A Model based Survey of Colour Deconvolution in Diagnostic Brightfield Microscopy: Error Estimation and Spectral Consideration

An image processing-based quantification of gram variability in Acinetobacter baumannii

Gram staining differentiates bacteria as gram-positive and gram-negative, depending on their cell wall constituents, and coloring cells in violet and pink, respectively. Sometimes, a subpopulation of the same bacterial species assumes different colors, ranging from pink to violet, for reasons that are not completely understood yet. We analyze conventional brightfield images and use an automated pipeline to count pink and violet cells. The ImageJ-based processing algorithm quantifies the gram variability in Acinetobacter baumannii ACICU in the stationary phase of growth with a percentage of 66% pink cells. Different bacterial strains and cell growth stages have been considered. RESEARCH HIGHLIGHTS: Gram staining differentiates bacteria into gram-positive (violet) and gram-negative (pink). Gram variability represents an inhomogeneous distribution of pink and violet cells within the same species. We developed an ImageJ-based pipeline for the quantification of Gram variability in Acinetobacter baumannii.

Differential diagnosis of thyroid nodule capsules using random forest guided selection of image features

Microscopic evaluation of tissue sections stained with hematoxylin and eosin is the current gold standard for diagnosing thyroid pathology. Digital pathology is gaining momentum providing the pathologist with additional cues to traditional routes when placing a diagnosis, therefore it is extremely important to develop new image analysis methods that can extract image features with diagnostic potential. In this work, we use histogram and texture analysis to extract features from microscopic images acquired on thin thyroid nodule capsules sections and demonstrate how they enable the differential diagnosis of thyroid nodules. Targeted thyroid nodules are benign (i.e., follicular adenoma) and malignant (i.e., papillary thyroid carcinoma and its sub-type arising within a follicular adenoma). Our results show that the considered image features can enable the quantitative characterization of the collagen capsule surrounding thyroid nodules and provide an accurate classification of the latter's type using random forest.

Meckel's Cartilage in Mandibular Development and Dysmorphogenesis

The Fgfr2c^C342Y/+ Crouzon syndrome mouse model carries a cysteine to tyrosine substitution at amino acid position 342 (Cys342Tyr; C342Y) in the fibroblast growth factor receptor 2 (Fgfr2) gene equivalent to a FGFR2 mutation commonly associated with Crouzon and Pfeiffer syndromes in humans. The Fgfr2c C342Y mutation results in constitutive activation of the receptor and is associated with upregulation of osteogenic differentiation. Fgfr2c^C342Y/+ Crouzon syndrome mice show premature closure of the coronal suture and other craniofacial anomalies including malocclusion of teeth, most likely due to abnormal craniofacial form. Malformation of the mandible can precipitate a plethora of complications including disrupting development of the upper jaw and palate, impediment of the airway, and alteration of occlusion necessary for proper mastication. The current paradigm of mandibular development assumes that Meckel’s cartilage (MC) serves as a support or model for mandibular bone formation and as a template for the later forming mandible. If valid, this implies a functional relationship between MC and the forming mandible, so mandibular dysmorphogenesis might be discerned in MC affecting the relationship between MC and mandibular bone. Here we investigate the relationship of MC to mandible development from the early mineralization of the mandible (E13.5) through the initiation of MC degradation at E17.7 using Fgfr2c^C342Y/+ Crouzon syndrome embryos and their unaffected littermates (Fgfr2c^+/+). Differences between genotypes in both MC and mandibular bone are subtle, however MC of Fgfr2c^C342Y/+ embryos is generally longer relative to unaffected littermates at E15.5 with specific aspects remaining relatively large at E17.5. In contrast, mandibular bone is smaller overall in Fgfr2c^C342Y/+ embryos relative to their unaffected littermates at E15.5 with the posterior aspect remaining relatively small at E17.5. At a cellular level, differences are identified between genotypes early (E13.5) followed by reduced proliferation in MC (E15.5) and in the forming mandible (E17.5) in Fgfr2c^C342Y/+ embryos. Activation of the ERK pathways is reduced in the perichondrium of MC in Fgfr2c^C342Y/+ embryos and increased in bone related cells at E15.5. These data reveal that the Fgfr2c C342Y mutation differentially affects cells by type, location, and developmental age indicating a complex set of changes in the cells that make up the lower jaw.

