21.4.4 Extraction and Analysis of Cells

Chapter Contents (Back)
Cells. Cell Extraction. Cell Segmentation.
See also Extraction and Analysis of Neurons.
See also Tracking Cells, Deformations, Motion, Real-Time Analysis.
See also HIV, HIV/AIDS Cell Analysis.
See also HEp-2 Cell Analysis, Human Epithelial Type 2 Cells.
See also 3-D Cell Analysis.
See also Extraction and Analysis Bacteria.
See also Diatoms.

Ledley, R.S.,
Analysis of Cells,
TC(21), No. 7, July 1972, pp. 740-752. BibRef 7207

Patrick, E.A.[Edward A.], Altman, J.[Joseph], Wild, R.[Richard],
Computer output display of cells and cell features,
PR(4), No. 2, May 1972, pp. 211-226.
Elsevier DOI 0309
BibRef

Young, I.T., and Paskowitz, I.L.,
Localization of cellular structures,
BiomedEng(22), No. 1, 1975, pp. 35-40. BibRef 7500

Carayannopoulos, G.L., Patrick, E.A.,
An algorithm for segmentation of metaphase spreads,
PR(8), No. 3, July 1976, pp. 151-161.
Elsevier DOI 0309
BibRef

Tajima, J.[Johji],
Automatic classification of micro-organisms by shape and color, in water quality control: A preliminary study,
PR(14), No. 1-6, 1981, pp. 211-217.
Elsevier DOI 0309
BibRef

Betz, J.W., Sengbusch, G.V., Ärtel, W.H.,
Low resolution slit-scan pattern recognition applying one-dimensional multiparameter analysis,
PR(13), No. 1, 1981, pp. 89-93.
Elsevier DOI 0309
rapid pre-screening of gynecological cell samples. BibRef

Sherman, A.B.[Andrew B.], Koss, L.G.[Leopold G.], Abbott, M.C.[Mark C.], Liao, M.W.[Mary W.],
A method of boundary determination in digital images of urothelial cells,
PR(13), No. 4, 1981, pp. 285-291.
Elsevier DOI 0309
BibRef

Meyer, F.,
Automatic Screening of Cytological Specimens,
CVGIP(35), No. 3, 1987, pp. 356-369.
Elsevier DOI Morphology. Application of morphology technique. TopHat filter. BibRef 8700

Lester, J.M.[James M.], Brenner, J.F.[John F.], Selles, W.D.[William D.],
Local transforms for biomedical image analysis,
CGIP(13), No. 1, May 1980, pp. 17-30.
Elsevier DOI 0501
BibRef

Zimmer, H.G.[Hans-Georg], Kronberg, H.[Harald], Bernstein, R.[Reinhard], Neuhoff, V.[Volker],
Improvements in microphotometry by digital signal processing,
PR(13), No. 1, 1981, pp. 79-82.
Elsevier DOI 0309
BibRef

Rüter, A., Harms, H., Aus, H.M.,
Standardized color measurement in automated cytophotometry with the light micrscope,
PR(13), No. 4, 1981, pp. 315-323.
Elsevier DOI 0309
BibRef

Harms, H., Rüter, A., Aus, H.M.,
A microprocessor-controlled axiomat microscope for acquisition of cell images,
PR(13), No. 4, 1981, pp. 325-329.
Elsevier DOI 0309
BibRef

Preston, Jr., K.[Kendall],
Tissue section analysis: Feature selection and image processing,
PR(13), No. 1, 1981, pp. 17-36.
Elsevier DOI 0309
BibRef

Preston, Jr., K.[Kendall],
Digital picture analysis in cytology,
DPA76(209-294).
Springer DOI BibRef 7600

White, B.S.[Benjamin S.], Castleman, K.R.[Kenneth R.],
Estimating cell populations,
PR(13), No. 5, 1981, pp. 365-370.
Elsevier DOI 0309
BibRef

Jordan, M.M., Perkins, W.J.,
Use of Models in Studying Macrophage Cell Images and the Underlying Biological Processes,
IVC(5), No. 2, May 1987, pp. 139-144.
Elsevier DOI BibRef 8705

Murray, C.[Carl], O'Malley, M.[Mark],
Segmentation of plant cell pictures,
IVC(11), No. 3, April 1993, pp. 155-162.
Elsevier DOI 0401
BibRef

Wu, K., Gauthier, D., and Levine, M.,
Live cell image segmentation,
BiomedEng(42), No. 1, 1995, pp. 1-12. BibRef 9500

Wu, H.S., Gil, J., Barba, J.,
Optimal Segmentation of Cell Images,
VISP(145), No. 1, February 1998, pp. 50-56. 9804
BibRef

Garrido, A., de la Blanca, N.P.[N. Pérez],
Applying deformable templates for cell image segmentation,
PR(33), No. 5, May 2000, pp. 821-832.
Elsevier DOI 0003
BibRef

Garrido Carrido, A.[Antonio], de la Blanca, N.P.[N. Pérez], and García-Silvente, M.,
A new methodology to automatically segment biomedical images,
CIAP97(II: 372-379).
Springer DOI 9709
BibRef
And:
Cell Image Segmentation,
SCIA97(xx-yy)
HTML Version. 9705
BibRef

Strijk, T.[Tycho], van Kreveld, M.[Marc],
Practical Extensions of Point Labeling in the Slider Model,
GeoInfo(6), No. 2, June 2002, pp. 181-197.
DOI Link Placing labels for a point feature. 0205
BibRef

Lessman, C.A.[Charles A.],
Use of Computer-Aided Screening for Detection of Motility Mutants in Zebrafish Embryos,
RealTimeImg(8), No. 3, June 2002, pp. 189-201.
DOI Link 0208
BibRef

Jiang, T.Z.[Tian-Zi], Yang, F.[Faguo],
An evolutionary tabu search for cell image segmentation,
SMC-B(32), No. 5, October 2002, pp. 675-678.
IEEE Top Reference. 0210
BibRef

Fiori, S.,
Information-theoretic learning for FAN network applied to eterokurtic component analysis,
VISP(149), No. 6, December 2002, pp. 347-354.
IEEE Top Reference. BibRef 0212
And: Erratum: VISP(150), No. 6, December 2003, pp. 370-370.
IEEE Abstract. 0304
BibRef

Zhang, B., Fadili, J.M., Starck, J.L.,
Wavelets, Ridgelets, and Curvelets for Poisson Noise Removal,
IP(17), No. 7, July 2008, pp. 1093-1108.
IEEE DOI 0806

See also Undecimated Wavelet Decomposition and its Reconstruction, The. BibRef

Long, X.[Xi], Cleveland, W.L.[W. Louis], Yao, Y.L.[Y. Lawrence],
Effective automatic recognition of cultured cells in bright field images using fisher's linear discriminant preprocessing,
IVC(23), No. 13, 29 November 2005, pp. 1203-1213.
Elsevier DOI 0512
BibRef

Long, X.[Xi], Cleveland, W.L.[W. Louis], Yao, Y.L.[Y. Lawrence],
Multiclass cell detection in bright field images of cell mixtures with ECOC probability estimation,
IVC(26), No. 4, April 2008, pp. 578-591.
Elsevier DOI 0711
Cell detection; Error Correcting Output Coding (ECOC); Multiclass classification; Support vector machines BibRef

Hodgson, S.[Simon], Harrison, R.F.[Robert F.], Cross, S.S.[Simon S.],
An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies,
IVC(24), No. 9, September 2006, pp. 1025-1038.
Elsevier DOI 0608
Liver biopsy analysis; Bayesian decision theory; Gaussian mixture models; Sequential forward floating search BibRef

Cho, S.B.[Sung-Bae], Yoo, S.H.[Si-Ho],
Fuzzy Bayesian validation for cluster analysis of yeast cell-cycle data,
PR(39), No. 12, December 2006, pp. 2405-2414.
Elsevier DOI 0609
Fuzzy clustering; Fuzzy c-means algorithm; Fuzzy Bayesian validation method; Yeast cell-cycle data BibRef

Ranzato, M., Taylor, P.E., House, J.M., Flagan, R.C., Le Cun, Y.L., Perona, P.,
Automatic recognition of biological particles in microscopic images,
PRL(28), No. 1, 1 January 2007, pp. 31-39.
Elsevier DOI 0611
Biological particles; Feature; Non-linearity; Recognition; Cells; Pollen; Mixture of Gaussians (MoG) BibRef

