Automated evaluation of HER-2/neu immunohistochemical expression in breast cancer using digital microscopy

Gavrielides, Marios A.; Masmoudi, Héla; Petrick, Nicholas; Myers, K.J.; Hewitt, Stephen M.

doi:10.1109/isbi.2008.4541119

Cited by 12 publications

(9 citation statements)

References 7 publications

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“…Features Methods (Gavrielides et al, 2008) Feature vector constructed using the RGB, HSV and LAB values that makes in total 9 features Colour pixel classifier followed by nuclei segmentation and ellipse fitting Linear regression and minimum cluster distance approach (Tuominen et al, 2012) ImmunoMembrane Colour features; membrane completeness and intensity of membrane from coefficients obtained using peak kappa agreement with benchmark data…”

Section: Techniquesmentioning

confidence: 99%

AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers

Tewary

Arun

Ahmed

et al. 2020

Journal of Microscopy

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Summary Human epidermal growth factor receptor 2 (HER2) is one of the widely used Immunohistochemical (IHC) markers for prognostic evaluation amongst the patient of breast cancer. Accurate quantification of cell membrane is essential for HER2 scoring in therapeutic decision making. In modern laboratory practice, expert pathologist visually assesses the HER2‐stained tissue sample under the bright field microscope for cell membrane assessment. This manual assessment is time consuming, tedious and quite often results in interobserver variability. Further, the burden of increasing number of patients is a challenge for the pathologists. To address these challenges, there is an urgent need with a rapid HER2 cell membrane extraction method. The proposed study aims at developing an automated IHC scoring system, termed as AutoIHC‐Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. A series of image processing approaches have been used to automatically extract the stained cells and membrane region, followed by automatic assessment of complete and broken membrane. Finally, a set of features are used to automatically classify the tissue under observation for the quantitative scoring as 0/1+, 2+ and 3+. In a set of surgically extracted cases of HER2‐stained tissues, obtained from collaborative hospital for the testing and validation of the proposed approach AutoIHC‐Analyzer and publicly available open source ImmunoMembrane software are compared for 90 set of randomly acquired images with the scores by expert pathologist where significant correlation is observed [(r = 0.9448; p < 0.001) and (r = 0.8521; p < 0.001)] respectively. The output shows promising quantification in automated scoring. Lay description In cancer prognosis amongst the patient of breast cancer, human epidermal growth factor receptor 2 (HER2) is used as Immunohistochemical (IHC) biomarker. The correct assessment of HER2 leads to the therapeutic decision making. In regular practice, the stained tissue sample is observed under a bright microscope and the expert pathologists score the sample as negative (0/1+), equivocal (2+) and positive (3+) case. The scoring is based on the standard guidelines relating the complete and broken cell membrane as well as intensity of staining in the membrane boundary. Such evaluation is time consuming, tedious and quite often results in interobserver variability. To assist in rapid HER2 cell membrane assessment, the proposed study aims at developing an automated IHC scoring system, termed as AutoIHC‐Analyzer, for automated cell membrane extraction followed by HER2 molecular expression assessment from stained tissue images. The input image is preprocessed using modified white patch and CMYK and RGB colour space were used in extracting the haematoxylin (negatively stained cells) and diaminobenzidine (DAB) stain observed in the tumour cell membrane. Segmentation and postprocessing are applied to create the masks for each of the stain channels. The membrane ma...

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Section: Techniquesmentioning

confidence: 99%

AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers

Tewary

Arun

Ahmed

et al. 2020

Journal of Microscopy

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“…Another category of nuclear segmentation techniques based on geometrical templates or on fixed models of the tissue morphology, such as in [86] [87], suffer due over generalization because of the non predictable shape and size variations of the cells; these variations are induced by the pathology as well as by the mechanical and thermal stress related to the preparation of the sample.…”

Section: Segmentation Of Nucleimentioning

confidence: 99%

“…[107] applied gaussian process ordinal regression and supervised neural networks to predict the scores of immunostained tissue microarray spots. [86] and [91] distinguished membrane stained specimens into 1+, 2+ and 3+ scores through Minimum Cluster Distance Classifier with parameters learnt on a pre-scored training dataset. [108] proposed an automated procedure that faithfully replicates the guidelines of the American Society of Clinical Oncology and the College of American Pathologists [38], using SVMs trained with visual scores to distinguish between 2+ and 3+ samples.…”

Section: Ihc Quantification and Semi-quantificationmentioning

confidence: 99%

“…6, the scatter plot suffers significant overlap of 0 and 1+ HER2 scores, as well as equivocal cases of 2+ scoring (see red and yellow circles, respectively). Even more severe overlaps were experienced by [86] and [91] in images with membrane staining. This definitely confirms the concerns about the the opportunity of using the scores of the pathologist as a golden stardard for the automated quantification techniques.…”

Section: Ihc Quantification and Semi-quantificationmentioning

confidence: 99%

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Computer-aided techniques for chromogenic immunohistochemistry: Status and directions

Cataldo

Ficarra

Macii

2012

Computers in Biology and Medicine

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Although immunohistochemistry (IHC) is a popular imaging technique, the quantitative analysis of IHC images via computer-aided methods is an emerging field that is gaining more and more importance thanks to the new developments in digital high-throughput scanners. In this paper, we discuss the main steps of IHC and review the techniques for computer-aided chromogenic IHC analysis, including methods to determine the location of interest of the antigens and quantify their activations. Moreover, we discuss the issues arising from the standardization of the immunostaining process, that are generally overlooked by the current literature, and finally provide requirements for reliable computer-aided IHC quantification.

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“…Most of the recent works have been proved to be effective with nuclear segmentation but do not address cellular membranes' segmentation [20], [21], [22] or are semi-automated in that they need a certain amount of user-intervention to add control points close to the target membrane boundary [23], [24]. Other recent works rely on elliptic approximation of the target membranes [25], [26], which does not reflect much the real morphology of several pathological tissues (see Fig. 1 for examples).…”

Section: Introductionmentioning

confidence: 99%

Automated Segmentation of Cells With IHC Membrane Staining

Ficarra

Cataldo

Acquaviva

et al. 2011

IEEE Trans. Biomed. Eng.

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This study presents a fully automated membrane segmentation technique for immunohistochemical tissue images with membrane staining, which is a critical task in computerized immunohistochemistry (IHC). Membrane segmentation is particularly tricky in immunohistochemical tissue images because the cellular membranes are visible only in the stained tracts of the cell, while the unstained tracts are not visible. Our automated method provides accurate segmentation of the cellular membranes in the stained tracts and reconstructs the approximate location of the unstained tracts using nuclear membranes as a spatial reference. Accurate cell-by-cell membrane segmentation allows per cell morphological analysis and quantification of the target membrane proteins that is fundamental in several medical applications such as cancer characterization and classification, personalized therapy design, and for any other applications requiring cell morphology characterization. Experimental results on real datasets from different anatomical locations demonstrate the wide applicability and high accuracy of our approach in the context of IHC analysis.

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Automated evaluation of HER-2/neu immunohistochemical expression in breast cancer using digital microscopy

Cited by 12 publications

References 7 publications

AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers

AutoIHC‐Analyzer: computer‐assisted microscopy for automated membrane extraction/scoring in HER2 molecular markers

Computer-aided techniques for chromogenic immunohistochemistry: Status and directions

Automated Segmentation of Cells With IHC Membrane Staining

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