Somatostatin Receptors in Human Meningiomas-Clinicopathological Aspects

Simple Summary

Meningioma diagnostics and grading are currently based on subjective histopathological criteria given by the 2016 World Health Organization (WHO) classification. However, biomarkers may provide a more objective approach to diagnostics. This study was designed to elucidate the diagnostic and prognostic value of somatostatin receptors (SSTRs) as biomarkers in meningiomas, which could help to identify patients with a higher risk of recurrence and provide more personalized treatment. We have confirmed, in a population of 162 patients, that SSTRs have diagnostic value and may aid in the differentiation between WHO grade 1 and grade 2 tumors. Furthermore, SSTR1, SSTR2 and SSTR5 were associated with higher malignancy grades. SSTR2 expression was found to be characteristic in meningiomas. To maintain objectiveness, we scoped for a digital evaluation of immunoreactivity. We aim to impact and motivate researchers to further investigations towards more objective criteria in meningioma diagnostics, which in turn will improve patient care.

Abstract

Meningiomas have high recurrence rates despite frequently benign histopathological appearances. Somatostatin receptors (SSTRs) may be reliable biomarkers that could identify patients with increased risk of recurrence. Even though SSTRs are previously detected in meningiomas, their associations to clinicopathological features remain unclear. The aim of this study was to investigate the diagnostic and prognostic value of SSTRs in a large series of human meningiomas with long follow-up data. Immunohistochemistry was used to measure the expression of SSTR1-SSTR5 in tissue samples from 162 patients diagnosed with intracranial meningiomas of World Health Organization (WHO) grade 1 or 2. Digital scoring and a manual staining index were applied to assess immunoreactivity. All SSTRs, except SSTR4, were upregulated in our series of meningiomas. SSTR1 (p = 0.036), SSTR2 (p = 0.036) and SSTR5 (p = 0.029) were associated with a higher malignancy grade. SSTR2 presented as the most reliable marker. Only SSTR2 was associated with time to recurrence (TTR) in univariate Cox regression analyses. Manual staining index was strongly correlated with digital scoring for all SSTRs (r > 0.65, p < 0.001). SSTRs, and especially SSTR2, are useful in the diagnostics of meningiomas, even though their prognostic value appears limited. Digital scoring is valuable to ensure reproducibility.

System for quantitative evaluation of DAB&H-stained breast cancer biopsy digital images (CHISEL)

This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and diverse compactness. It stands out with the proposed seamless segmentation based on regions of interest cropping as well as the explicit step of nuclei cluster splitting followed by a boundary refinement. The system utilizes machine learning and recursive local processing to eliminate distorted (inaccurate) outlines. The method was validated using two labeled datasets which proved the relevance of the achieved results. The evaluation was based on the IISPV dataset of tissue from biopsy of breast cancer patients, with markers of T cells, along with Warwick Beta Cell Dataset of DAB&H-stained tissue from postmortem diabetes patients. Based on the comparison of the ground truth with the results of the detected and classified objects, we conclude that the proposed method can achieve better or similar results as the state-of-the-art methods. This system deals with the complex problem of nuclei quantification in digitalized images of immunohistochemically stained tissue sections, achieving best results for DAB&H-stained breast cancer tissue samples. Our method has been prepared with user-friendly graphical interface and was optimized to fully utilize the available computing power, while being accessible to users with fewer resources than needed by deep learning techniques.

Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin

Histological evaluation plays a major role in cancer diagnosis and treatment. The appearance of H&E-stained images can vary significantly as a consequence of differences in several factors, such as reagents, staining conditions, preparation procedure and image acquisition system. Such potential sources of noise can all have negative effects on computer-assisted classification. To minimize such artefacts and their potentially negative effects several color pre-processing methods have been proposed in the literature-for instance, color augmentation, color constancy, color deconvolution and color transfer. Still, little work has been done to investigate the efficacy of these methods on a quantitative basis. In this paper, we evaluated the effects of color constancy, deconvolution and transfer on automated classification of H&E-stained images representing different types of cancers-specifically breast, prostate, colorectal cancer and malignant lymphoma. Our results indicate that in most cases color pre-processing does not improve the classification accuracy, especially when coupled with color-based image descriptors. Some pre-processing methods, however, can be beneficial when used with some texture-based methods like Gabor filters and Local Binary Patterns.