Chan, Y.K.[Yung-Kuan], Liau, D.L.[Duan-Li], Chen, Y.F.[Yung-Fu], Wu, H.C.[Hsien-Chu], Chu, Y.P.[Yen-Ping],
A minute lossy method for 2D-gel images compression,
IJIST(16), No. 1, 2006, pp. 1-8.
DOI Link 0703
BibRef

Castanon, C.A.B.[Cesar A.B.], Fraga, J.S.[Jane S.], Fernandez, S.[Sandra], Gruber, A.[Arthur], da Fontoura Costa, L.[Luciano],
Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria,
PR(40), No. 7, July 2007, pp. 1899-1910.
Elsevier DOI 0704
Shape analysis; Feature extraction; Pattern classification; Image processing; Remote diagnosis; Real-time systems; Eimeria; Avian coccidiosis BibRef

Mosaliganti, K., Janoos, F., Sharp, R., Ridgway, R., Machiraju, R., Huang, K., Wenzel, P., de Bruin, A., Leone, G., Saltz, J.,
Detection and Visualization of Surface-Pockets to Enable Phenotyping Studies,
MedImg(26), No. 9, September 2007, pp. 1283-1290.
IEEE DOI 0710
BibRef

Jafari-Khouzani, K., Soltanian-Zadeh, H., Fotouhi, F., Parrish, J.R., Finley, Jr., R.L.,
Automated Segmentation and Classification of High Throughput Yeast Assay Spots,
MedImg(26), No. 10, October 2007, pp. 1401-1411.
IEEE DOI 0711
BibRef

Alcantara, D.A.[Dan A.], Carmichael, O.T.[Owen T.], Harcourt-Smith, W.[Will], Sterner, K.[Kirstin], Frost, S.R.[Stephen R.], Dutton, R.A.[Rebecca A.], Thompson, P.[Paul], Delson, E.[Eric], Amenta, N.[Nina],
Exploration of Shape Variation Using Localized Components Analysis,
PAMI(31), No. 8, August 2009, pp. 1510-1516.
IEEE DOI 0906
Shape variations of human brain regions. Preserves local variation. BibRef

Shah, S.K.[Shishir K.],
Automatic Cell Segmentation Using a Shape-Classification Model in Immunohistochemically Stained Cytological Images,
IEICE(E91-D), No. 7, July 2008, pp. 1955-1962.
DOI Link 0807
BibRef

Patterson, J., Stayton, P.S., Li, X.,
In Situ Characterization of the Degradation of PLGA Microspheres in Hyaluronic Acid Hydrogels by Optical Coherence Tomography,
MedImg(28), No. 1, January 2009, pp. 74-81.
IEEE DOI 0901
BibRef

Luengo-Oroz, M.A.[Miguel A.], Angulo, J.[Jesús],
Cyclic Mathematical Morphology in Polar-Logarithmic Representation,
IP(18), No. 5, May 2009, pp. 1090-1096.
IEEE DOI 0904

See also Hypercomplex Mathematical Morphology. BibRef

Luengo-Oroz, M.A.[Miguel A.], Angulo, J.[Jesús], Flandrin, G.[Georges], Klossa, J.[Jacques],
Mathematical Morphology in Polar-Logarithmic Coordinates. Application to Erythrocyte Shape Analysis,
IbPRIA05(II:199).
Springer DOI 0509
BibRef

Kumar, S., Ong, S.H., Ranganath, S., Chew, F.T.,
Invariant texture classification for biomedical cell specimens via non-linear polar map filtering,
CVIU(114), No. 1, January 2010, pp. 44-53.
Elsevier DOI 1001
Biomedical cells; Contrast-invariant; Polar map; Rotation invariant; Scale-invariant; Texture classification; Support vector machine BibRef

Kumar, S., Ong, S.H., Ranganath, S., Chew, F.T., Ong, T.C.,
Segmentation of microscope cell images via adaptive eigenfilters,
ICIP04(I: 135-138).
IEEE DOI 0505
BibRef

Smal, I., Grigoriev, I., Akhmanova, A., Niessen, W.J., Meijering, E.H.W.,
Microtubule Dynamics Analysis Using Kymographs and Variable-Rate Particle Filters,
IP(19), No. 7, July 2010, pp. 1861-1876.
IEEE DOI 1007
intracellular dynamics. BibRef

Wu, H.S., Fiel, M.I., Schiano, T.D., Ramer, M., Burstein, D., Gil, J.,
Segmentation of textured cell images based on frequency analysis,
IET-IPR(5), No. 2, April 2011, pp. 148-158.
DOI Link 1103
BibRef

Gallego, M.Á.[M. Ángeles], Ibáñez, M.V.[M. Victoria], Simó, A.[Amelia],
Non-homogeneous temporal Boolean models to study endocytosis,
PR(45), No. 4, April 2012, pp. 1245-1254.
Elsevier DOI 1112
Temporal Boolean model; Endocytosis; Spatial non-homogeneity; Germ-grain model; Parameter estimation BibRef

Gu, Q.Y.[Qing-Yi], Takaki, T.[Takeshi], Ishii, I.[Idaku],
A Fast Multi-Object Extraction Algorithm Based on Cell-Based Connected Components Labeling,
IEICE(E95-D), No. 2, February 2012, pp. 636-645.
WWW Link. 1202
BibRef
Earlier:
2000-fps multi-object extraction based on cell-based labeling,
ICIP10(3761-3764).
IEEE DOI 1009
Label cells, scan image once for moment features. BibRef

Gu, Q.Y.[Qing-Yi], Takaki, T.[Takeshi], Ishii, I.[Idaku],
Fast FPGA-Based Multiobject Feature Extraction,
CirSysVideo(23), No. 1, January 2013, pp. 30-45.
IEEE DOI 1302
BibRef

Hagwood, C., Bernal, J., Halter, M., Elliott, J.,
Evaluation of Segmentation Algorithms on Cell Populations Using CDF Curves,
MedImg(31), No. 2, February 2012, pp. 380-390.
IEEE DOI 1202
BibRef

Glaß, M.[Markus], Möller, B.[Birgit], Zirkel, A.[Anne], Wächter, K.[Kristin], Hüttelmaier, S.[Stefan], Posch, S.[Stefan],
Cell migration analysis: Segmenting scratch assay images with level sets and support vector machines,
PR(45), No. 9, September 2012, pp. 3154-3165.
Elsevier DOI 1206
BibRef
Earlier:
Scratch Assay Analysis with Topology-Preserving Level Sets and Texture Measures,
IbPRIA11(100-108).
Springer DOI 1106
Scratch assay; Segmentation; Level sets; Texture; Topology; SVM BibRef

Theriault, D.H.[Diane H.], Walker, M.L.[Matthew L.], Wong, J.Y.[Joyce Y.], Betke, M.[Margrit],
Cell morphology classification and clutter mitigation in phase-contrast microscopy images using machine learning,
MVA(23), No. 4, July 2012, pp. 659-673.
WWW Link. 1206
BibRef

Deepak, K.S.[K. Sai], Medathati, N.V.K.[N.V. Kartheek], Sivaswamy, J.[Jayanthi],
Detection and discrimination of disease-related abnormalities based on learning normal cases,
PR(45), No. 10, October 2012, pp. 3707-3716.
Elsevier DOI 1206
Abnormality detection; Computer-aided diagnosis; Learning normal; Medical images; Shape descriptor; Texture descriptor BibRef

Meijering, E.,
Cell Segmentation: 50 Years Down the Road,
SPMag(29), No. 5, 2012, pp. 140-145.
IEEE DOI 1209
Survey, Cell Segmentation. Life Sciences. Review of the results. BibRef

Randell, D.A.[David A.], Landini, G.[Gabriel], Galton, A.,
Discrete Mereotopology for Spatial Reasoning in Automated Histological Image Analysis,
PAMI(35), No. 3, March 2013, pp. 568-581.
IEEE DOI 1303
parts and topology to model space. BibRef

Nozaka, H.[Hiroyuki], Miura, T.[Tomisato], Zheng, Z.X.[Zhong-Xi],
Multi-Layer Virtual Slide Scanning System with Multi-Focus Image Fusion for Cytopathology and Image Diagnosis,
IEICE(E96-D), No. 4, April 2013, pp. 856-863.
WWW Link. 1304
BibRef

