Computer-based image analysis in breast pathology

Gandomkar, Ziba; Brennan, Patrick C.; Mello‐Thoms, Claudia

doi:10.4103/2153-3539.192814

Cited by 34 publications

(16 citation statements)

References 61 publications

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“…In the following, we give a brief comparison of these methods ( Table 3). An extensive review and comparison of methods (not restricted to immunohistochemical quantification) used in the classification of breast cancer pathology slides is given in [15]. Few research works using biomarker-specific features in the classification of IHC slides have been reported so far.…”

Section: Feature Dimensionmentioning

confidence: 99%

Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides

Mukundan

2019

J. Imaging

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The evaluation of breast cancer grades in immunohistochemistry (IHC) slides takes into account various types of visual markers and morphological features of stained membrane regions. Digital pathology algorithms using whole slide images (WSIs) of histology slides have recently been finding several applications in such computer-assisted evaluations. Features that are directly related to biomarkers used by pathologists are generally preferred over the pixel values of entire images, even though the latter has more information content. This paper explores in detail various types of feature measurements that are suitable for the automated scoring of human epidermal growth factor receptor 2 (HER2) in histology slides. These are intensity features known as characteristic curves, texture features in the form of uniform local binary patterns (ULBPs), morphological features specifying connectivity of regions, and first-order statistical features of the overall intensity distribution. This paper considers important properties of the above features and outlines methods for reducing information redundancy, maximizing inter-class separability, and improving classification accuracy in the combined feature set. This paper also presents a detailed experimental analysis performed using the aforementioned features on a WSI dataset of IHC stained slides.

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Section: Feature Dimensionmentioning

confidence: 99%

Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides

Mukundan

2019

J. Imaging

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“…Once the nuclei figures are segmented, features defining their morphology, texture, and spatial relationship with one another are calculated to represent different nuclei classes in a multi‐dimensional feature space . Morphological features characterize size and shape of nuclei figures such as area, perimeter, major axis, minor axis, shape factor, etc.…”

Section: Introductionmentioning

confidence: 99%

Automatic cellularity assessment from post‐treated breast surgical specimens

et al. 2017

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Neoadjuvant treatment (NAT) of breast cancer (BCa) is an option for patients with the locally advanced disease. It has been compared with standard adjuvant therapy with the aim of improving prognosis and surgical outcome. Moreover, the response of the tumor to the therapy provides useful information for patient management. The pathological examination of the tissue sections after surgery is the gold-standard to estimate the residual tumor and the assessment of cellularity is an important component of tumor burden assessment. In the current clinical practice, tumor cellularity is manually estimated by pathologists on hematoxylin and eosin (H&E) stained slides, the quality, and reliability of which might be impaired by inter-observer variability which potentially affects prognostic power assessment in NAT trials. This procedure is also qualitative and time-consuming. In this paper, we describe a method of automatically assessing cellularity. A pipeline to automatically segment nuclei figures and estimate residual cancer cellularity from within patches and whole slide images (WSIs) of BCa was developed. We have compared the performance of our proposed pipeline in estimating residual cancer cellularity with that of two expert pathologists. We found an intra-class agreement coefficient (ICC) of 0.89 (95% CI of [0.70, 0.95]) between pathologists, 0.74 (95% CI of [0.70, 0.77]) between pathologist #1 and proposed method, and 0.75 (95% CI of [0.71, 0.79]) between pathologist #2 and proposed method. We have also successfully applied our proposed technique on a WSI to locate areas with high concentration of residual cancer. The main advantage of our approach is that it is fully automatic and can be used to find areas with high cellularity in WSIs. This provides a first step in developing an automatic technique for post-NAT tumor response assessment from pathology slides.

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“…Indeed, the main part of the deep learning applications in bio and medical imaging concerns the computer-aided images interpretations and analyses [66][67][68][69][70]. For example, analyzing histopathology images for breast cancer diagnosis [63,[71][72][73], or a digital pathology and image analysis with a focus on research and biomarker discovery [74]). The major part of the deep learning papers published in bio and medical imaging concerns the segmentation, localization and classification of the nuclei [75] and mitosis [76] for bioimaging, and lesion and anatomical object (such as organ, landmarks and other substructures) for medical imaging.…”

Section: Bio and Medical Imagingmentioning

confidence: 99%

Deep Learning in the Biomedical Applications: Recent and Future Status

2019

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Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain. In this domain, the different areas of interest concern the Omics (study of the genome—genomics—and proteins—transcriptomics, proteomics, and metabolomics), bioimaging (study of biological cell and tissue), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM). This paper reviews the major deep learning concepts pertinent to such biomedical applications. Concise overviews are provided for the Omics and the BBMI. We end our analysis with a critical discussion, interpretation and relevant open challenges.

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Computer-based image analysis in breast pathology

Cited by 34 publications

References 61 publications

Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides

Analysis of Image Feature Characteristics for Automated Scoring of HER2 in Histology Slides

Automatic cellularity assessment from post‐treated breast surgical specimens

Deep Learning in the Biomedical Applications: Recent and Future Status

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