S.T.,
Automatic detection of direct radiation for digital fluoroscopy
optimization,
ICIP16(3379-3383)
IEEE DOI
1610
Feature extraction
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Ohara, N.,
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Sato, I.,
Nakamura, T.,
Yamaguchi, M.,
Simultaneous linear separation and unmixing of fluorescent and
reflective components from a single hyperspectral image,
ICIP16(4344-4348)
IEEE DOI
1610
Decision support systems
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Craciun, P.,
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Stochastic geometry for multiple object tracking in fluorescence
microscopy,
ICIP16(444-448)
IEEE DOI
1610
Biological system modeling
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1610
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Koos, K.,
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DIC Microscopy Image Reconstruction Using a Novel Variational
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DICTA15(1-7)
IEEE DOI
1603
image reconstruction
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Samak, A.,
Wiliem, A.,
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Walsh, M.,
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Barksdale, S.,
Edwards, R.,
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Lovell, B.C.,
An Optimization Approach to Scanning Skin Direct Immunofluorescence
Specimens,
DICTA15(1-8)
IEEE DOI
1603
fluorescence
BibRef
Gharipour, A.,
Liew, A.W.C.,
A Multi-Kernel Local Level Set Image Segmentation Algorithm for
Fluorescence Microscopy Images,
DICTA15(1-5)
IEEE DOI
1603
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DICTA15(1-5)
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1603
fluorescence
biology computing
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ICIP15(4496-4500)
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1512
Beating cilia
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fluorescence; hyperspectral imaging; relighting
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Neves, J.C.[Joăo C.],
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Fluorescent Analysis of Photosynthetic Microbes and Polycyclic Aromatic
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A Human Inspired Local Ratio-Based Algorithm for Edge Detection in
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A Hypothesis Testing Approach for Fluorescent Blob Identification,
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Multivariate Skew t-Mixture Models:
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0912
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Krämer, P.,
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Comparison of Segmentation Algorithms for the Zebrafish Heart in
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ISVC09(II: 1041-1050).
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0911
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Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Tracking Cells, Deformations, Motion, Real-Time Analysis .