21.4.2.1 Cell Nucleus, Cell Nuclei Analysis, Detection

CR Chisto Labeled Nuclei Dataset,
Online2016
WWW Link. Dataset, Nuclei. 1602
Dataset of colorectal cancer histology images consisting of nearly 30,000 dotted nuclei with over 22,000 labeled with the type of cell they belong to. BibRef

Ott, R., Schürmann, J., Reinhardt, E.R., Bloss, W.H.,
Automated classification of cytological specimens based on features extracted from nuclei images,
PR(13), No. 1, 1981, pp. 83-87.
Elsevier DOI 0309
BibRef

Bamford, P.[Pascal], Lovell, B.C.[Brian C.],
Unsupervised cell nucleus segmentation with active contours,
SP(71), No. 2, 15 December 1998, pp. 203-213. BibRef 9812
Earlier:
Bayesian Analysis of Cell Nucleus Segmentation by a Viterbi Search Based Active Contour,
ICPR98(Vol I: 133-135).
IEEE DOI 9808
BibRef
And:
Improving the Robustness of Cell Nucleus Segmentation,
BMVC98(xx-yy). BibRef

Yang, S., Kohler, D., Teller, K., Cremer, T., Le Baccon, P., Heard, E., Eils, R., Rohr, K.,
Nonrigid Registration of 3-D Multichannel Microscopy Images of Cell Nuclei,
IP(17), No. 4, April 2008, pp. 493-499.
IEEE DOI 0803
BibRef

Kim, I.H., Chen, Y.C.M., Spector, D.L., Eils, R., Rohr, K.,
Nonrigid Registration of 2-D and 3-D Dynamic Cell Nuclei Images for Improved Classification of Subcellular Particle Motion,
IP(20), No. 4, April 2011, pp. 1011-1022.
IEEE DOI 1103
BibRef

McCullough, D.P., Gudla, P.R., Harris, B.S., Collins, J.A., Meaburn, K.J., Nakaya, M.A., Yamaguchi, T.P., Misteli, T., Lockett, S.J.,
Segmentation of Whole Cells and Cell Nuclei From 3-D Optical Microscope Images Using Dynamic Programming,
MedImg(27), No. 5, May 2008, pp. 723-734.
IEEE DOI 0711
BibRef

Yu, D.G.[Dong-Gang], Pham, T.D.[Tuan D.], Zhou, X.B.[Xiao-Bo], Wong, S.T.C.[Stephen T.C.],
Recognition and analysis of cell nuclear phases for high-content screening based on morphological features,
PR(42), No. 4, April 2009, pp. 498-508.
Elsevier DOI 0812
Nuclei phases; Cell screening; Feature extraction; Morphological feature; Feed-forward detection; Feed-back detection; Shape recognition; Normal cellular cycle BibRef

Cloppet, F., Boucher, A.,
Segmentation of complex nucleus configurations in biological images,
PRL(31), No. 8, 1 June 2010, pp. 755-761.
Elsevier DOI 1004
BibRef
Earlier:
Segmentation of overlapping/aggregating nuclei cells in biological images,
ICPR08(1-4).
IEEE DOI 0812
Nucleus segmentation; Prior information; Watershed-based segmentation; Biomedical imaging BibRef

Quelhas, P., Marcuzzo, M., Mendonca, A.M., Campilho, A.,
Cell Nuclei and Cytoplasm Joint Segmentation Using the Sliding Band Filter,
MedImg(29), No. 8, August 2010, pp. 1463-1473.
IEEE DOI 1008
BibRef

Quelhas, P.[Pedro], Mendonca, A.M.[Ana Maria], Campilho, A.[Aurelio],
3D Cell Nuclei Fluorescence Quantification Using Sliding Band Filter,
ICPR10(2508-2511).
IEEE DOI 1008
BibRef

Marcuzzo, M.[Monica], Quelhas, P.[Pedro], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
Evaluation of Symmetry Enhanced Sliding Band Filter for Plant Cell Nuclei Detection in Low Contrast Noisy Fluorescent Images,
ICIAR09(824-831).
Springer DOI 0907
BibRef

Carleos, C.[Carlos], López-Díaz, M.C.[M. Concepción], López-Díaz, M.[Miguel],
A Stochastic Order of Shape Variability with an Application to Cell Nuclei Involved in Mastitis,
JMIV(38), No. 2, October 2010, pp. 95-107.
WWW Link. 1011
BibRef

Carleos, C.[Carlos], López-Díaz, M.C.[María Concepción], López-Díaz, M.[Miguel],
Ranking Star-Shaped Valued Mappings with Respect to Shape Variability,
JMIV(48), No. 1, January 2014, pp. 1-12.
Springer DOI 1402
BibRef

Plissiti, M.E.[Marina E.], Nikou, C.[Christophoros], Charchanti, A.[Antonia],
Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images,
PRL(32), No. 6, 15 April 2011, pp. 838-853.
Elsevier DOI 1103
Cell nuclei segmentation; Pap smear images; Morphological reconstruction; Watersheds; Feature selection; Clustering BibRef