Ferro, L.[Luís], Marques, M.[Marco], Leal, P.[Pedro], Romão, S.[Susana], Cruz, T.[Tânia], Tomás, A.M.[Ana M.],
Automatic Spectral Unmixing of Leishmania Infection Macrophage Cell Cultures Image,
ICIAR13(621-629).
Springer DOI 1307
BibRef

Leal, P.[Pedro], Ferro, L.[Luís], Marques, M.[Marco], Romão, S.[Susana], Cruz, T.[Tânia], Tomá, A.M.[Ana M.], Castro, H.[Helena], Quelhas, P.[Pedro],
Automatic Assessment of Leishmania Infection Indexes on In Vitro Macrophage Cell Cultures,
ICIAR12(II: 432-439).
Springer DOI 1206
BibRef

Filipczuk, P.[Pawel], Krawczyk, B.[Bartosz], Wozniak, M.[Michal],
Classifier ensemble for an effective cytological image analysis,
PRL(34), No. 14, 2013, pp. 1748-1757.
Elsevier DOI 1308
Pattern recognition BibRef

White, A.G., Lees, B., Kao, H.L.[Huey-Ling], Cipriani, P.G., Munarriz, E., Paaby, A.B., Erickson, K., Guzman, S., Rattanakorn, K., Sontag, E., Geiger, D., Gunsalus, K.C., Piano, F.,
DevStaR: High-Throughput Quantification of C. elegans Developmental Stages,
MedImg(32), No. 10, 2013, pp. 1791-1803.
IEEE DOI 1311
biology computing BibRef

Badsha, S., Mokhtar, N., Arof, H., Lim, Y.A., Mubin, M., Ibrahim, Z.,
Automatic Cryptosporidium and Giardia viability detection in treated water,
JIVP(2013), No. 1, 2013, pp. 56.
DOI Link 1311
BibRef

Mualla, F., Scholl, S., Sommerfeldt, B., Maier, A., Hornegger, J.,
Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering,
MedImg(32), No. 12, 2013, pp. 2274-2286.
IEEE DOI 1312
Couplings BibRef

Hagwood, C., Bernal, J., Halter, M., Elliott, J., Brennan, T.,
Testing Equality of Cell Populations Based on Shape and Geodesic Distance,
MedImg(32), No. 12, 2013, pp. 2230-2237.
IEEE DOI 1312
Extraterrestrial measurements BibRef

Comic, L.[Lidija], de Floriani, L.[Leila], Iuricich, F.[Federico], Fugacci, U.[Ulderico],
Topological modifications and hierarchical representation of cell complexes in arbitrary dimensions,
CVIU(121), No. 1, 2014, pp. 2-12.
Elsevier DOI 1404
BibRef
Earlier: A1, A2, Only:
Topological Operators on Cell Complexes in Arbitrary Dimensions,
CTIC12(98-107).
Springer DOI 1206
Computational topology BibRef

Fachada, N.[Nuno], Figueiredo, M.A.T.[Mário A.T.], Lopes, V.V.[Vitor V.], Martins, R.C.[Rui C.], Rosa, A.C.[Agostinho C.],
Spectrometric differentiation of yeast strains using minimum volume increase and minimum direction change clustering criteria,
PRL(45), No. 1, 2014, pp. 55-61.
Elsevier DOI 1407
Clustering BibRef

Chang, H.[Hang], Wen, Q.[Quan], Parvin, B.[Bahram],
Coupled segmentation of nuclear and membrane-bound macromolecules through voting and multiphase level set,
PR(48), No. 3, 2015, pp. 882-893.
Elsevier DOI 1412
Segmentation of membrane-bound macromolecules BibRef

Ortiz-de-Solórzano, C., Muñoz-Barrutia, A., Meijering, E., Kozubek, M.,
Toward a Morphodynamic Model of the Cell: Signal processing for cell modeling,
SPMag(32), No. 1, January 2015, pp. 20-29.
IEEE DOI 1502
biomechanics BibRef

Ziraldo, R., Link, N., Abrams, J., Ma, L.[Lan],
Towards automatic image analysis and assessment of the multicellular apoptosis process,
IET-IPR(9), No. 5, 2015, pp. 424-433.
DOI Link 1506
biology computing BibRef

Bise, R., Sato, Y.,
Cell Detection From Redundant Candidate Regions Under Nonoverlapping Constraints,
MedImg(34), No. 7, July 2015, pp. 1417-1427.
IEEE DOI 1507
Image segmentation BibRef

Saito, P.T.M.[Priscila T.M.], Suzuki, C.T.N.[Celso T.N.], Gomes, J.F.[Jancarlo F.], de Rezende, P.J.[Pedro J.], Falcão, A.X.[Alexandre X.],
Robust active learning for the diagnosis of parasites,
PR(48), No. 11, 2015, pp. 3572-3583.
Elsevier DOI 1506
Active learning BibRef

Wang, Z.Z.[Zhen-Zhou],
A semi-automatic method for robust and efficient identification of neighboring muscle cells,
PR(53), No. 1, 2016, pp. 300-312.
Elsevier DOI 1602
Segmentation BibRef

Su, H., Yin, Z., Huh, S., Kanade, T., Zhu, J.,
Interactive Cell Segmentation Based on Active and Semi-Supervised Learning,
MedImg(35), No. 3, March 2016, pp. 762-777.
IEEE DOI 1603
Complexity theory BibRef

Ahmad, O.[Ola], Collet, C.[Christophe],
Scale-space spatio-temporal random fields: Application to the detection of growing microbial patterns from surface roughness,
PR(58), No. 1, 2016, pp. 27-38.
Elsevier DOI 1606
Spatio-temporal modeling BibRef

Svensson, C.M.[Carl-Magnus], Bondoc, K.G.[Karen Grace], Pohnert, G.[Georg], Figge, M.T.[Marc Thilo],
Segmentation of clusters by template rotation expectation maximization,
CVIU(154), No. 1, 2017, pp. 64-72.
Elsevier DOI 1612
Segmentation of clusters of nearly identical objects. BibRef

Hao, R.Q.[Ru-Qian], Wang, X.Z.[Xiang-Zhou], Zhang, J.[Jing], Liu, J.X.[Juan-Xiu], Ni, G.M.[Guang-Ming], Du, X.[Xiao_Hui], Liu, L.[Lin], Liu, Y.[Yong],
Automatic detection of trichomonads based on an improved Kalman background reconstruction algorithm,
JOSA-A(34), No. 5, May 2017, pp. 752-759.
DOI Link 1705
General Image processing BibRef

Loewke, N.O., Pai, S., Cordeiro, C., Black, D., King, B.L., Contag, C.H., Chen, B., Baer, T.M., Solgaard, O.,
Automated Cell Segmentation for Quantitative Phase Microscopy,
MedImg(37), No. 4, April 2018, pp. 929-940.
IEEE DOI 1804
Adaptive optics, Biomedical optical imaging, Image segmentation, Microscopy, Optical imaging, Optical microscopy, Optical sensors, volumetric distribution BibRef

Tareef, A., Song, Y., Huang, H., Feng, D., Chen, M., Wang, Y., Cai, W.,
Multi-Pass Fast Watershed for Accurate Segmentation of Overlapping Cervical Cells,
MedImg(37), No. 9, September 2018, pp. 2044-2059.
IEEE DOI 1809
Image segmentation, Shape, Microscopy, Task analysis, Image edge detection, Transforms, Active contours, Chan-Vese active contour BibRef

Tareef, A., Song, Y., Lee, M.Z., Feng, D.D., Chen, M., Cai, W.D.,
Morphological Filtering and Hierarchical Deformation for Partially Overlapping Cell Segmentation,
DICTA15(1-7)
IEEE DOI 1603
cellular biophysics BibRef

Zalev, J., Kolios, M.C.,
Image Reconstruction Combined With Interference Removal Using a Mixed-Domain Proximal Operator,
SPLetters(25), No. 12, December 2018, pp. 1840-1844.
IEEE DOI 1812
biological tissues, biomedical optical imaging, gradient methods, image reconstruction, medical image processing, minimisation, proximal operator BibRef

Hiramatsu, Y., Hotta, K., Imanishi, A., Matsuda, M., Terai, K.,
Cell Image Segmentation by Integrating Multiple CNNs,
Microscopy18(2286-22866)
IEEE DOI 1812
Image segmentation, Neural networks, Semantics, Training, Biomembranes, Fluorescence, Kernel BibRef

