Modified Reinhard Algorithm for Color Normalization of Colorectal Cancer Histopathology Images

Roy, Santanu; Panda, Shubhajit; Jangid, Mahesh

doi:10.23919/eusipco54536.2021.9616117

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…Studies argue a normalization step is required to match performance between scanners or different scanning protocols (31). In future works, we aim to examine how color normalization may alter results within the pre-analytic variable realm (32,33,34). Preliminary experiments show Reinhard normalization (32) as a suitable intervention to address GT450 performance differences (see supplemental figure 5).…”

Section: Discussionmentioning

confidence: 99%

Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides

Oliveira¹,

Lai²,

Harvey³

et al. 2023

Journal of Neuropathology &Amp; Experimental Neurology

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Computational machine learning (ML)-based frameworks could be advantageous for scalable analyses in neuropathology. A recent deep learning (DL) framework has shown promise in automating the processes of visualizing and quantifying different types of amyloid-β deposits as well as segmenting white matter (WM) from gray matter (GM) on digitized immunohistochemically stained slides. However, this framework has only been trained and evaluated on amyloid-β-stained slides with minimal changes in preanalytic variables. In this study, we evaluated select preanalytical variables including magnification, compression rate, and storage format using three digital slides scanners (Zeiss Axioscan Z1, Leica Aperio AT2, and Leica Aperio GT 450), on over 60 whole slide images, in a cohort of 14 cases having a spectrum of amyloid-β deposits. We conducted statistical comparisons of preanalytic variables with repeated measures analysis of variance evaluating the outputs of two DL frameworks for segmentation and object classification tasks. For both WM/GM segmentation and amyloid-β plaque classification tasks, there were statistical differences with respect to scanner types (p < 0.05) and magnifications (p < 0.05). Although small numbers of cases were analyzed, this pilot study highlights the significance of preanalytic variables that may alter the performance of ML algorithms.

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Section: Discussionmentioning

confidence: 99%

Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides

Oliveira¹,

Lai²,

Harvey³

et al. 2023

Journal of Neuropathology &Amp; Experimental Neurology

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show abstract

“…During the color correction process, the color characteristics of one image are aligned with those of another. Careful consideration was given to the reference image selection, as normalization outcomes can be influenced if the chosen reference image exhibits darker colors ( Roy et al, 2021 ). The application of Reinhard color normalization to our dataset is based on established practices in the literature ( Lakshmanan et al, 2019 ; Qu et al, 2021 ), demonstrating its superior performance compared to other techniques.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images

Aalam,

Ahanger,

Masoodi

et al. 2024

Front. Mol. Biosci.

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Esophageal cancer (EC) remains a significant health challenge globally, with increasing incidence and high mortality rates. Despite advances in treatment, there remains a need for improved diagnostic methods and understanding of disease progression. This study addresses the significant challenges in the automatic classification of EC, particularly in distinguishing its primary subtypes: adenocarcinoma and squamous cell carcinoma, using histopathology images. Traditional histopathological diagnosis, while being the gold standard, is subject to subjectivity and human error and imposes a substantial burden on pathologists. This study proposes a binary class classification system for detecting EC subtypes in response to these challenges. The system leverages deep learning techniques and tissue-level labels for enhanced accuracy. We utilized 59 high-resolution histopathological images from The Cancer Genome Atlas (TCGA) Esophageal Carcinoma dataset (TCGA-ESCA). These images were preprocessed, segmented into patches, and analyzed using a pre-trained ResNet101 model for feature extraction. For classification, we employed five machine learning classifiers: Support Vector Classifier (SVC), Logistic Regression (LR), Decision Tree (DT), AdaBoost (AD), Random Forest (RF), and a Feed-Forward Neural Network (FFNN). The classifiers were evaluated based on their prediction accuracy on the test dataset, yielding results of 0.88 (SVC and LR), 0.64 (DT and AD), 0.82 (RF), and 0.94 (FFNN). Notably, the FFNN classifier achieved the highest Area Under the Curve (AUC) score of 0.92, indicating its superior performance, followed closely by SVC and LR, with a score of 0.87. This suggested approach holds promising potential as a decision-support tool for pathologists, particularly in regions with limited resources and expertise. The timely and precise detection of EC subtypes through this system can substantially enhance the likelihood of successful treatment, ultimately leading to reduced mortality rates in patients with this aggressive cancer.

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“…A similar kind of adaptive transformation is recently proposed by S. Roy et al. [63] for color normalization of histopathology images. However, we observe that some CXR images (

) can be negative.…”

Section: Methodsmentioning

confidence: 99%

SVD-CLAHE boosting and balanced loss function for Covid-19 detection from an imbalanced Chest X-Ray dataset

Roy

Tyagi

Bansal

et al. 2022

Computers in Biology and Medicine

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Modified Reinhard Algorithm for Color Normalization of Colorectal Cancer Histopathology Images

Cited by 9 publications

References 9 publications

Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides

Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides

Deep learning-based identification of esophageal cancer subtypes through analysis of high-resolution histopathology images

SVD-CLAHE boosting and balanced loss function for Covid-19 detection from an imbalanced Chest X-Ray dataset

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