Plissiti, M.E.[Marina E.], Vrigkas, M., Nikou, C.[Christophoros],
Segmentation of cell clusters in Pap smear images using intensity variation between superpixels,
WSSIP15(184-187)
IEEE DOI 1603
CCD image sensors BibRef

Wang, W., Ozolek, J.A., Slepcev, D., Lee, A.B., Chen, C., Rohde, G.K.,
An Optimal Transportation Approach for Nuclear Structure-Based Pathology,
MedImg(30), No. 3, March 2011, pp. 621-631.
IEEE DOI 1103
BibRef

Ali, R.[Rehan], Gooding, M.[Mark], Szilágyi, T.[Tünde], Vojnovic, B.[Borivoj], Christlieb, M.[Martin], Brady, M.[Michael],
Automatic segmentation of adherent biological cell boundaries and nuclei from brightfield microscopy images,
MVA(23), No. 4, July 2012, pp. 607-621.
WWW Link. 1206
adherent eukaryotic cells. BibRef

Wang, W.[Wei], Slepcev, D.[Dejan], Basu, S.[Saurav], Ozolek, J.A.[John A.], Rohde, G.K.[Gustavo K.],
A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images,
IJCV(101), No. 2, January 2013, pp. 254-269.
WWW Link. 1302
BibRef

Kolouri, S.[Soheil], Tosun, A.B.[Akif B.], Ozolek, J.A.[John A.], Rohde, G.K.[Gustavo K.],
A continuous linear optimal transport approach for pattern analysis in image datasets,
PR(51), No. 1, 2016, pp. 453-462.
Elsevier DOI 1601
Optimal transport BibRef

Han, J.W.[Ji Wan], Breckon, T.P.[Toby P.], Randell, D.A.[David A.], Landini, G.[Gabriel],
The application of support vector machine classification to detect cell nuclei for automated microscopy,
MVA(23), No. 1, January 2012, pp. 15-24.
WWW Link. 1201
BibRef

Esteves, T.[Tiago], Quelhas, P.[Pedro], Mendonça, A.M.[Ana Maria], Campilho, A.[Aurélio],
Gradient convergence filters and a phase congruency approach for in vivo cell nuclei detection,
MVA(23), No. 4, July 2012, pp. 623-638.
WWW Link. 1206
BibRef

Plissiti, M.E., Nikou, C.,
Overlapping Cell Nuclei Segmentation Using a Spatially Adaptive Active Physical Model,
IP(21), No. 11, November 2012, pp. 4568-4580.
IEEE DOI 1210
BibRef

Chang, H., Han, J., Borowsky, A., Loss, L., Gray, J.W., Spellman, P.T., Parvin, B.,
Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association,
MedImg(32), No. 4, April 2013, pp. 670-682.
IEEE DOI 1304
BibRef

Moussavi, F., Wang, Y., Lorenzen, P., Oakley, J., Russakoff, D., Gould, S.,
A Unified Graphical Models Framework for Automated Mitosis Detection in Human Embryos,
MedImg(33), No. 7, July 2014, pp. 1551-1562.
IEEE DOI 1407
Computational modeling BibRef

Widmer, C.[Christian], Heinrich, S.[Stephanie], Drewe, P.[Philipp], Lou, X.H.[Xing-Hua], Umrania, S.[Shefali], Rätsch, G.[Gunnar],
Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images,
SIViP(8), No. S1, December 2014, pp. 41-48.
Springer DOI 1411
cellular nuclear segmentation in microscope images. BibRef

Pecot, T., Bouthemy, P., Boulanger, J., Chessel, A., Bardin, S., Salamero, J., Kervrann, C.,
Background Fluorescence Estimation and Vesicle Segmentation in Live Cell Imaging With Conditional Random Fields,
IP(24), No. 2, February 2015, pp. 667-680.
IEEE DOI 1502
adhesion BibRef

Paul, A., Mukherjee, D.P.,
Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images,
IP(24), No. 11, November 2015, pp. 4041-4054.
IEEE DOI 1509
biological organs BibRef

Zhang, W., Fehrenbach, J., Desmaison, A., Lobjois, V., Ducommun, B., Weiss, P.,
Structure Tensor Based Analysis of Cells and Nuclei Organization in Tissues,
MedImg(35), No. 1, January 2016, pp. 294-306.
IEEE DOI 1601
Anisotropic magnetoresistance BibRef

Bejnordi, B.E.[B. Ehteshami], Litjens, G., Timofeeva, N., Otte-Holler, I., Homeyer, A., Karssemeijer, N., van der Laak, J.A.W.M.,
Stain Specific Standardization of Whole-Slide Histopathological Images,
MedImg(35), No. 2, February 2016, pp. 404-415.
IEEE DOI 1602
Design automation BibRef