Awad, S.I.[Samer I.], Abdallat, R.G.[Rula G.], Smadi, O.A.[Othman A.], Al-Momani, T.D.[Thakir D.],
Automated identification and counting of proliferating mesenchymal stem cells in bone callus,
IJCVR(9), No. 1, 2019, pp. 1-13.
DOI Link 1903
BibRef

Winter, M., Mankowski, W., Wait, E., de la Hoz, E.C., Aguinaldo, A., Cohen, A.R.,
Separating Touching Cells Using Pixel Replicated Elliptical Shape Models,
MedImg(38), No. 4, April 2019, pp. 883-893.
IEEE DOI 1904
Image segmentation, Transforms, Task analysis, Fluorescence, Gaussian mixture model, Shape, Segmentation, cell segmentation, segmenting touching objects BibRef

Fehri, H., Gooya, A., Lu, Y., Meijering, E., Johnston, S.A., Frangi, A.F.,
Bayesian Polytrees With Learned Deep Features for Multi-Class Cell Segmentation,
IP(28), No. 7, July 2019, pp. 3246-3260.
IEEE DOI 1906
Bayes methods, directed graphs, image classification, image segmentation, learning (artificial intelligence), error prediction BibRef

Silva, R.R.V.[Romuere R.V.], Araujo, F.H.D.[Flavio H.D.], Ushizima, D.M.[Daniela M.], Bianchi, A.G.C.[Andrea G.C.], Carneiro, C.M.[Claudia M.], Medeiros, F.N.S.[Fatima N.S.],
Radial feature descriptors for cell classification and recommendation,
JVCIR(62), 2019, pp. 105-116.
Elsevier DOI 1908
Radial feature descriptors, Cell classification, Image retrieval, Convolutional neural networks BibRef

Xue, Y., Bigras, G., Hugh, J., Ray, N.,
Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection,
MedImg(38), No. 11, November 2019, pp. 2632-2641.
IEEE DOI 1911
Computer architecture, Microprocessors, Encoding, Microscopy, Training, Compressed sensing, Backpropagation, Cell detection, CNN, end-to-end training BibRef

Banerjee, S.[Sriparna], Chaudhuri, S.S.[Sheli Sinha],
Total contribution score and fuzzy entropy based two-stage selection of FC, ReLU and inverseReLU features of multiple convolution neural networks for erythrocytes detection,
IET-CV(13), No. 7, Octomber 2019, pp. 640-650.
DOI Link 1911
BibRef

Liu, Y., Yuan, H., Wang, Z., Ji, S.,
Global Pixel Transformers for Virtual Staining of Microscopy Images,
MedImg(39), No. 6, June 2020, pp. 2256-2266.
IEEE DOI 2006
Microscopy, Task analysis, Predictive models, Convolution, Fuses, Tensors, Computational modeling, Cellular structures, multi-scale inputs BibRef

Borovec, J., Kybic, J., Arganda-Carreras, I., Sorokin, D.V., Bueno, G., Khvostikov, A.V., Bakas, S., Chang, E.I.C., Heldmann, S., Kartasalo, K., Latonen, L., Lotz, J., Noga, M., Pati, S., Punithakumar, K., Ruusuvuori, P., Skalski, A., Tahmasebi, N., Valkonen, M., Venet, L., Wang, Y., Weiss, N., Wodzinski, M., Xiang, Y., Xu, Y., Yan, Y., Yushkevich, P., Zhao, S., Muñoz-Barrutia, A.,
ANHIR: Automatic Non-Rigid Histological Image Registration Challenge,
MedImg(39), No. 10, October 2020, pp. 3042-3052.
IEEE DOI 2010
Image registration, Microscopy, Lung, Magnetic resonance imaging, IEEE Senior Members, Robustness, Image resolution, microscopy BibRef

Huang, Y.L.[Ya-Li], Hu, X.F.[Xue-Fang], Hao, L.[Lei], Gao, Y.H.[Yue-Hua], Liu, Z.W.[Zhi-Wen], Wang, P.G.[Pei-Guang],
Quantitative analysis of cell morphology based on the contourlet transform,
IET-IPR(14), No. 12, October 2020, pp. 2826-2832.
DOI Link 2010
BibRef

Zhang, J.H.[Jing-Hua], Li, C.[Chen], Kosov, S.[Sergey], Grzegorzek, M.[Marcin], Shirahama, K.[Kimiaki], Jiang, T.[Tao], Sun, C.[Changhao], Li, Z.[Zihan], Li, H.[Hong],
LCU-Net: A novel low-cost U-Net for environmental microorganism image segmentation,
PR(115), 2021, pp. 107885.
Elsevier DOI 2104
Environmental miroorganisms, Image segmentation, Deep convolutional neural networks, Low-cost BibRef

Mitra, S.[Shyamali], Das, N.[Nibaran], Dey, S.[Soumyajyoti], Chakraborty, S.[Sukanta], Nasipuri, M.[Mita], Naskar, M.K.[Mrinal Kanti],
Cytology Image Analysis Techniques Toward Automation: Systematically Revisited,
Surveys(54), No. 3, April 2021, pp. xx-yy.
DOI Link 2106
Survey, Cytology. Image classification, image segmentation, computer aided diagnosis, malignant and benign BibRef

Liu, J.F.[Jian-Fei], Shen, C.[Christine], Aguilera, N.[Nancy], Cukras, C.[Catherine], Hufnagel, R.B.[Robert B.], Zein, W.M.[Wadih M.], Liu, T.[Tao], Tam, J.[Johnny],
Active Cell Appearance Model Induced Generative Adversarial Networks for Annotation-Efficient Cell Segmentation and Identification on Adaptive Optics Retinal Images,
MedImg(40), No. 10, October 2021, pp. 2820-2831.
IEEE DOI 2110
Image segmentation, Shape, Adaptive optics, Annotations, Retina, Adaptation models, Training data, Active appearance model, generative adversarial networks BibRef

Zhao, T.Y.[Tian-Yi], Yin, Z.Z.[Zhao-Zheng],
Weakly Supervised Cell Segmentation by Point Annotation,
MedImg(40), No. 10, October 2021, pp. 2736-2747.
IEEE DOI 2110
Image segmentation, Annotations, Training, Neural networks, Task analysis, Deep learning, Computer architecture, human in the loop BibRef

Wang, J.[Jie], Zhang, M.X.[Ming-Xing], Zhang, J.[Ji], Wang, Y.[Yibo], Gahlmann, A.[Andreas], Acton, S.T.[Scott T.],
Graph-Theoretic Post-Processing of Segmentation With Application to Dense Biofilms,
IP(30), 2021, pp. 8580-8594.
IEEE DOI 2110
Image segmentation, Microorganisms, Feature extraction, Clustering algorithms, Biomedical imaging, cell segmentation BibRef

Bajla, I.[Ivan], Teplan, M.[Michal],
Yeast cell detection in color microscopic images using ROC-optimized decoloring and segmentation,
IET-IPR(16), No. 2, 2022, pp. 606-621.
DOI Link 2201
BibRef

Nguyen, E.H.[Ethan H.], Yang, H.C.[Hai-Chun], Deng, R.[Ruining], Lu, Y.Z.[Yu-Zhe], Zhu, Z.[Zheyu], Roland, J.T.[Joseph T.], Lu, L.[Le], Landman, B.A.[Bennett A.], Fogo, A.B.[Agnes B.], Huo, Y.[Yuankai],
Circle Representation for Medical Object Detection,
MedImg(41), No. 3, March 2022, pp. 746-754.
IEEE DOI 2203
Object detection, Biomedical imaging, Head, Feature extraction, Heating systems, Convolutional neural networks, Training, pathology BibRef

Liu, Z.[Zhe], Bagnaninchi, P.[Pierre], Yang, Y.J.[Yun-Jie],
Impedance-Optical Dual-Modal Cell Culture Imaging With Learning-Based Information Fusion,
MedImg(41), No. 4, April 2022, pp. 983-996.
IEEE DOI 2204
Electrical impedance tomography, Imaging, Image reconstruction, Electrodes, Deep learning, Conductivity, Cell culture, image processing BibRef

Vicent, M.[Mabirizi], Simon, K.[Kawuma], Yonasi, S.[Safari],
An algorithm to detect overlapping red blood cells for sickle cell disease diagnosis,
IET-IPR(16), No. 6, 2022, pp. 1669-1677.
DOI Link 2204
BibRef

