Analyzing huge pathology images with open source software

[1]  Stephen R. Thomas,et al.  Quantitative characterization of the imaging limits of diffuse low-grade oligodendrogliomas. , 2013, Neuro-Oncology.

[2]  A. Janin,et al.  Stack or trash? Quality assessment of virtual slides , 2013, Diagnostic Pathology.

[3]  Bahram Parvin,et al.  Invariant Delineation of Nuclear Architecture in Glioblastoma Multiforme for Clinical and Molecular Association , 2013, IEEE Transactions on Medical Imaging.

[4]  Masaru Ishii,et al.  CognitionMaster: an object-based image analysis framework , 2013, Diagnostic Pathology.

[5]  Matloob Khushi,et al.  Open source tools for management and archiving of digital microscopy data to allow integration with patient pathology and treatment information , 2013, Diagnostic Pathology.

[6]  David Ameisen.  Intégration des lames virtuelles dans le dossier patient électronique , 2013 .

[7]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[8]  Klaus Kayser,et al.  Introduction of virtual microscopy in routine surgical pathology - a hypothesis and personal view from Europe , 2012, Diagnostic Pathology.

[9]  Alexandre Granier,et al.  WIDE-Web Image and Data Environment , 2012 .

[10]  Xiang Li,et al.  Estimating the ground truth from multiple individual segmentations incorporating prior pattern analysis with application to skin lesion segmentation , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Marcial García Rojo,et al.  COST Action “EuroTelepath”: digital pathology integration in electronic health record, including primary care centres , 2011, Diagnostic pathology.

[12]  Jun Kong,et al.  A comprehensive framework for classification of nuclei in digital microscopy imaging: An application to diffuse gliomas , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[13]  Klaus Kayser,et al.  Grid computing in image analysis , 2011, Diagnostic pathology.

[14]  C. Rueden,et al.  Metadata matters: access to image data in the real world , 2010, The Journal of cell biology.

[15]  H. Grabsch,et al.  The proportion of tumour cells is an independent predictor for survival in colorectal cancer patients , 2010, British Journal of Cancer.

[16]  Gloria Bueno,et al.  Review of imaging solutions for integrated quantitative immunohistochemistry in the Pathology daily practice. , 2010, Folia histochemica et cytobiologica.

[17]  A. Madabhushi,et al.  Histopathological Image Analysis: A Review , 2009, IEEE Reviews in Biomedical Engineering.

[18]  P. Maxwell,et al.  Advanced Techniques in Diagnostic Cellular Pathology. , 2009 .

[19]  G. Kayser,et al.  Towards an automated virtual slide screening: theoretical considerations and practical experiences of automated tissue-based virtual diagnosis to be implemented in the Internet , 2006, Diagnostic pathology.

[20]  Cigdem Demir,et al.  Augmented cell-graphs for automated cancer diagnosis , 2005, ECCB/JBI.

[21]  J Lundin,et al.  Virtual microscopy , 2004, Journal of Clinical Pathology.

[22]  Weiqin Jiang,et al.  Using nuclear morphometry to discriminate the tumorigenic potential of cells: a comparison of statistical methods. , 2004, Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology.

[23]  Cigdem Demir,et al.  The cell graphs of cancer , 2004, ISMB/ECCB.

[24]  D. B. Davis,et al.  Sun Microsystems Inc. , 1993 .

[25]  V. Kosma,et al.  Application of morphometry in tumor pathology. , 1987, Analytical and quantitative cytology and histology.