Gharipour, A.[Amin], Liew, A.W.C.[Alan Wee-Chung],
Segmentation of cell nuclei in fluorescence microscopy images: An integrated framework using level set segmentation and touching-cell splitting,
PR(58), No. 1, 2016, pp. 1-11.
Elsevier DOI 1606
Fluorescence microscopy images BibRef

Ram, S., Rodríguez, J.J.,
Size-Invariant Detection of Cell Nuclei in Microscopy Images,
MedImg(35), No. 7, July 2016, pp. 1753-1764.
IEEE DOI 1608
biomedical optical imaging BibRef

Ram, S., Nguyen, V.T., Limesand, K.H., Rodriguez, J.J.,
Combined Detection and Segmentation of Cell Nuclei in Microscopy Images Using Deep Learning,
SSIAI20(26-29)
IEEE DOI 2009
biological organs, biomedical optical imaging, cancer, cellular biophysics, convolutional neural nets, confocal microscopy BibRef

Shahul Hameed, K.A., Banumathi, A., Ulaganathan, G.,
P53immunostained cell nuclei segmentation in tissue images of oral squamous cell carcinoma,
SIViP(11), No. 2, February 2017, pp. 363-370.
WWW Link. 1702
BibRef

Zhang, W.J.[Wan-Jun], Li, H.Q.[Hui-Qi],
Automated segmentation of overlapped nuclei using concave point detection and segment grouping,
PR(71), No. 1, 2017, pp. 349-360.
Elsevier DOI 1707
Nuclei, segmentation BibRef

Kumar, N., Verma, R., Sharma, S., Bhargava, S., Vahadane, A., Sethi, A.,
A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology,
MedImg(36), No. 7, July 2017, pp. 1550-1560.
IEEE DOI 1707
Diseases, Image color analysis, Image segmentation, Machine learning, Measurement, Pathology, Training, Annotation, boundaries, dataset, deep learning, nuclear segmentation, nuclei BibRef

Song, J., Xiao, L., Lian, Z.,
Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images,
IP(27), No. 12, December 2018, pp. 5759-5774.
IEEE DOI 1810
biomedical optical imaging, blood, cancer, cellular biophysics, feature extraction, image classification, image segmentation, complete contour inference BibRef

Sorokin, D.V., Peterlik, I., Tektonidis, M., Rohr, K., Matula, P.,
Non-Rigid Contour-Based Registration of Cell Nuclei in 2-D Live Cell Microscopy Images Using a Dynamic Elasticity Model,
MedImg(37), No. 1, January 2018, pp. 173-184.
IEEE DOI 1801
biomechanics, biomedical optical imaging, cellular biophysics, deformation, elasticity, fluorescence, image matching, registration BibRef

Kurmi, Y.[Yashwant], Chaurasia, V.[Vijayshri],
Multifeature-based medical image segmentation,
IET-IPR(12), No. 8, August 2018, pp. 1491-1498.
DOI Link 1808
overlapped nuclei. BibRef

Nie, W.Z.[Wei-Zhi], Yan, Y.[Yan], Hao, T.[Tong], Liu, C.C.[Chen-Chen], Su, Y.T.[Yu-Ting],
Mitosis event recognition and detection based on evolution of feature in time domain,
MVA(29), No. 8, November 2018, pp. 1249-1256.
WWW Link. 1811
BibRef

Hou, L.[Le], Nguyen, V.[Vu], Kanevsky, A.B.[Ariel B.], Samaras, D.[Dimitris], Kurc, T.M.[Tahsin M.], Zhao, T.H.[Tian-Hao], Gupta, R.R.[Rajarsi R.], Gao, Y.[Yi], Chen, W.J.[Wen-Jin], Foran, D.[David], Saltz, J.H.[Joel H.],
Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images,
PR(86), 2019, pp. 188-200.
Elsevier DOI 1811
Pathology image analysis, Convolutional neural network, Unsupervised learning, Semi-supervised learning BibRef

Shi, J.[Jun], Zheng, X.[Xiao], Wu, J.J.[Jin-Jie], Gong, B.M.[Bang-Ming], Zhang, Q.[Qi], Ying, S.H.[Shi-Hui],
Quaternion Grassmann average network for learning representation of histopathological image,
PR(89), 2019, pp. 67-76.
Elsevier DOI 1902
Principal component analysis network (PCANet), Quaternion algebra, Color histopathological image BibRef

Wu, J.J.[Jin-Jie], Shi, J.[Jun], Ying, S.H.[Shi-Hui], Zhang, Q.[Qi], Li, Y.[Yan],
Learning Representation for Histopathological Image with Quaternion Grassmann Average Network,
MLMI16(122-129).
Springer DOI 1611
BibRef

Naylor, P., Laé, M., Reyal, F., Walter, T.,
Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map,
MedImg(38), No. 2, February 2019, pp. 448-459.
IEEE DOI 1902
Image segmentation, Cancer, Pathology, Task analysis, Biology, Tumors, Computer architecture, Cancer research, deep learning, nuclei segmentation BibRef