Benazzouz, M.[Mourtada], Benomar, M.L.[Mohammed Lamine], Moualek, Y.[Youcef],
Modified U-Net for cytological medical image segmentation,
IJIST(32), No. 5, 2022, pp. 1761-1773.
DOI Link 2209
angiography, cytology, medical images, segmentation, U-Net BibRef

Sathyan, R.R.[Remya Remani], Menon, G.C.[Gopakumar Chandrasekhara], Prasad, H.[Hari], Sreedharan, H.[Hariharan], Hemanth, D.J.[Duraisamy Jude],
Deep learning-based semantic segmentation of interphase cells and debris from metaphase images,
IJIST(32), No. 6, 2022, pp. 2017-2033.
DOI Link 2212
automated karyotyping system (AKS), backbone networks, deep learning, interphase cells, semantic segmentation BibRef

Deng, X.[Ximu], Srivastava, A.[Anuj], Sarkar, R.[Rituparna], Labruyère, E.[Elisabeth], Olivo-Marin, J.C.[Jean-Christophe],
Characterizing Cell Shape Distributions Using k-Mode Kernel Mixtures,
ICPR22(2517-2523)
IEEE DOI 2212
Manifolds, Measurement, Shape, Microscopy, Sociology, Statistical distributions BibRef

Mukherjee, S.[Suvadip], Sarkar, R.[Rituparna], Manich, M.[Maria], Labruyère, E.[Elisabeth], Olivo-Marin, J.C.[Jean-Christophe],
Domain Adapted Multitask Learning for Segmenting Amoeboid Cells in Microscopy,
MedImg(42), No. 1, January 2023, pp. 42-54.
IEEE DOI 2301
Image segmentation, Microscopy, Task analysis, Multitasking, Training, Imaging, Adaptation models, Cell segmentation, cell biology BibRef

Kostrykin, L.[Leonid], Rohr, K.[Karl],
Superadditivity and Convex Optimization for Globally Optimal Cell Segmentation Using Deformable Shape Models,
PAMI(45), No. 3, March 2023, pp. 3831-3847.
IEEE DOI 2302
Shape, Minimization, Image segmentation, Deformable models, Computational modeling, Microscopy, Cell cluster splitting, surface fitting BibRef

Dawoud, Y.[Youssef], Bouazizi, A.[Arij], Ernst, K.[Katharina], Carneiro, G.[Gustavo], Belagiannis, V.[Vasileios],
Knowing What to Label for Few Shot Microscopy Image Cell Segmentation,
WACV23(3557-3566)
IEEE DOI 2302
Training, Image segmentation, Microscopy, Computational modeling, Semisupervised learning, Predictive models, Applications: Biomedical/healthcare/medicine BibRef

Han, L.[Liang], Su, H.[Hang], Yin, Z.Z.[Zhao-Zheng],
Phase Contrast Image Restoration by Formulating Its Imaging Principle and Reversing the Formulation With Deep Neural Networks,
MedImg(42), No. 4, April 2023, pp. 1068-1082.
IEEE DOI 2304
Image restoration, Microscopy, Imaging, Image segmentation, Deep learning, Optical microscopy, Neural networks, cell segmentation BibRef

Zhu, Y.M.[Yan-Ming], Yin, X.F.[Xue-Fei], Meijering, E.[Erik],
A Compound Loss Function With Shape Aware Weight Map for Microscopy Cell Segmentation,
MedImg(42), No. 5, May 2023, pp. 1278-1288.
IEEE DOI 2305
Image segmentation, Shape, Microscopy, Compounds, Microprocessors, Annotations, Microscopy cell segmentation, deep learning, shape aware weight map BibRef

Yu, J.[Ji],
Point-Supervised Single-Cell Segmentation via Collaborative Knowledge Sharing,
MedImg(42), No. 12, December 2023, pp. 3884-3894.
IEEE DOI Code:
WWW Link. 2312
BibRef

Liang, D.[Dan], Pi, Y.J.[Yang-Jun], Hu, K.[Kai], Cui, Y.G.[Yu-Guo], Huang, Y.[Ye], Liang, D.T.[Dong-Tai], Feng, S.L.[Shi-Liang],
Accurate and fast extraction of adhesive cells based on concave points detection and matching,
IJIST(34), No. 1, 2024, pp. e22934.
DOI Link 2401
adhesive cell, concave detection, region extraction, segmentation BibRef

Li, B.[Bo], Zhang, Y.[Yong], Zhang, C.Y.[Cheng-Yang], Piao, X.L.[Xing-Lin], Hu, Y.L.[Yong-Li], Yin, B.C.[Bao-Cai],
Multi-Scale Hypergraph-Based Feature Alignment Network for Cell Localization,
PR(149), 2024, pp. 110260.
Elsevier DOI 2403
Cell localization, Feature alignment, Hypergraph neural network, Multi-scale hypergraph attention, Stepwise adaptive fusion BibRef

Kang, M.[Ming], Ting, C.M.[Chee-Ming], Ting, F.F.[Fung Fung], Phan, R.C.W.[Raphaël C.W.],
ASF-YOLO: A novel YOLO model with attentional scale sequence fusion for cell instance segmentation,
IVC(147), 2024, pp. 105057.
Elsevier DOI Code:
WWW Link. 2406
Medical image analysis, Small object segmentation, You only look once (YOLO), Sequence feature fusion, Attention mechanism BibRef

Zhang, Y.Q.[Yan-Qiu], Du, J.[Jinlian], Jin, X.[Xueyun], Zhang, X.[Xiao],
Cell Image Segmentation Method Based on Edge Feature Residual Fusion,
CVIDL23(148-153)
IEEE DOI 2403
Deep learning, Image segmentation, Fuses, Microprocessors, Image edge detection, Computational modeling, Semantics, residual fusion BibRef

Wolf, S.[Steffen], Lalit, M.[Manan], McDole, K.[Katie], Funke, J.[Jan],
Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images,
ICCV23(21206-21215)
IEEE DOI 2401
BibRef

He, H.L.[Hong-Liang], Wang, J.[Jun], Wei, P.X.[Peng-Xu], Xu, F.[Fan], Ji, X.Y.[Xiang-Yang], Liu, C.[Chang], Chen, J.[Jie],
TopoSeg: Topology-Aware Nuclear Instance Segmentation,
ICCV23(21250-21259)
IEEE DOI Code:
WWW Link. 2401
BibRef

Pastore, V.P.[Vito Paolo], Alfano, P.D.[Paolo Didier], Oke, A.[Ashwini], Capponi, S.[Sara], Eltanan, D.[Daniel], Woodruff-Madeira, X.[Xavier], Nguyen, A.[Anita], Fung, J.C.[Jennifer Carol], Bianco, S.[Simone],
An Unsupervised Learning Approach to Resolve Phenotype to Genotype Mapping in Budding Yeasts Vacuoles,
CIAP23(II:247-258).
Springer DOI 2312
BibRef

Fang, Y.T.[Ya-Ting], Zhong, B.J.[Bao-Jiang], Wang, Z.K.[Zi-Kai], Ma, K.K.[Kai-Kuang],
A Multi-Scale Cell Segmentation Method for Detecting Hematological Disorders,
ICIP23(141-145)
IEEE DOI 2312
BibRef

Zhou, D.H.[Dong-Hao], Gu, C.B.[Chun-Bin], Xu, J.[Junde], Liu, F.[Furui], Wang, Q.[Qiong], Chen, G.Y.[Guang-Yong], Heng, P.A.[Pheng-Ann],
RepMode: Learning to Re-Parameterize Diverse Experts for Subcellular Structure Prediction,
CVPR23(3312-3322)
IEEE DOI 2309
BibRef

Jiang, H.[Hao], Zhang, R.S.[Ru-Shan], Zhou, Y.N.[Yan-Ning], Wang, Y.[Yumeng], Chen, H.[Hao],
DoNet: Deep De-Overlapping Network for Cytology Instance Segmentation,
CVPR23(15641-15650)
IEEE DOI 2309
BibRef

Tyagi, A.K.[Aayush Kumar], Mohapatra, C.[Chirag], Das, P.[Prasenjit], Makharia, G.[Govind], Mehra, L.[Lalita], Prathosh, A.P., Mausam,
DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting,
CVPR23(23913-23923)
IEEE DOI 2309
BibRef

