Automatic tissue segmentation of breast biopsies imaged by QPI

Majeed, Hassaan; Nguyen, Tuan Hung; Kandel, Mikhail E.; Marcias, Virgilia; Minh, N.; Tangella, Krishnarao; Balla, Andre; Popescu, Gabriel

doi:10.1117/12.2209142

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Majeed et al [17] proposed a texton-based approach to segment breast tissue into different regions: epithelium, stroma, and lumen. The proposed method used a Leung-Malik (LM) filter bank to compute the gradient at different orientations at different spatial scales.…”

Section: Introductionmentioning

confidence: 99%

Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

Tan¹,

Mustafa²,

Mashor³

et al. 2021

Preprint

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Precise segmentation of tumor regions plays prominent role in the grading of breast carcinoma using the Nottingham Histological Grading (NHG) system. A robust segmentation framework is expected to produce cost-effective, repeatable, and reproducible quantitative outputs. In this study, a spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images is presented. The proposed framework consists of five main stages: (1) color normalization, (2) segmentation and removal of nucleus cells, (3) SNIC, (4) FCM with knowledge-based initial centroids selection, and (5) post-processing. The novelty of the proposed framework lies within its simple but powerful in clustering tumor regions precisely in a heterogenous environment. The SNIC is implemented to remove and replace the intensity of the nucleus cells based on the spatial constraints. Also, a knowledge-based initial centroids selection method is implemented to ease the FCM clustering algorithm. Both of these methods are posited to facilitate the clustering stage producing complementary results. To validate the hypothesis, careful justifications are performed to evaluate the role of SNIC and knowledge-based initial centroids selection. These methods are found plausible by achieving positive results in \(Acc\), \(F1\), \(AOM\), and \(CEI\) of 91.2%, 92.1%, 85.7%, and 90.1%, respectively. To further demonstrate the applicability of the proposed framework, four recent works are included for benchmarking purposes. The proposed framework found outperformed these methods with the lowest percentages in over-segmentation and under-segmentation: 8.7% and 6.6%, respectively.

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Section: Introductionmentioning

confidence: 99%

Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

Tan¹,

Mustafa²,

Mashor³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In the latter case, QPI computes the complex amplitude of light transmitted through weakly scattering biological specimens to obtain their optical thickness. Such measurements have been used to study live cell dynamics [4], cell volume and dry mass [5], and tissue architecture [6,7] among other things [8]. A distinct advantage of QPI is its ability to measure alterations in optical thickness with nanoscale sensitivity, thereby providing a mechanism to visualize and contrast small structural changes in phase objects.…”

Section: Introductionmentioning

confidence: 99%

Fourier phase based depth-resolved nanoscale nuclear architecture mapping for cancer detection

Uttam

Liu

2018

Methods

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Quantitative phase imaging (QPI) modality has been widely adopted in a variety of applications ranging from identifying photomask defects in lithography to characterizing cell structure and tissue morphology in cancer. Traditional QPI utilizes the electromagnetic phase of transmitted light to measure, with nanometer scale sensitivity, alterations in the optical thickness of a sample of interest. In our work, the QPI paradigm is generalized to study depth-resolved properties of phase objects with slowly varying refractive index without a strong interface by utilizing the Fourier phase associated with Fourier-domain optical coherence tomography (FD-OCT). Specifically, based on computing the Fourier phase of light back-scattered by cell nuclei, we have developed nanoscale nuclear architecture mapping (nanoNAM) method that quantifies, with nanoscale sensitivity, (a) the depth-resolved alterations in mean nuclear optical density, and (b) depth-resolved localized heterogeneity in optical density of the cell nuclei. We have used nanoNAM to detect malignant transformation in colon carcinogenesis, even in tissue that appears histologically normal according to pathologists, thereby showing its potential as a pathology aid in cases where pathology examination remains inconclusive, and for screening patient populations at risk of developing cancer. In this paper, we integrate all aspects of nanoNAM, from principle through instrumentation and analysis, to show that nanoNAM is a promising, low-cost, and label-free method for identifying pathologically indeterminate pre-cancerous and cancerous cells. Importantly, it can seamlessly integrate into the clinical pipeline by utilizing clinically prepared formalin-fixed, paraffin-embedded tissue sections.

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Spatial neighborhood intensity constraint (SNIC) and knowledge-based clustering framework for tumor region segmentation in breast histopathology images

Tan

Mustafa

Mashor

et al. 2022

Multimed Tools Appl

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Automatic tissue segmentation of breast biopsies imaged by QPI

Cited by 3 publications

References 14 publications

Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

Spatial neighborhood intensity constraint (SNIC) clustering framework for tumor region in breast histopathology images

Fourier phase based depth-resolved nanoscale nuclear architecture mapping for cancer detection

Spatial neighborhood intensity constraint (SNIC) and knowledge-based clustering framework for tumor region segmentation in breast histopathology images

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