Tofighi, M., Guo, T., Vanamala, J.K.P., Monga, V.,
Prior Information Guided Regularized Deep Learning for Cell Nucleus Detection,
MedImg(38), No. 9, September 2019, pp. 2047-2058.
IEEE DOI 1909
Shape, Image edge detection, Computer architecture, Microprocessors, Deep learning, Biomedical imaging, learnable shapes BibRef

Gao, Q., Rohr, K.,
A Global Method for Non-Rigid Registration of Cell Nuclei in Live Cell Time-Lapse Images,
MedImg(38), No. 10, October 2019, pp. 2259-2270.
IEEE DOI 1910
Optical imaging, Biomedical optical imaging, Strain, Microscopy, Computational modeling, Optical microscopy, Optical scattering, Markov random field BibRef

Kumar, N.[Neeraj], Verma, R.[Ruchika], Anand, D.[Deepak], Zhou, Y.N.[Yan-Ning], Onder, O.F.[Omer Fahri], Tsougenis, E.[Efstratios], Chen, H.[Hao], Heng, P.A.[Pheng-Ann], Li, J.H.[Jia-Hui], Hu, Z.Q.[Zhi-Qiang], Wang, Y.Z.[Yun-Zhi], Koohbanani, N.A.[Navid Alemi], Jahanifar, M.[Mostafa], Tajeddin, N.Z.[Neda Zamani], Gooya, A.[Ali], Rajpoot, N.[Nasir], Ren, X.[Xuhua], Zhou, S.H.[Si-Hang], Wang, Q.[Qian], Shen, D.G.[Ding-Gang], Yang, C.K.[Cheng-Kun], Weng, C.H.[Chi-Hung], Yu, W.H.[Wei-Hsiang], Yeh, C.Y.[Chao-Yuan], Yang, S.[Shuang], Xu, S.[Shuoyu], Yeung, P.H.[Pak Hei], Sun, P.[Peng], Mahbod, A.[Amirreza], Schaefer, G.[Gerald], Ellinger, I.[Isabella], Ecker, R.[Rupert], Smedby, O.[Orjan], Wang, C.L.[Chun-Liang], Chidester, B.[Benjamin], Ton, T.V.[That-Vinh], Tran, M.T.[Minh-Triet], Ma, J.[Jian], Do, M.N.[Minh N.], Graham, S.[Simon], Vu, Q.D.[Quoc Dang], Kwak, J.T.[Jin Tae], Gunda, A.[Akshaykumar], Chunduri, R.[Raviteja], Hu, C.[Corey], Zhou, X.Y.[Xiao-Yang], Lotfi, D.[Dariush], Safdari, R.[Reza], Kascenas, A.[Antanas], O'Neil, A.[Alison], Eschweiler, D.[Dennis], Stegmaier, J.[Johannes], Cui, Y.P.[Yan-Ping], Yin, B.C.[Bao-Cai], Chen, K.L.[Kai-Lin], Tian, X.M.[Xin-Mei], Gruening, P.[Philipp], Barth, E.[Erhardt], Arbel, E.[Elad], Remer, I.[Itay], Ben-Dor, A.[Amir], Sirazitdinova, E.[Ekaterina], Kohl, M.[Matthias], Braunewell, S.[Stefan], Li, Y.X.[Yue-Xiang], Xie, X.P.[Xin-Peng], Shen, L.L.[Lin-Lin], Ma, J.[Jun], Baksi, K.D.[Krishanu Das], Khan, M.A.[Mohammad Azam], Choo, J.[Jaegul], Colomer, A.[Adrián], Naranjo, V.[Valery], Pei, L.M.[Lin-Min], Iftekharuddin, K.M.[Khan M.], Roy, K.[Kaushiki], Bhattacharjee, D.[Debotosh], Pedraza, A.[Anibal], Bueno, M.G.[Maria Gloria], Devanathan, S.[Sabarinathan], Radhakrishnan, S.[Saravanan], Koduganty, P.[Praveen], Wu, Z.H.[Zi-Han], Cai, G.Y.[Guan-Yu], Liu, X.J.[Xiao-Jie], Wang, Y.Q.[Yu-Qin], Sethi, A.[Amit],
A Multi-Organ Nucleus Segmentation Challenge,
MedImg(39), No. 5, May 2020, pp. 1380-1391.
IEEE DOI 2005
Image segmentation, Pathology, Image color analysis, Semantics, Machine learning algorithms, Task analysis, Deep learning, aggregated Jaccard index BibRef

Su, Y.T.[Yu-Ting], Lu, Y.[Yao], Liu, J.[Jing], Chen, M.[Mei], Liu, A.A.[An-An],
Spatio-Temporal Mitosis Detection in Time-Lapse Phase-Contrast Microscopy Image Sequences: A Benchmark,
MedImg(40), No. 5, May 2021, pp. 1319-1328.
IEEE DOI 2105
Microscopy, Image sequences, Feature extraction, Spatiotemporal phenomena, Visualization, Computer architecture, phase-contrast microscopy BibRef