Kato, S.[Sota], Hotta, K.[Kazuhiro],
One-shot and Partially-Supervised Cell Image Segmentation Using Small Visual Prompt,
CVMI23(4295-4304)
IEEE DOI 2309
BibRef

Abousamra, S.[Shahira], Gupta, R.[Rajarsi], Kurc, T.[Tahsin], Samaras, D.[Dimitris], Saltz, J.[Joel], Chen, C.[Chao],
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology,
CVPR23(3323-3333)
IEEE DOI 2309
BibRef

Casado-García, Á.[Ángela], Carlos, E.[Estefanía], Domínguez, C.[César], Heras, J.[Jónathan], Izco, M.[María], Mata, E.[Eloy], Pascual, V.[Vico], Álvarez-Erviti, L.[Lydia],
Microgliaj: An Automatic Tool for Microglial Cell Detection and Segmentation,
IbPRIA23(593-602).
Springer DOI 2307
BibRef

Golts, A.[Alona], Livneh, I.[Ido], Zohar, Y.[Yaniv], Ciechanover, A.[Aaron], Elad, M.[Michael],
Simultaneous Detection and Classification of Partially and Weakly Supervised Cells,
MCV22(313-329).
Springer DOI 2304
BibRef

Chattopadhyay, N.[Nilanjan], Gehlot, S.[Shiv], Singhal, N.[Nitin],
Fusion: Fully Unsupervised Test-time Stain Adaptation via Fused Normalization Statistics,
MIA-COVID19D22(566-576).
Springer DOI 2304
BibRef

Nguyen, T.P.[Tien-Phat], Pham, T.T.[Trong-Thang], Nguyen, T.[Tri], Le, H.[Hieu], Nguyen, D.[Dung], Lam, H.[Hau], Nguyen, P.[Phong], Fowler, J.[Jennifer], Tran, M.T.[Minh-Triet], Le, N.[Ngan],
EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification,
WACV23(1980-1989)
IEEE DOI 2302
Image segmentation, Embryo, Head, Computational modeling, Source coding, Collaboration, Transformers BibRef

Nishimura, K.[Kazuya], Bise, R.[Ryoma],
Weakly Supervised Cell-Instance Segmentation with Two Types of Weak Labels by Single Instance Pasting,
WACV23(3184-3193)
IEEE DOI 2302
Learning systems, Image segmentation, Costs, Image recognition, Image analysis, Annotations BibRef

Keaton, M.R.[Matthew R.], Zaveri, R.J.[Ram J.], Doretto, G.[Gianfranco],
CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentation,
WACV23(455-466)
IEEE DOI 2302
Training, Adaptation models, Annotations, Computational modeling, Biological system modeling, Graphics processing units BibRef

Zhou, Y.T.[Ya-Ting], Li, W.J.[Wen-Jing], Yang, G.[Ge],
SCTS: Instance Segmentation of Single Cells Using a Transformer-Based Semantic-Aware Model and Space-Filling Augmentation,
WACV23(5933-5942)
IEEE DOI 2302
Training, Image segmentation, Adaptation models, Statistical analysis, Shape, Semantics, Training data, Low-level and physics-based vision BibRef

Liu, Y.[Yi], Nie, Y.L.[Yuan-Liu], Liu, N.[Ning], Yao, F.Q.[Feng-Qin], Zhu, J.R.[Jing-Ru], Wang, S.K.[Sheng-Ke],
EA-UNet: A Macrophages Image Segmentation Model Based on U-Net with External Attention,
ICIVC22(387-392)
IEEE DOI 2301
Training, Image segmentation, Adaptation models, Shape, Semantics, Semisupervised learning, Feature extraction, external-attention BibRef

Liu, R.[Rong], Ao, B.[Bin], Wen, Q.[Qing], Wu, X.[Xin], Yin, J.P.[Jian-Ping], Li, K.[Kuan],
Combining ExtremeNet with Shape Constraints and Re-Discrimination to Detect Cells from CD56 Images,
ICPR22(4587-4593)
IEEE DOI 2212
Training, Deep learning, Shape, Neurons, Object detection, Convolutional neural networks, CD56 images, Re-Discrimination BibRef

Xu, A.[An], Li, W.Q.[Wen-Qi], Guo, P.F.[Peng-Fei], Yang, D.[Dong], Roth, H.[Holger], Hatamizadeh, A.[Ali], Zhao, C.[Can], Xu, D.G.[Da-Guang], Huang, H.[Heng], Xu, Z.Y.[Zi-Yue],
Closing the Generalization Gap of Cross-Silo Federated Medical Image Segmentation,
CVPR22(20834-20843)
IEEE DOI 2210
Training, Image segmentation, Privacy, Image resolution, Federated learning, Microscopy, Linear programming, Medical, Privacy and federated learning BibRef

Cerrone, L.[Lorenzo], Vijayan, A.[Athul], Mody, T.[Tejasvinee], Schneitz, K.[Kay], Hamprecht, F.A.[Fred A.],
CellTypeGraph: A New Geometric Computer Vision Benchmark,
CVPR22(20865-20875)
IEEE DOI 2210
Plants (biology), Supervised learning, Biological systems, Computer architecture, Benchmark testing, Medical, grouping and shape analysis BibRef

Sugimoto, T.[Tatsuhiko], Ito, H.[Hiroaki], Teramoto, Y.[Yuki], Yoshizawa, A.[Akihiko], Bise, R.[Ryoma],
Multi-Class Cell Detection Using Modified Self-Attention,
CVMI22(1854-1862)
IEEE DOI 2210
Heating systems, Pathology, Aggregates, Feature extraction, Pattern recognition BibRef

Zhou, Z.Q.[Zi-Qi], Qi, L.[Lei], Yang, X.[Xin], Ni, D.[Dong], Shi, Y.[Yinghuan],
Generalizable Cross-modality Medical Image Segmentation via Style Augmentation and Dual Normalization,
CVPR22(20824-20833)
IEEE DOI 2210
Image segmentation, Adaptation models, Codes, Shape, Microscopy, Computed tomography, Medical, biological and cell microscopy, Transfer/low-shot/long-tail learning BibRef

Liu, F.B.[Feng-Bei], Tian, Y.[Yu], Chen, Y.H.[Yuan-Hong], Liu, Y.Y.[Yu-Yuan], Belagiannis, V.[Vasileios], Carneiro, G.[Gustavo],
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification,
CVPR22(20665-20674)
IEEE DOI 2210
Training, Semisupervised learning, Thorax, Skin, Pattern recognition, Lesions, Medical diagnostic imaging, Medical, biological and cell microscopy BibRef

Tang, Y.C.[Yu-Cheng], Yang, D.[Dong], Li, W.Q.[Wen-Qi], Roth, H.R.[Holger R.], Landman, B.[Bennett], Xu, D.G.[Da-Guang], Nath, V.[Vishwesh], Hatamizadeh, A.[Ali],
Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis,
CVPR22(20698-20708)
IEEE DOI 2210
Image segmentation, Image analysis, Computational modeling, Computed tomography, Diversity reception, biological and cell microscopy BibRef

Ding, Z.P.[Zhi-Peng], Niethammer, M.[Marc],
Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment,
CVPR22(20752-20761)
IEEE DOI 2210
Training, Image registration, Solid modeling, Buildings, Sociology, Probabilistic logic, biological and cell microscopy, Medical BibRef

Wu, Y.F.[Yi-Fan], Jiahao, T.Z.[Tom Z.], Wang, J.[Jiancong], Yushkevich, P.A.[Paul A.], Hsieh, M.A.[M. Ani], Gee, J.C.[James C.],
NODEO: A Neural Ordinary Differential Equation Based Optimization Framework for Deformable Image Registration,
CVPR22(20772-20781)
IEEE DOI 2210
Measurement, Image registration, Neural networks, Ordinary differential equations, Benchmark testing, biological and cell microscopy BibRef

Wang, J.F.[Jian-Feng], Lukasiewicz, T.[Thomas],
Rethinking Bayesian Deep Learning Methods for Semi-Supervised Volumetric Medical Image Segmentation,
CVPR22(182-190)
IEEE DOI 2210
Deep learning, Training, Image segmentation, Microscopy, Microprocessors, Computer architecture, Probabilistic logic, biological and cell microscopy BibRef

