QuPath: Open source software for digital pathology image analysis
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Peter Bankhead | Manuel Salto-Tellez | Maurice B Loughrey | José A Fernández | Yvonne Dombrowski | Darragh G McArt | Philip D Dunne | Stephen McQuaid | Ronan T Gray | Liam J Murray | Helen G Coleman | Jacqueline A James | Peter W Hamilton | M. Salto‐Tellez | P. Hamilton | P. Bankhead | D. McArt | P. Dunne | J. James | S. McQuaid | H. Coleman | M. Loughrey | R. Gray | Yvonne Dombrowski | L. J. Murray | L. Murray
[1] Stephen McQuaid,et al. The prognostic significance of the aberrant extremes of p53 immunophenotypes in breast cancer , 2014, Histopathology.
[2] Anne E Carpenter,et al. CellProfiler: free, versatile software for automated biological image analysis. , 2007, BioTechniques.
[3] Anthony G Gallagher,et al. Do we see what we think we see? The complexities of morphological assessment , 2009, The Journal of pathology.
[4] M. Salto‐Tellez,et al. Digital pathology and image analysis in tissue biomarker research. , 2014, Methods.
[5] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[6] Lik Hang Lee,et al. Patterns and prognostic relevance of PD-1 and PD-L1 expression in colorectal carcinoma , 2016, Modern Pathology.
[7] Mahadev Satyanarayanan,et al. OpenSlide: A vendor-neutral software foundation for digital pathology , 2013, Journal of pathology informatics.
[8] R W Blamey,et al. A new immunohistochemical antibody for the assessment of estrogen receptor status on routine formalin-fixed tissue samples. , 1995, Human pathology.
[9] John M S Bartlett,et al. An international Ki67 reproducibility study. , 2013, Journal of the National Cancer Institute.
[10] Gilles Louppe,et al. Collaborative analysis of multi-gigapixel imaging data using Cytomine , 2016, Bioinform..
[11] Pascal Fua,et al. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[12] D. Lane,et al. P53 abnormalities and outcomes in colorectal cancer: a systematic review , 2005, British Journal of Cancer.
[13] J. Lunceford,et al. Pembrolizumab for the treatment of non-small-cell lung cancer. , 2015, The New England journal of medicine.
[14] Johannes E. Schindelin,et al. Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.
[15] A. Ruifrok,et al. Quantification of histochemical staining by color deconvolution. , 2001, Analytical and quantitative cytology and histology.
[16] E. Steyerberg,et al. Reporting and Methods in Clinical Prediction Research: A Systematic Review , 2012, PLoS medicine.
[17] J. Pyo,et al. Prognostic Role of PD-L1 in Malignant Solid Tumors: A Meta-Analysis , 2016, The International journal of biological markers.
[18] Hans J. Tanke,et al. The Carcinoma–Stromal Ratio of Colon Carcinoma Is an Independent Factor for Survival Compared to Lymph Node Status and Tumor Stage , 2007, Cellular oncology : the official journal of the International Society for Cellular Oncology.
[19] Kerstin Pingel,et al. 50 Years of Image Analysis , 2012 .
[20] Vilppu J Tuominen,et al. ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 , 2010, Breast Cancer Research.
[21] C Blake Gilks,et al. Patterns of p53 immunoreactivity in endometrial carcinomas: ‘all or nothing’ staining is of importance , 2011, Histopathology.
[22] Mark Lawler,et al. Immune-Derived PD-L1 Gene Expression Defines a Subgroup of Stage II/III Colorectal Cancer Patients with Favorable Prognosis Who May Be Harmed by Adjuvant Chemotherapy , 2016, Cancer Immunology Research.
[23] Gerard Pasterkamp,et al. SlideToolkit: An Assistive Toolset for the Histological Quantification of Whole Slide Images , 2014, PloS one.
[24] F. Schmidt. Meta-Analysis , 2008 .
[25] Robert M. Haralick,et al. Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..
[26] Peter Dalgaard,et al. R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .
[27] Douglas G Altman,et al. Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration , 2012, BMC Medicine.
[28] P. Grambsch,et al. A Package for Survival Analysis in S , 1994 .
[29] Sidra Nawaz,et al. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology , 2015, Laboratory Investigation.
[30] Kevin W Eliceiri,et al. NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.
[31] John D. Pfeifer,et al. Review of the current state of whole slide imaging in pathology , 2011, Journal of pathology informatics.
[32] Anne E Carpenter,et al. A call for bioimaging software usability , 2012, Nature Methods.
[33] Douglas G. Altman,et al. Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK): Explanation and Elaboration , 2012, PLoS medicine.
[34] Marc Lartaud,et al. Analyzing huge pathology images with open source software , 2013, Diagnostic Pathology.
[35] E. Mohammadi,et al. Barriers and facilitators related to the implementation of a physiological track and trigger system: A systematic review of the qualitative evidence , 2017, International journal for quality in health care : journal of the International Society for Quality in Health Care.
[36] Nicolas Chenouard,et al. Icy: an open bioimage informatics platform for extended reproducible research , 2012, Nature Methods.
[37] B F Warren,et al. The proportion of tumor-stroma as a strong prognosticator for stage II and III colon cancer patients: validation in the VICTOR trial. , 2013, Annals of oncology : official journal of the European Society for Medical Oncology.
[38] Z. Trajanoski,et al. Type, Density, and Location of Immune Cells Within Human Colorectal Tumors Predict Clinical Outcome , 2006, Science.
[39] C. Rueden,et al. Metadata matters: access to image data in the real world , 2010, The Journal of cell biology.