Xing, F.Y.[Fu-Yong], Cornish, T.C.[Toby C.], Bennett, T.D.[Tellen D.], Ghosh, D.[Debashis],
Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images,
MedImg(40), No. 10, October 2021, pp. 2880-2896.
IEEE DOI 2110
Microscopy, Image segmentation, Training, Adaptation models, Task analysis, Annotations, Data models, Nucleus detection, microscopy image analysis BibRef

Verma, R.[Ruchika], Kumar, N.[Neeraj], Patil, A.[Abhijeet], Kurian, N.C.[Nikhil Cherian], Rane, S.[Swapnil], Graham, S.[Simon], Vu, Q.D.[Quoc Dang], Zwager, M.[Mieke], Raza, S.E.A.[Shan E. Ahmed], Rajpoot, N.[Nasir], Wu, X.[Xiyi], Chen, H.[Huai], Huang, Y.J.[Yi-Jie], Wang, L.S.[Li-Sheng], Jung, H.[Hyun], Brown, G.T.[G. Thomas], Liu, Y.L.[Yan-Ling], Liu, S.[Shuolin], Jahromi, S.A.F.[Seyed Alireza Fatemi], Khani, A.A.[Ali Asghar], Montahaei, E.[Ehsan], Baghshah, M.S.[Mahdieh Soleymani], Behroozi, H.[Hamid], Semkin, P.[Pavel], Rassadin, A.[Alexandr], Dutande, P.[Prasad], Lodaya, R.[Romil], Baid, U.[Ujjwal], Baheti, B.[Bhakti], Talbar, S.[Sanjay], Mahbod, A.[Amirreza], Ecker, R.[Rupert], Ellinger, I.[Isabella], Luo, Z.P.[Zhi-Peng], Dong, B.[Bin], Xu, Z.Y.[Zheng-Yu], Yao, Y.H.[Yue-Han], Lv, S.[Shuai], Feng, M.[Ming], Xu, K.[Kele], Zunair, H.[Hasib], Ben Hamza, A.[Abdessamad], Smiley, S.[Steven], Yin, T.K.[Tang-Kai], Fang, Q.R.[Qi-Rui], Srivastava, S.[Shikhar], Mahapatra, D.[Dwarikanath], Trnavska, L.[Lubomira], Zhang, H.Y.[Han-Yun], Narayanan, P.L.[Priya Lakshmi], Law, J.[Justin], Yuan, Y.[Yinyin], Tejomay, A.[Abhiroop], Mitkari, A.[Aditya], Koka, D.[Dinesh], Ramachandra, V.[Vikas], Kini, L.[Lata], Sethi, A.[Amit],
MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge,
MedImg(40), No. 12, December 2021, pp. 3413-3423.
IEEE DOI 2112
Annotations, Image segmentation, Tumors, Computer architecture, Training, Task analysis, Semantics, Multi-organ dataset, panoptic quality BibRef

Verma, R.[Ruchika], Kumar, N.[Neeraj], Patil, A.[Abhijeet], Kurian, N.C.[Nikhil Cherian], Rane, S.[Swapnil], Sethi, A.[Amit],
Author's Reply to 'MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge',
MedImg(41), No. 4, April 2022, pp. 1000-1003.
IEEE DOI 2204
Measurement, Image segmentation, Codes, Computer bugs, Biomedical imaging, Nucleus segmentation, MoNuSAC, challenges BibRef

Foucart, A.[Adrien], Debeir, O.[Olivier], Decaestecker, C.[Christine],
Comments on 'MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge',
MedImg(41), No. 4, April 2022, pp. 997-999.
IEEE DOI 2204
Digital pathology, challenge, nuclei segmentation, nuclei classification BibRef

Liu, S.S.[Shan-Shan], Hu, R.[Ruo], Wu, J.[Jianfang], Zhang, X.[Xizheng], He, J.[Jun], Zhao, H.M.[Hui-Min], Wang, H.[Huajia], Li, X.J.[Xiang-Jun],
Research on data classification and feature fusion method of cancer nuclei image based on deep learning,
IJIST(32), No. 3, 2022, pp. 969-981.
DOI Link 2205
cancer cell image, classification, deep learning, feature fusion model, multiscale image BibRef

Khan, R.[Rafflesia], Debnath, R.[Rameswar],
Morphology Preserving Segmentation Method for Occluded Cell Nuclei from Medical Microscopy Image,
IJIG(22), No. 2, April 2022, pp. 2250019.
DOI Link 2205
BibRef