Kong, F.[Fanjie], Henao, R.[Ricardo],
Efficient Classification of Very Large Images with Tiny Objects,
CVPR22(2374-2384)
IEEE DOI 2210
Satellites, Microscopy, Microprocessors, Machine vision, Memory management, Convolutional neural networks, biological and cell microscopy BibRef

Gauthier, S.[Shanel], Thérien, B.[Benjamin], Alséne-Racicot, L.[Laurent], Chaudhary, M.[Muawiz], Rish, I.[Irina], Belilovsky, E.[Eugene], Eickenberg, M.[Michael], Wolf, G.[Guy],
Parametric Scattering Networks,
CVPR22(5739-5748)
IEEE DOI 2210
Wavelet transforms, Uncertainty, Scattering, Filter banks, Computer architecture, Stability analysis, Low-level vision, biological and cell microscopy BibRef

Matsoukas, C.[Christos], Haslum, J.F.[Johan Fredin], Sorkhei, M.[Moein], Söderberg, M.[Magnus], Smith, K.[Kevin],
What Makes Transfer Learning Work for Medical Images: Feature Reuse & Other Factors,
CVPR22(9215-9224)
IEEE DOI 2210
Deep learning, Microscopy, Microprocessors, Transfer learning, Computer architecture, Data models, Pattern recognition, biological and cell microscopy BibRef

Shen, Y.Q.[Yi-Qing], Zhou, Y.Y.[Yu-Yin], Yu, L.Q.[Le-Quan],
CD2-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning,
CVPR22(10031-10040)
IEEE DOI 2210
Privacy, Image analysis, Distance learning, Microscopy, Collaborative work, Data models, Privacy and federated learning, biological and cell microscopy BibRef

Yu, J.C.[Jun-Chi], Cao, J.[Jie], He, R.[Ran],
Improving Subgraph Recognition with Variational Graph Information Bottleneck,
CVPR22(19374-19383)
IEEE DOI 2210
Training, Upper bound, Filtering, Perturbation methods, Graph neural networks, Pattern recognition, biological and cell microscopy BibRef

Zhang, W.Q.[Wen-Qiao], Zhu, L.[Lei], Hallinan, J.[James], Zhang, S.Y.[Sheng-Yu], Makmur, A.[Andrew], Cai, Q.P.[Qing-Peng], Ooi, B.C.[Beng Chin],
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation,
CVPR22(20634-20644)
IEEE DOI 2210
Training, Adaptation models, Spinal cord, Annotations, Semisupervised learning, Predictive models, Labeling, Medical, biological and cell microscopy BibRef

Gräbel, P.[Philipp], Thull, J.[Julian], Crysandt, M.[Martina], Klinkhammer, B.M.[Barbara M.], Boor, P.[Peter], Brummendorf, T.H.[Tim H.], Merhof, D.[Dorit],
Analysis of automatically generated embedding guides for cell classification,
IPTA22(1-6)
IEEE DOI 2206
Representation learning, Training, Visualization, Microscopy, Image processing, Prediction algorithms, Bones, em-bedding guides BibRef

Anoshina, N.A.[Nadezhda A.], Sorokin, D.V.[Dmitry V.],
Weak supervision using cell tracking annotation and image registration improves cell segmentation,
IPTA22(1-5)
IEEE DOI 2206
Measurement, Training, Image segmentation, Image registration, Annotations, Sociology, Neural networks, image registration, weakly-supervised learning BibRef

Nowara, E.M.[Ewa M.], McDuff, D.[Daniel], Veeraraghavan, A.[Ashok],
The Benefit of Distraction: Denoising Camera-Based Physiological Measurements using Inverse Attention,
ICCV21(4935-4944)
IEEE DOI 2203
Heart rate, Noise reduction, Lighting, Physiology, Data mining, Motion measurement, Vision applications and systems, and cell microscopy BibRef

Cheng, M.F.[Ming-Fei], Zhao, K.[Kaili], Guo, X.H.[Xu-Hong], Xu, Y.J.[Ya-Jing], Guo, J.[Jun],
Joint Topology-preserving and Feature-refinement Network for Curvilinear Structure Segmentation,
ICCV21(7127-7136)
IEEE DOI 2203
Image segmentation, Network topology, Roads, Semantics, Refining, Logic gates, Drives, Segmentation, grouping and shape, Medical, and cell microscopy BibRef

Panteli, A.[Andreas], Teuwen, J.[Jonas], Horlings, H.[Hugo], Gavves, E.[Efstratios],
Sparse-shot Learning with Exclusive Cross-Entropy for Extremely Many Localisations,
ICCV21(2793-2803)
IEEE DOI 2203
Pathology, Image resolution, Annotations, Computational modeling, Supervised learning, Task analysis, and cell microscopy BibRef

Ma, T.Y.[Tian-Yu], Dalca, A.V.[Adrian V.], Sabuncu, M.R.[Mert R.],
Hyper-Convolution Networks for Biomedical Image Segmentation,
WACV22(1989-1998)
IEEE DOI 2202
Image segmentation, Convolution, Architecture, Neural networks, Computer architecture, Grouping and Shape Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy BibRef

Zhang, Q.[Qi], Meng, Z.[Zhu], Zhao, Z.C.[Zhi-Cheng], Su, F.[Fei],
GSLD: A Global Scanner with Local Discriminator Network for Fast Detection of Sparse Plasma Cell in Immunohistochemistry,
ICIP21(86-90)
IEEE DOI 2201
Deep learning, Image processing, Aggregates, Cells (biology), Object detection, Plasmas, Immunohistochemistry, deep learning BibRef

Zhu, Y.[Yuang], Chen, Z.[Zhao], Zheng, Y.X.[Yu-Xin], Zhang, Q.H.[Qing-Hua], Wang, X.[Xuan],
Real-Time Cell Counting in Unlabeled Microscopy Images,
CDPath21(694-703)
IEEE DOI 2112
Training, Deep learning, Adaptation models, Microscopy, Manuals, Data structures, Real-time systems BibRef

Bao, R.[Rina], Al-Shakarji, N.M.[Noor M.], Bunyak, F.[Filiz], Palaniappan, K.[Kannappan],
DMNet: Dual-Stream Marker Guided Deep Network for Dense Cell Segmentation and Lineage Tracking,
CVAMD21(3354-3363)
IEEE DOI 2112
Training, Image segmentation, Shape, Microscopy, Pipelines, Stem cells, Streaming media BibRef

Fujii, H.[Haruki], Tanaka, H.[Hayato], Ikeuchi, M.[Momoko], Hotta, K.[Kazuhiro],
X-net with Different Loss Functions for Cell Image Segmentation,
CVMI21(3788-3795)
IEEE DOI 2109
Image segmentation, Convolution, Semantics, Memory management, Object segmentation, Feature extraction, Decoding BibRef

Bouyssoux, A.[Alexandre], Fezzani, R.[Riadh], Olivo-Marin, J.C.[Jean-Christophe],
Extended Depth of Field Preserving Color Fidelity For Automated Digital Cytology,
ICPR21(1-7)
IEEE DOI 2105
Wavelet transforms, Image segmentation, Image color analysis, Gray-scale, Task analysis, Image reconstruction BibRef

Sarkar, R.[Rituparna], Mukherjee, S.[Suvadip], Labruyère, E.[Elisabeth], Olivo-Marin, J.C.[Jean-Christophe],
Learning to segment clustered amoeboid cells from brightfield microscopy via multi-task learning with adaptive weight selection,
ICPR21(3845-3852)
IEEE DOI 2105
Image segmentation, Adaptation models, Adaptive systems, Microscopy, Neural networks, Predictive models, Tools BibRef

Paolanti, M.[Marina], Mameli, M.[Marco], Frontoni, E.[Emanuele], Gioacchini, G.[Giorgia], Giorgini, E.[Elisabetta], Notarstefano, V.[Valentina], Zacà, C.[Carlotta], Carnevali, O.[Oliana], Borini, A.[Andrea],
Automatic Classification of Human Granulosa Cells in Assisted Reproductive Technology using vibrational spectroscopy imaging,
ICPR21(209-216)
IEEE DOI 2105
Pregnancy, Spectroscopy, Cloud computing, Fourier transforms, Statistical analysis, Subspace constraints, Imaging, Granulosa Cells BibRef

Lockhart, L.[Lisette], Saeedi, P.[Parvaneh], Au, J.[Jason], Havelock, J.[Jon],
Human Embryo Cell Centroid Localization and Counting in Time-Lapse Sequences,
ICPR21(8306-8311)
IEEE DOI 2105
Location awareness, Training, Pregnancy, Measurement, Visualization, Embryo, Pattern recognition BibRef

