Computer-Aided Evaluation of Protein Expression in Pathological Tissue Images

Ficarra, Elisa; Macii, Enrico; Micheli, Giovanni De; Benini, Luca

doi:10.1109/cbms.2006.71

Cited by 10 publications

(10 citation statements)

References 12 publications

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“…The positive portions are highlighted by a more intense coloration compared to the negative ones, that are just lightly stained or not stained at all. These cell-by-cell measures will be then categorized into three or four groups (typically 0 to 3+) in order to obtain a scoring of protein expression in the whole sample [28,33].…”

Section: For Examples)mentioning

confidence: 99%

Automated segmentation of tissue images for computerized IHC analysis

Cataldo

Ficarra

Acquaviva

et al. 2010

Computer Methods and Programs in Biomedicine

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112

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This paper presents two automated methods for the segmentation of immunohistochemical tissue images that overcome the limitations of the manual approach as well as of the existing computerized techniques. The first independent method, based on unsupervised color clustering, recognizes automatically the target cancerous areas in the specimen and disregards the stroma; the second method, based on colors separation and morphological processing, exploits automated segmentation of the nuclear membranes of the cancerous cells. Extensive experimental results on real tissue images demonstrate the accuracy of our techniques compared to manual segmentations; additional experiments show that our techniques are more effective in immunohistochemical images than popular approaches based on supervised learning or active contours. The proposed procedure can be exploited for any applications that require tissues and cells exploration and to perform reliable and standardized measures of the activity of specific proteins involved in multi-factorial genetic pathologies.

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Section: For Examples)mentioning

confidence: 99%

Automated segmentation of tissue images for computerized IHC analysis

Cataldo

Ficarra

Acquaviva

et al. 2010

Computer Methods and Programs in Biomedicine

Self Cite

112

View full text Add to dashboard Cite

show abstract

“…The most important indicator of performance for our application is indeed the percentage of correctly detected nuclei: this index is in fact related to the possibility of using the segmentation performed by the algorithm as first step for automated measures of protein activity [7]. As shown by Table 2, we obtained a satis-(a) (b) Figure 4.…”

Section: Resultsmentioning

confidence: 97%

“…This second goal is critical to perform accurate and robust cell segmentation and protein activity quantification [7]. Other steps in tissue image segmentation are not in the scope of this paper; to have more details about further tissue analysis, cell segmentation and protein activity quantification see [7].…”

Section: Introductionmentioning

confidence: 98%

Selection of Tumor Areas and Segmentation of Nuclear Membranes in Tissue Confocal Images: A Fully Automated Approach

Song

Yoo

2007

2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007)

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An accurate and standardized technique for tumor tissue segmentation is a critical step for monitoring and quantifying the activity of specific families of proteins involved in multi-factorial genetic pathologies. However, fully automated tissue and cell segmentation in clinical images presents many challenges related to the characteristics of the images that make traditional approaches substantially ineffective or incomplete. In this paper we present a fully-automated algorithm that is able to perform accurate and fast segmentation of tissue images. Experimental results on several real-life datasets demonstrate the high level of accuracy achievable thanks to our approach.

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“…In [7], an alteration of the traditional Hough transform was utilized as a seed-finding method for each cell along with a partial differential equation solver to approximate the contour of the membrane. In [5], a novel technique is introduced for membrane segmentation based on hue value thresholding and virtual cell membrane fitting through morphology and weighted barycentre membrane points initialization procedure. Matkowskyj et al [6] exploited the utilities of commercial software and the difference of signal energy between the images derived from control tissue slide (identically treated slide) and the experimental tissue slide (primary antibodyexposed slide) in order to quantitatively evaluate IHC staining.…”

Section: Introductionmentioning

confidence: 99%

Automated analysis of immunohistochemical images based on curve evolution approaches

Livanos

Zervakis

Giakos

2013

2013 IEEE International Conference on Imaging Systems and Techniques (IST)

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The HER2/neu oncogene is notable both for its role in the pathogenesis of breast cancer and its role as a target of treatment. Qualitative or quantitative protein evaluation has been achieved using immunohistochemistry (IHC) on frozen and archival tissues, a widely adopted technique due to the standardization of the internal procedural steps and its easy and low-cost applicability to any laboratory. The goal of the present study is to introduce an efficient tool for the automated detection of HER2 protein overexpression in tissues, providing accurate, instant, yet objective interpretation outcomes through a formalized procedure. The comparison of results with classifications by specialists who evaluated the same tissue samples dataset confirms the efficiency and prospect of the methodology.

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Computer-Aided Evaluation of Protein Expression in Pathological Tissue Images

Abstract: Abstract

Cited by 10 publications

References 12 publications

Automated segmentation of tissue images for computerized IHC analysis

Automated segmentation of tissue images for computerized IHC analysis

Selection of Tumor Areas and Segmentation of Nuclear Membranes in Tissue Confocal Images: A Fully Automated Approach

Automated analysis of immunohistochemical images based on curve evolution approaches

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