Li, Y.H.[Yong-Hui], Xue, Y.[Yao], Li, L.F.[Liang-Fu], Zhang, X.J.[Xing-Jun], Qian, X.M.[Xue-Ming],
Domain Adaptive Box-Supervised Instance Segmentation Network for Mitosis Detection,
MedImg(41), No. 9, September 2022, pp. 2469-2485.
IEEE DOI 2209
Feature extraction, Image segmentation, Head, Adaptation models, Annotations, Training, Semantics, Mitosis detection, pesudo masks BibRef

Chen, S.C.[Sheng-Cong], Ding, C.X.[Chang-Xing], Liu, M.F.[Min-Feng], Cheng, J.[Jun], Tao, D.C.[Da-Cheng],
CPP-Net: Context-Aware Polygon Proposal Network for Nucleus Segmentation,
IP(32), 2023, pp. 980-994.
IEEE DOI 2302
Shape, Image segmentation, Feature extraction, Proposals, Robustness, Task analysis, Predictive models, Nucleus segmentation, perceptual loss BibRef

Wang, J.[Juan], Zhang, Z.[Zetao], Wu, M.[Minghu], Ye, Y.G.[Yong-Gang], Wang, S.[Sheng], Cao, Y.[Ye], Yang, H.[Hao],
Improved BlendMask: Nuclei instance segmentation for medical microscopy images,
IET-IPR(17), No. 7, 2023, pp. 2284-2296.
DOI Link 2305
image segmentation, convolutional neural nets, image processing, medical image processing BibRef

Zhou, Y.[Yang], Wu, Y.J.[Yong-Jian], Wang, Z.[Zihua], Wei, B.Z.[Bing-Zheng], Lai, M.[Maode], Shou, J.Z.[Jian-Zhong], Fan, Y.[Yubo], Xu, Y.[Yan],
Cyclic Learning: Bridging Image-Level Labels and Nuclei Instance Segmentation,
MedImg(42), No. 10, October 2023, pp. 3104-3116.
IEEE DOI 2310
BibRef

Wu, H.[Huisi], Wang, Z.Z.[Zhao-Ze], Zhao, Z.B.[Ze-Bin], Chen, C.[Cheng], Qin, J.[Jing],
Continual Nuclei Segmentation via Prototype-Wise Relation Distillation and Contrastive Learning,
MedImg(42), No. 12, December 2023, pp. 3794-3804.
IEEE DOI Code:
WWW Link. 2312
BibRef

Lim, S.[Seohoon], Xu, Z.X.[Zhi-Xin], Chong, Y.[Yosep], Jung, S.W.[Seung-Won],
CAWM: Class-Aware Weight Map for Improved Semi-Supervised Nuclei Segmentation,
SPLetters(31), 2024, pp. 81-85.
IEEE DOI 2401
BibRef

Zhou, H.Y.[Hao-Yang], Feng, B.[Bao], Chen, H.B.[Hong-Bo],
Segmentation of nucleus based on dynamic convolution and deep features of stain distribution,
IET-IPR(18), No. 4, 2024, pp. 939-950.
DOI Link 2403
deep learning, nucleus, pathological image, segmentation BibRef

Lin, Y.[Yi], Wang, Z.[Zeyu], Zhang, D.[Dong], Cheng, K.T.[Kwang-Ting], Chen, H.[Hao],
BoNuS: Boundary Mining for Nuclei Segmentation With Partial Point Labels,
MedImg(43), No. 6, June 2024, pp. 2137-2147.
IEEE DOI Code:
WWW Link. 2406
Annotations, Image segmentation, Cancer, Histopathology, Technological innovation, Task analysis, Manuals, pixel affinity BibRef

Fan, J.A.[Jian-An], Liu, D.N.[Dong-Nan], Chang, H.[Hang], Cai, W.D.[Wei-Dong],
Learning to Generalize over Subpartitions for Heterogeneity-Aware Domain Adaptive Nuclei Segmentation,
IJCV(132), No. 8, August 2024, pp. 2861-2884.
Springer DOI 2408
BibRef

Utz, J.[Jonas], Weise, T.[Tobias], Schlereth, M.[Maja], Wagner, F.[Fabian], Thies, M.[Mareike], Gu, M.X.[Ming-Xuan], Uderhardt, S.[Stefan], Breininger, K.[Katharina],
Focus on Content not Noise: Improving Image Generation for Nuclei Segmentation by Suppressing Steganography in CycleGAN,
BioIm23(3858-3866)
IEEE DOI 2401
BibRef

Hassan, T.[Taimur], Abdalla, M.[Moshira], Raja, H.[Hina], Owais, M.[Muhammad], Werghi, N.[Naoufel],
Robust Nucleus Classification with Iterative Graph Representational Learning,
ICIP23(3414-3418)
IEEE DOI 2312
BibRef

Han, Y.[Yue], Lei, Y.[Yang], Shkolnikov, V.[Viktor], Xin, D.[Daisy], Auduong, A.[Alicia], Barcelo, S.[Steven], Allebach, J.[Jan], Delp, E.J.[Edward J.],
An Ensemble Method with Edge Awareness for Abnormally Shaped Nuclei Segmentation,
CVMI23(4315-4325)
IEEE DOI 2309
BibRef