Cruz, D.[Daniel], Claro, M.[Maíla], Veras, R.[Rodrigo], Vogado, L.[Luis], Portela, H.[Helano], Moura, N.[Nayara], Luz, D.[Daniel],
P-fidenet: Plasmodium Falciparum Identification Neural Network,
ISVC20(I:369-380).
Springer DOI 2103
BibRef

Padhee, S.[Swati], Alambo, A.[Amanuel], Banerjee, T.[Tanvi], Subramaniam, A.[Arvind], Abrams, D.M.[Daniel M.], Nave Jr., G.K.[Gary K.], Shah, N.[Nirmish],
Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits,
CAIHA20(77-85).
Springer DOI 2103
BibRef

Fujita, S.[Seiya], Han, X.H.[Xian-Hua],
Cell Detection and Segmentation in Microscopy Images with Improved Mask R-CNN,
MLCSA20(58-70).
Springer DOI 2103
BibRef

Wang, W.N.[Wei-Ning], Guo, P.R.[Pei-Rong], Li, L.[Lemin], Tan, Y.[Yan], Shi, H.X.[Hong-Xia], Wei, Y.[Yan], Xu, X.M.[Xiang-Min],
Attention-based Fine-grained Classification of Bone Marrow Cells,
ACCV20(V:652-668).
Springer DOI 2103
BibRef

Liu, Y., Nedo, A., Seward, K., Caplan, J., Kambhamettu, C.,
Quantifying Actin Filaments in Microscopic Images using Keypoint Detection Techniques and A Fast Marching Algorithm,
ICIP20(2506-2510)
IEEE DOI 2011
Junctions, Image segmentation, Microscopy, Heating systems, Neural networks, Machine learning, Feature extraction, Quantification analysis BibRef

Jiang, N., Yu, F.,
A Foreground Mask Network for Cell Counting,
ICIVC20(128-132)
IEEE DOI 2009
Convolution, Decoding, Frequency modulation, Kernel, Feature extraction, Semantics, Computational modeling, mask BibRef

Jensen, P.M., Dahl, A.B., Dahl, V.A.,
Multi-object Graph-based Segmentation with Non-overlapping Surfaces,
Microscopy20(4204-4212)
IEEE DOI 2008
Image segmentation, Microscopy, Optical microscopy, Shape, Image edge detection, Optimization BibRef

Kulikov, V., Lempitsky, V.,
Instance Segmentation of Biological Images Using Harmonic Embeddings,
CVPR20(3842-3850)
IEEE DOI 2008
Training, Harmonic analysis, Image segmentation, Biological information theory, Neural networks, Shape, Cells (biology) BibRef

Lu, W., Graham, S., Bilal, M., Rajpoot, N., Minhas, F.,
Capturing Cellular Topology in Multi-Gigapixel Pathology Images,
DLGC20(1049-1058)
IEEE DOI 2008
Pathology, Machine learning, Visualization, Computational modeling, Convolutional neural networks, Principal component analysis, Breast cancer BibRef

Shibuya, E., Hotta, K.,
Feedback U-net for Cell Image Segmentation,
Microscopy20(4195-4203)
IEEE DOI 2008
Feature extraction, Image segmentation, Neurons, Convolution, Decoding, Logic gates, Computer architecture BibRef

Sulc, M., Picek, L., Matas, J., Jeppesen, T.S., Heilmann-Clausen, J.,
Fungi Recognition: A Practical Use Case,
WACV20(2305-2313)
IEEE DOI 2006
Fungi, Image recognition, Biodiversity, Agriculture, Training, Visualization BibRef

Fujitani, M., Mochizuki, Y., Iizuka, S., Simo-Serra, E., Kobayashi, H., Iwamoto, C., Ohuchida, K., Hashizume, M., Hontani, H., Ishikawa, H.,
Re-staining Pathology Images by FCNN,
MVA19(1-6)
DOI Link 1911
biological tissues, biomedical optical imaging, convolutional neural nets, diseases, image colour analysis, Chemicals BibRef

Javed, S., Mahmood, A., Werghi, N., Rajpoot, N.,
Deep Multiresolution Cellular Communities for Semantic Segmentation of Multi-Gigapixel Histology Images,
VRMI19(342-351)
IEEE DOI 2004
Feature extraction, Tumors, Image resolution, Cancer, Pathology, Neural networks, Support vector machines, CANCER, HISTOLOGY, microenvironment BibRef

Rad, R.M., Saeedi, P., Au, J., Havelock, J.,
BLAST-NET: Semantic Segmentation of Human Blastocyst Components via Cascaded Atrous Pyramid and Dense Progressive Upsampling,
ICIP19(1865-1869)
IEEE DOI 1910
Human Embryo, Blastocyst, IVF, Semantic Segmentation, Medical Image Analysis, Deep Learning BibRef

Scherzinger, A.[Aaron], Hugenroth, P.[Philipp], Rüder, M.[Marike], Bogdan, S.[Sven], Jiang, X.Y.[Xiao-Yi],
Multi-class Cell Segmentation Using CNNs with F1-measure Loss Function,
GCPR18(434-446).
Springer DOI 1905
BibRef

Klemm, S.[Sören], Jiang, X.Y.[Xiao-Yi], Risse, B.[Benjamin],
Deep Distance Transform to Segment Visually Indistinguishable Merged Objects,
GCPR18(422-433).
Springer DOI 1905
BibRef

Caicedo, J.C.[Juan C.], McQuin, C.[Claire], Goodman, A.[Allen], Singh, S.[Shantanu], Carpenter, A.E.[Anne E.],
Weakly Supervised Learning of Single-Cell Feature Embeddings,
CVPR18(9309-9318)
IEEE DOI 1812
Compounds, Feature extraction, Biology, Training, Sociology, Statistics, Microscopy BibRef

Haehn, D.[Daniel], Kaynig, V.[Verena], Tompkin, J.[James], Lichtman, J.W.[Jeff W.], Pfister, H.[Hanspeter],
Guided Proofreading of Automatic Segmentations for Connectomics,
CVPR18(9319-9328)
IEEE DOI 1812
Image segmentation, Tools, Error correction, Computer architecture, Visualization, Task analysis BibRef

Yellin, F., Haeffele, B.D., Roth, S., Vidal, R.,
Multi-cell Detection and Classification Using a Generative Convolutional Model,
CVPR18(8953-8961)
IEEE DOI 1812
Blood, Convolutional codes, Task analysis, Biological system modeling, Sociology, Statistics BibRef

Oraibi, Z.A., Yousif, H., Hafiane, A., Seetharaman, G., Palaniappan, K.,
Learning Local and Deep Features for Efficient Cell Image Classification Using Random Forests,
ICIP18(2446-2450)
IEEE DOI 1809
Feature extraction, Radio frequency, Computer architecture, Machine learning, Microprocessors, Task analysis, Forestry, Image Classification BibRef

Guerrero-Peña, F.A., Marrero Fernandez, P.D., Ing Ren, T., Yui, M., Rothenberg, E., Cunha, A.,
Multiclass Weighted Loss for Instance Segmentation of Cluttered Cells,
ICIP18(2451-2455)
IEEE DOI 1809
Image segmentation, Training, Blood, Entropy, Surface acoustic waves, Cells (biology), Deep learning, instance segmentation, cell segmentation BibRef

Ma, H., Beiter, R., Gaultier, A., Acton, S.T., Lin, Z.,
OSLO: Automatic Cell Counting and Segmentation for Oligodendrocyte Progenitor Cells,
ICIP18(2431-2435)
IEEE DOI 1809
Image segmentation, Saliency detection, Optimization, Image edge detection, Task analysis, Standards, Progenitor cells, bioimage analysis BibRef

Marcal, A.R.S.[André R. S.], Martins, J.[Joana], Selaru, E.[Elena], Tavares, F.[Fernando],
Towards Automatic Calibration of Dotblot Images,
ICIAR18(39-46).
Springer DOI 1807
BibRef

Han, L., Murphy, R.F., Ramanan, D.,
Learning Generative Models of Tissue Organization with Supervised GANs,
WACV18(682-690)
IEEE DOI 1806
Spatial orgainzation of cells in tissues. biology computing, cellular biophysics, image classification, image segmentation, learning (artificial intelligence), Organizations BibRef