Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference

Perez-Bueno, Fernando; Vega, Miguel; Sales, María Á.; Aneiros‐Fernández, José; Naranjo, Valery; Molina, Rafael; Katsaggelos, Aggelos K.

doi:10.1016/j.cmpb.2021.106453

Cited by 23 publications

(16 citation statements)

References 27 publications

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“…The only work from the first branch that is more focused on preprocessing is that of Salvi et al [18] and includes an overview of the different tasks required. In many cases, WSI preprocessing is reduced to color normalization [15], [19] and other relevant tasks such as artifacts detection or different approaches to dealing with color variation [14], [28] are ignored.…”

Section: A Related Workmentioning

confidence: 99%

“…The Bayesian approach was also utilized by Pérez-Bueno et al [85] with the use of a Total Variation (TV) prior. The work in [28] uses the high-pass filtered domain to set sparse general super Gaussian priors on the concentrations. Then BCD problem is approached as a dictionary learning problem in [104], implementing Bayesian K-SVD for BCD of histological images.…”

Section: A Stain Separation Using Bcdmentioning

confidence: 99%

“…When the metric is calculated on the reconstructed separation (e.g. H-only and E-only images), the use of the Quaternion Structural Similarity (QSSIM) [28] is recommended to account for color similarities. Other approaches use the Euclidean distance between the GT and the BCD concentrations or the Normalized Mean Squared Error (NMSE).…”

Section: ) Quantitative Metricsmentioning

confidence: 99%

“…This comparison does not consider different distributions of the stains in the images, so it is often performed on previously identified regions such as nuclei, cytoplasm or red blood cells [89]. The most popular metric is the Normalized Median Intensity (NMI) [28], [44], [99], where the median intensity value is divided by the 95 th percentile. The NMI value is calculated for the entire dataset in which the color variance is measured, and then the standard deviation (SD) and the coefficient of variation (CV) (standard deviation divided by mean) are used as metrics.…”

Section: ) Quantitative Metricsmentioning

confidence: 99%

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The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

et al. 2022

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Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis of different types of cancer. The preparation and digitization of histological tissues leads to the introduction of artifacts and variations that need to be addressed before the tissues are analyzed. WSI preprocessing can significantly improve the performance of computational pathology systems and is often used to facilitate human or machine analysis. Color processing techniques are usually the main concern, while other areas are frequently ignored. In this paper, we present a detailed study of the state-of-the-art in three different areas of WSI preprocessing: Artifacts detection, color variation, and the emerging field of pathology-specific data augmentation. We include a summary of evaluation techniques along with a discussion of possible limitations and future research directions for new methods.

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Section: A Related Workmentioning

confidence: 99%

Section: A Stain Separation Using Bcdmentioning

confidence: 99%

Section: ) Quantitative Metricsmentioning

confidence: 99%

Section: ) Quantitative Metricsmentioning

confidence: 99%

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The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

et al. 2022

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“…To extract the features of patches, patch-level classifiers have been trained based on transfer learning, with little attention given to the characteristics of patches [ 3 , 4 , 5 , 14 , 15 , 16 , 17 ]. Additionally, to improve the performance of a CNN model as a patch-level classifier, prior studies employed image modification techniques such as data augmentation [ 18 , 19 ], color transformation [ 20 , 21 ], and stain normalization [ 22 , 23 , 24 ]. The goals of image modification techniques are to amplify the number of patch images, extract the morphological features, and reduce the deviations across WSI scan devices.…”

Section: Introductionmentioning

confidence: 99%

MixPatch: A New Method for Training Histopathology Image Classifiers

et al. 2022

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CNN-based image processing has been actively applied to histopathological analysis to detect and classify cancerous tumors automatically. However, CNN-based classifiers generally predict a label with overconfidence, which becomes a serious problem in the medical domain. The objective of this study is to propose a new training method, called MixPatch, designed to improve a CNN-based classifier by specifically addressing the prediction uncertainty problem and examine its effectiveness in improving diagnosis performance in the context of histopathological image analysis. MixPatch generates and uses a new sub-training dataset, which consists of mixed-patches and their predefined ground-truth labels, for every single mini-batch. Mixed-patches are generated using a small size of clean patches confirmed by pathologists while their ground-truth labels are defined using a proportion-based soft labeling method. Our results obtained using a large histopathological image dataset shows that the proposed method performs better and alleviates overconfidence more effectively than any other method examined in the study. More specifically, our model showed 97.06% accuracy, an increase of 1.6% to 12.18%, while achieving 0.76% of expected calibration error, a decrease of 0.6% to 6.3%, over the other models. By specifically considering the mixed-region variation characteristics of histopathology images, MixPatch augments the extant mixed image methods for medical image analysis in which prediction uncertainty is a crucial issue. The proposed method provides a new way to systematically alleviate the overconfidence problem of CNN-based classifiers and improve their prediction accuracy, contributing toward more calibrated and reliable histopathology image analysis.

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A Robust BKSVD Method for Blind Color Deconvolution and Blood Detection on H &E Histological Images

Perez-Bueno

Engan

Molina

2023

Lecture Notes in Computer Science

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Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference

Cited by 23 publications

References 27 publications

The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

The Devil is in the Details: Whole Slide Image Acquisition and Processing for Artifacts Detection, Color Variation, and Data Augmentation: A Review

MixPatch: A New Method for Training Histopathology Image Classifiers

A Robust BKSVD Method for Blind Color Deconvolution and Blood Detection on H &E Histological Images

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