Bonte, T.[Thomas], Philbert, M.[Maxence], Coleno, E.[Emeline], Bertrand, E.[Edouard], Imbert, A.[Arthur], Walter, T.[Thomas],
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BioImage22(423-436).
Springer DOI 2304
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Magoulianitis, V.[Vasileios], Han, P.[Peida], Yang, Y.J.[Yi-Jing], Kuo, C.C.J.[C.C. Jay],
An Unsupervised Parameter-Free Nuclei Segmentation Method for Histology Images,
ICIP22(226-230)
IEEE DOI 2211
Dimensionality reduction, Image segmentation, Histopathology, Image color analysis, Transforms, Indexes, Histology Images, Morphological Processing BibRef

Habis, A.[Antoine], Meas-Yedid, V.[Vannary], Obando, D.F.G.[Daniel Felipe González], Olivo-Marin, J.C.[Jean-Christophe], Angelini, E.D.[Elsa D.],
Smart Learning of Click and Refine for Nuclei Segmentation on Histology Images,
ICIP22(2281-2285)
IEEE DOI 2211
Deep learning, Image segmentation, Histopathology, Glands, Manuals, Educational technology, Medical diagnosis, Histology images, visual similarity BibRef

Zhao, W.[Wanbi], Zhou, X.[Xu],
Bi-Polar Mask for Joint Cell and Nuclei Instance Segmentation,
ICIP22(3421-3425)
IEEE DOI 2211
Image segmentation, Pathology, Annotations, Cervical cancer, cell segmentation, overlapping, nuclei segmentation, computational pathology BibRef

Wu, L.M.[Li-Ming], Chen, A.[Alain], Salama, P.[Paul], Dunn, K.W.[Kenneth W.], Delp, E.J.[Edward J.],
3D Centroidnet: Nuclei Centroid Detection with Vector Flow Voting,
ICIP22(651-655)
IEEE DOI 2211
Training, Codes, Annotations, Microscopy, Biological tissues, Nuclei centroid detection, voting mechanism BibRef

Wu, L.M.[Li-Ming], Chen, A.[Alain], Salama, P.[Paul], Dunn, K.W.[Kenneth W.], Delp, E.J.[Edward J.],
An Ensemble Learning and Slice Fusion Strategy for Three-Dimensional Nuclei Instance Segmentation,
CVMI22(1883-1893)
IEEE DOI 2210
Training, Pathology, Image segmentation, Annotations, Microscopy, Supervised learning BibRef

Naghizadeh, A.[Alireza], Xu, H.Y.[Hong-Ye], Mohamed, M.[Mohab], Metaxas, D.N.[Dimitris N.], Liu, D.F.[Dong-Fang],
Semantic Aware Data Augmentation for Cell Nuclei Microscopical Images with Artificial Neural Networks,
ICCV21(3932-3941)
IEEE DOI 2203
Training, Image segmentation, Microscopy, Microprocessors, Semantics, Training data, Computer architecture, Medical, biological, Neural generative models BibRef

He, H.L.[Hong-Liang], Huang, Z.Y.[Zhong-Yi], Ding, Y.[Yao], Song, G.[Guoli], Wang, L.[Lin], Ren, Q.[Qian], Wei, P.X.[Peng-Xu], Gao, Z.Q.[Zhi-Qiang], Chen, J.[Jie],
CDNet: Centripetal Direction Network for Nuclear Instance Segmentation,
ICCV21(4006-4015)
IEEE DOI 2203
Image segmentation, Pathology, Codes, Aggregates, Task analysis, Nuclear measurements, Medical, biological, and cell microscopy, grouping and shape BibRef

Feng, Z.L.[Zun-Lei], Wang, Z.H.[Zhong-Hua], Wang, X.C.[Xin-Chao], Mao, Y.N.[Yi-Ning], Li, T.[Thomas], Lei, J.[Jie], Wang, Y.X.[Yue-Xuan], Song, M.L.[Ming-Li],
Mutual-Complementing Framework for Nuclei Detection and Segmentation in Pathology Image,
ICCV21(4016-4025)
IEEE DOI 2203
Image segmentation, Pathology, Manuals, Robustness, Clinical diagnosis, Cancer, Medical, biological, and cell microscopy, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Hossain, M.S.[Md Shamim], Armstrong, L.J.[Leisa J.], Abu-Khalaf, J.[Jumana], Cook, D.M.[David M.], Zaenker, P.[Pauline],
Overlapping Cell Nuclei Segmentation in Digital Histology Images using Intensity-based Contours,
DICTA21(1-9)
IEEE DOI 2201
Image segmentation, Image analysis, Histopathology, Annotations, Shape, Microprocessors, Digital images, Overlapping cell nuclei, Nucleus contour BibRef

