Novel segmentation algorithm for identification of cell membrane staining in HER2 images

Wdowiak, Marek; Markiewicz, Tomasz; Osowski, S.; Patera, Janusz; Kozłowski, Wojciech

doi:10.1016/j.patrec.2016.10.003

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…1b), palm-print lines for biometric applications (Fig. 1c), staining cell membranes [3], and blood vessels in computertomography [4] images (Fig. 1e and Fig.…”

Section: Introductionmentioning

confidence: 99%

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Strisciuglio

Azzopardi

Petkov

2019

IEEE Trans. on Image Process.

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Delineation of curvilinear structures in images is an important basic step of several image processing applications, such as segmentation of roads or rivers in aerial images, vessels or staining membranes in medical images, and cracks in pavements and roads, among others. Existing methods suffer from insufficient robustness to noise. In this paper, we propose a novel operator for the detection of curvilinear structures in images, which we demonstrate to be robust to various types of noise and effective in several applications. We call it RUSTICO, which stands for RobUST Inhibition-augmented Curvilinear Operator. It is inspired by the push-pull inhibition in visual cortex and takes as input the responses of two trainable B-COSFIRE filters of opposite polarity. The output of RUSTICO consists of a magnitude map and an orientation map. We carried out experiments on a data set of synthetic stimuli with noise drawn from different distributions, as well as on several benchmark data sets of retinal fundus images, crack pavements, and aerial images and a new data set of rose bushes used for automatic gardening. We evaluated the performance of RUSTICO by a metric that considers the structural properties of line networks (connectivity, area, and length) and demonstrated that RUSTICO outperforms many existing methods with high statistical significance. RUSTICO exhibits high robustness to noise and texture.

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“…1b), palm-print lines for biometric applications (Fig. 1c), staining cell membranes [3], and blood vessels in computertomography [4] images (Fig. 1e and Fig.…”

Section: Introductionmentioning

confidence: 99%

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Strisciuglio

Azzopardi

Petkov

2019

IEEE Trans. on Image Process.

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“…Tuominen et al [25] proposed a color deconvolution-based method for HER2 status prediction and developed a free software application called ImmunoMembrane based on this method. Wdowiak et al [26] presented another approach to discover small membrane sections defined by linear patterns of different shapes. This approach assumes that the complex shape of the membrane staining results from the small membrane sections.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques

Niyas

Bygari

Naik

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Objective: Molecular subtyping is an important procedure for prognosis and targeted therapy of breast carcinoma, the most common type of malignancy affecting women. Immunohistochemistry (IHC) analysis is the widely accepted method for molecular subtyping. It involves the assessment of the four molecular biomarkers namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67 using appropriate antibody reagents. Conventionally, these biomarkers are assessed manually by a pathologist, who finally combines individual results to identify the molecular subtype. Molecular subtyping necessitates the status of all the four biomarkers together, and to the best of our knowledge, no such automated method exists. This paper proposes a novel deep learning framework for automatic molecular subtyping of breast cancer from IHC images. Methods and procedures: A modified LadderNet architecture is proposed to segment the immunopositive elements from ER, PR, HER2, and Ki67 biomarker slides. This architecture uses long skip connections to pass encoder feature space from different semantic levels to the decoder layers, allowing concurrent learning with multi-scale features. The entire architecture is an ensemble of multiple fully convolutional neural networks, and learning pathways are chosen adaptively based on input data. The segmentation stage is followed by a post-processing stage to quantify the extent of immunopositive elements to predict the final status for each biomarker. Results: The performance of segmentation models for each IHC biomarker is evaluated qualitatively and quantitatively. Furthermore, the biomarker prediction results are also evaluated. The results obtained by our method are highly in concordance with manual assessment by pathologists. Clinical impact: Accurate automated molecular subtyping can speed up this pathology procedure, reduce pathologists’ workload and associated costs, and facilitate targeted treatment to obtain better outcomes.

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“…Wdoviak et al [9,10] use morphological tools for the membrane analysis, specifically, they use hourglass shape structuring elements for membrane shape detection. Subsequently, their proposal describes the use of diverse linear shape patterns that are combined to represent the HER2 overexpressed membrane shape.…”

Section: Related Workmentioning

confidence: 99%

A novel ensemble feature selection method for pixel-level segmentation of HER2 overexpression

Aguilera

Pezoa

Rodríguez-Delherbe

2022

Complex Intell. Syst.

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Classifying histopathology images on a pixel-level requires sets of features able to capture the complex characteristics of the images, like the irregular cell morphology and the color heterogeneity on the tissue aspect. In this context, feature selection becomes a crucial step in the classification process such that it reduces model complexity and computational costs, avoids overfitting, and thereby it improves the model performance. In this study, we propose a new ensemble feature selection method by combining a set of base selectors, classifiers, and rank aggregation methods, aiming to determine from any initial set of handcrafted features, a smaller set of relevant color and texture pixel-level features, subsequently used for segmenting HER2 overexpression on a pixel-level, in breast cancer tissue images. We have been able to significantly reduce the set of initial features, using the proposed ensemble feature selection method. The best results are obtained using $$\chi ^2$$ χ 2 , Random Forest, and Runoff as the based selector, classifier, and aggregation method, respectively. The classification performance of the best model trained on the selected features set results in 0.939 recall, 0.866 specificity, 0.903 accuracy, 0.875 precision, and 0.906 F1-score.

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Novel segmentation algorithm for identification of cell membrane staining in HER2 images

Cited by 7 publications

References 10 publications

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures

Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques

A novel ensemble feature selection method for pixel-level segmentation of HER2 overexpression

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