Journal: Diagnostic Pathology

Article Title: Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring

doi: 10.1186/1746-1596-7-42

Figure Lengend Snippet: Manual and automated annotations of ovarian serous carcinoma. Ovarian serous carcinoma TMA spots immunohistochemically stained for S100A1. Representative lowly and highly stained spots are shown ( A-B ). Image data were processed by both manual pathologist-supervised hand annotations and automated Genie Histology Pattern Recognition software. Digital hand annotations are presented as green outlines of carcinoma, excluding stroma and minimizing background and glass ( C-D ). These same TMA spots were classified by Genie as carcinoma (dark blue), stroma (yellow), and glass (light blue) ( E-F ).

Article Snippet: In this study, we used TMAs of ovarian serous carcinomas stained with an antibody directed against S100A1 to determine the ability of commercially available software algorithms (Genie Histology Pattern Recognition software suite including Genie Training v1 and Genie Classifier v1, and Color Deconvolution v9, Aperio Technologies, Vista, CA, USA) to replicate results obtained solely through visual inspection by a pathologist.

Techniques: Staining, Software

Journal: Diagnostic Pathology

Article Title: Quantitative comparison of immunohistochemical staining measured by digital image analysis versus pathologist visual scoring

doi: 10.1186/1746-1596-7-42

Figure Lengend Snippet: Representative comparisons of pathologist visual scoring with automated IHC measurement. Ovarian serous carcinoma TMA spots stained for S100A1 were interpreted by pathologist visual scoring as 0 (no staining), 1 (<10% of carcinoma staining), 2 (10%-50% of carcinoma staining), or 3 (>50% of carcinoma staining). Representative spot for each score is shown as A-D; each column shows the identical TMA spot processed by digital methods. Genie Histology Pattern Recognition software classified tissue areas into carcinoma (dark blue), stroma (yellow), or glass (light blue) ( E-H ). Color Deconvolution software individually analyzed DAB staining (deconvolved by its RGB color components; I-L ), and measured staining intensity only within areas classified as carcinoma. Pseudocolors represent staining intensity in shown as M-P (gray = image areas not annotated by Genie as carcinoma and therefore not considered; blue = no staining, yellow = low intensities, orange = medium intensities, and red = high intensities in Genie-annotated carcinomatous areas considered).

Article Snippet: In this study, we used TMAs of ovarian serous carcinomas stained with an antibody directed against S100A1 to determine the ability of commercially available software algorithms (Genie Histology Pattern Recognition software suite including Genie Training v1 and Genie Classifier v1, and Color Deconvolution v9, Aperio Technologies, Vista, CA, USA) to replicate results obtained solely through visual inspection by a pathologist.

Techniques: Staining, Software

Journal: Cancers

Article Title: Lysyl-Oxidase Dependent Extracellular Matrix Stiffness in Hodgkin Lymphomas: Mechanical and Topographical Evidence

doi: 10.3390/cancers14010259

Figure Lengend Snippet: Digital pathology imaging and measurement of lymph node stroma in HL and NHL. Slides of hematoxilin-eosin (HE, representative cases in Panel ( A )) or Mallory trichrome staining (representative cases in Panel ( B )) from LN sections of patients with HL ( n = 11), FL1-2 ( n = 6), FL3A ( n = 8), DLBCL ( n = 6), compared with 9 LDN, were subjected to Digital Pathology Imaging. Panel ( C ). HE sections were analyzed with the Genie software (right images in Panel ( A )) and the percentage area of stromal compartment per slide was calculated, adjusted to total tissue area in the image analyzed. Data are expressed as a percentage of green pseudocolor (stromal) areas and are the mean ± SEM of the indicated number of cases for each histological type. * p < 0.05 vs. LDN, FL1-2 and FL3A. Panel ( D ): digital images of Mallory trichrome stained sections were analyzed with an automated image analysis algorithm for color deconvolution (right images in Panel ( B )) and collagen areas quantified by the Image Scope software. Data are expressed as percentage of blue (collagen) areas and are the mean ± SEM of the indicated number of cases for each histological type. * p < 0.005 vs. LDN, FL1-2 and FL3A.

Article Snippet: Layers of each virtual slide annotation were created manually, selecting the whole area, and analyzed with the automated image analysis algorithm color deconvolution (Aperio software version 9.1, Aperio Technologies, Nussloch GmbH, Wetzlar, Germany).

Techniques: Imaging, Staining, Software

Journal: Methods in enzymology

Article Title: Quantitative methods to investigate the 4D dynamics of heterochromatic repair sites in Drosophila cells

doi: 10.1016/bs.mie.2017.11.033

Figure Lengend Snippet: Underexposure of the cells enables the long and frequent image collection required for focus tracking while minimizing cell damage and photobleaching. The application of deconvolution and equalization algorithms enables recovery of most details in underexposed images. In the example shown, a Kc cell stably expressing mEGFP-Mu2 and mCh-HP1a shows an overall enrichment of Mu2/Mdc1 signals inside the heterochromatin domain before IR (-IR), and damage foci associated with the heterochromatin domain at 10 min after IR (+IR) (Ryu et al., 2015). The signal has been collected first in underexposed conditions (5 ms for mCh and 20 ms for GFP, with 10% T), then in optimal imaging conditions (12 ms for mCh and 20 ms for GFP, with 100% T), for both time points as indicated. The noisy signal of raw underexposed images is significantly improved by post-image processing (deconvolution and equalization), revealing even the weak signals associated with small foci (arrowheads). Images are max intensity projections of one nucleus. Scale bar = 1μm.

Article Snippet: This results in underexposing the image, but most image details can be recovered post-imaging by optimized deconvolution and photobleaching correction algorithms available in SoftWorX ( and Section 1.5 of this protocol). fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window caption a7 caption a8 Image details in underexposed samples are recovered with post-image processing Underexposure of the cells enables the long and frequent image collection required for focus tracking while minimizing cell damage and photobleaching.

Techniques: Stable Transfection, Expressing, Imaging