Nejatbakhsh, A.[Amin], Varol, E.[Erdem],
Neuron matching in C. elegans with robust approximate linear regression without correspondence,
WACV21(2836-2845)
IEEE DOI 2106
Neurons, Linear regression, Computational neuroscience, Approximation algorithms, Noise measurement BibRef

Ling, C., Halter, M., Plant, A., Majurski, M., Stinson, J., Chalfoun, J.,
Analyzing U-Net Robustness for Single Cell Nucleus Segmentation from Phase Contrast Images,
Microscopy20(4157-4163)
IEEE DOI 2008
Image segmentation, Training, Computer architecture, Microprocessors, Microscopy, Manuals BibRef

Jackson, P.T.[Philip T.], Wang, Y.[Yinhai], Knight, S.[Sinead], Chen, H.M.[Hong-Ming], Dorval, T.[Thierry], Brown, M.[Martin], Bendtsen, C.[Claus], Obara, B.[Boguslaw],
Phenotypic Profiling of High Throughput Imaging Screens with Generic Deep Convolutional Features,
MVA19(1-4)
DOI Link 1911
biomedical optical imaging, cellular biophysics, drugs, fluorescence, medical image processing, pattern clustering, Neural networks BibRef

Narotamo, H.[Hemaxi], Sanches, J.M.[J. Miguel], Silveira, M.[Margarida],
Segmentation of Cell Nuclei in Fluorescence Microscopy Images Using Deep Learning,
IbPRIA(I:53-64).
Springer DOI 1910
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On Generative Modeling of Cell Shape Using 3D GANs,
CIAP19(II:672-682).
Springer DOI 1909
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Javer, A.[Avelino], Brown, A.E.X.[André E. X.], Kokkinos, I.[Iasonas], Rittscher, J.[Jens],
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Zhang, J., Chen, Y., Kuo, Y., Chen, C.,
Fast automatic segmentation of cells and nucleuses in large-scale liquid-based monolayer smear images,
IVCNZ17(1-6)
IEEE DOI 1902
cancer, image enhancement, image segmentation, iterative methods, medical image processing, monolayers, Cervical Cancer Screening BibRef

Kalinin, A.A., Allyn-Feuer, A., Ade, A., Fon, G., Meixner, W., Dilworth, D., de Wet, J.R., Higgins, G.A., Zheng, G., Creekmore, A., Wiley, J.W., Verdone, J.E., Veltri, R.W., Pienta, K.J., Coffey, D.S., Athey, B.D., Dinov, I.D.,
3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results,
Microscopy18(2353-23538)
IEEE DOI 1812
Computer architecture, Image segmentation, Microprocessors, Microscopy, Morphology BibRef

Tofighi, M., Guo, T., Vanamala, J.K.P., Monga, V.,
Deep Networks with Shape Priors for Nucleus Detection,
ICIP18(719-723)
IEEE DOI 1809
Shape, Image edge detection, Training, Computer architecture, Biomedical imaging, Feature extraction, shape priors BibRef

Rad, R.M., Saeedi, P., Au, J., Havelock, J.,
Multi-Resolutional Ensemble of Stacked Dilated U-Net for Inner Cell Mass Segmentation in Human Embryonic Images,
ICIP18(3518-3522)
IEEE DOI 1809
Image segmentation, Embryo, Indexes, Convolution, Training, Kernel, Machine learning, IVF, Human Embryo, Inner Cell Mass, Segmentation, Deep Learning BibRef

de Mesquita Sá, Jr., J.J.[Jarbas Joaci], Backes, A.R.[André R.], Bruno, O.M.[Odemir Martinez],
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Transfer Learning for Cell Nuclei Classification in Histopathology Images,
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Scherzinger, A.[Aaron], Kleene, F.[Florian], Dierkes, C.[Cathrin], Kiefer, F.[Friedemann], Hinrichs, K.H.[Klaus H.], Jiang, X.Y.[Xiao-Yi],
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GCPR16(105-116).
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Bortsova, G.[Gerda], Sterr, M.[Michael], Wang, L.C.[Li-Chao], Milletari, F.[Fausto], Navab, N.[Nassir], Böttcher, A.[Anika], Lickert, H.[Heiko], Theis, F.[Fabian], Peng, T.Y.[Ting-Ying],
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MLMI16(287-295).
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Fouad, S.[Shereen], Landini, G.[Gabriel], Randell, D.[David], Galton, A.[Antony],
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ICIAR16(599-607).
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Multi-genomic curve extraction,
MVA15(283-286)
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ICCVG16(174-183).
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Oszutowska-Mazurek, D.[Dorota], Mazurek, P.[Przemyslaw], Sycz, K.[Kinga], Waker-Wójciuk, G.[Grazyna],
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ICIP13(670-674)
IEEE DOI 1402
Algorithm design and analysis BibRef

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Gene Expression, Genome .