A scalable, fully automated approach for regional quantification of immunohistochemical staining of astrocytes in the rat brain

BACKGROUND

Astrocytes play a critical role in CNS functions by providing physiological support to surrounding cells. These cells present a particularly unique challenge for in vitro immunohistochemical quantification due reactive gliosis after insult or injury, which is characterized by the extension of long processes.

NEW METHOD

We present an optimized QuPath protocol that is scalable, fully automated, and capable of being applied to images generated by whole slide scanning technology using this open-source software.

RESULTS

We induced mechanical injury in the rat brain and stained astrocytes using glial fibrillary acidic protein (GFAP) and 3,3-diaminobenzidine (DAB) chromogen detection. Slides were scanned using a whole slide scanner, Vectra Polaris. Using QuPath, we summarize and contrast three ways of quantifying astrocytes in uninjured (contralateral) and injured (ipsilateral) hemispheres: optical density, positive pixels and positive proportion.

COMPARISON WITH EXISTING METHODS

Robust quantification of DAB stained astrocytes remains elusive. Previous methodologies have relied on software that is not compatible with whole slide scanner images. Use of such software can compromise the data integrity within the image and is limited by issues with scalability and lack of automation. Previous methods using manual histopathological scoring are also limited by the ability to quantify large numbers of astrocytes. Given these limitations, we were unable to directly compare our method with those using other software or manual histopathology.

CONCLUSIONS

Based on an analysis of our method, we conclude that positive proportion may be the most effective way to quantify astrocytic responses using GFAP and DAB immunohistochemistry in the brain.

Image-to-image translation for automatic ink removal in whole slide images

Purpose: Deep learning models are showing promise in digital pathology to aid diagnoses. Training complex models requires a significant amount and diversity of well-annotated data, typically housed in institutional archives. These slides often contain clinically meaningful markings to indicate regions of interest. If slides are scanned with the ink present, then the downstream model may end up looking for regions with ink before making a classification. If scanned without the markings, the information regarding where the relevant regions are located is lost. A compromise solution is to scan the slide with the annotations present but digitally remove them. Approach: We proposed a straightforward framework to digitally remove ink markings from whole slide images using a conditional generative adversarial network based on Pix2Pix. Results: The peak signal-to-noise ratio increased 30%, structural similarity index increased 20%, and visual information fidelity increased 200% relative to previous methods. Conclusions: When comparing our digital removal of marked images with rescans of clean slides, our method qualitatively and quantitatively exceeds current benchmarks, opening the possibility of using archived clinical samples as resources to fuel the next generation of deep learning models for digital pathology.

Hyperspectral and multispectral imaging in digital and computational pathology: a systematic review [Invited]

Hyperspectral imaging (HSI) and multispectral imaging (MSI) technologies have the potential to transform the fields of digital and computational pathology. Traditional digitized histopathological slides are imaged with RGB imaging. Utilizing HSI/MSI, spectral information across wavelengths within and beyond the visual range can complement spatial information for the creation of computer-aided diagnostic tools for both stained and unstained histological specimens. In this systematic review, we summarize the methods and uses of HSI/MSI for staining and color correction, immunohistochemistry, autofluorescence, and histopathological diagnostic research. Studies include hematology, breast cancer, head and neck cancer, skin cancer, and diseases of central nervous, gastrointestinal, and genitourinary systems. The use of HSI/MSI suggest an improvement in the detection of diseases and clinical practice compared with traditional RGB analysis, and brings new opportunities in histological analysis of samples, such as digital staining or alleviating the inter-laboratory variability of digitized samples. Nevertheless, the number of studies in this field is currently limited, and more research is needed to confirm the advantages of this technology compared to conventional imagery.

Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks

Image analysis tools for cancer, such as automatic nuclei segmentation, are impacted by the inherent variation contained in pathology image data. Convolutional neural networks (CNN), demonstrate success in generalizing to variable data, illustrating great potential as a solution to the problem of data variability. In some CNN-based segmentation works for digital pathology, authors apply color normalization (CN) to reduce color variability of data as a preprocessing step prior to prediction, while others do not. Both approaches achieve reasonable performance and yet, the reasoning for utilizing this step has not been justified. It is therefore important to evaluate the necessity and impact of CN for deep learning frameworks, and its effect on downstream processes. In this paper, we evaluate the effect of popular CN methods on CNN-based nuclei segmentation frameworks.

Automated digital image quantification of histological staining for the analysis of the trilineage differentiation potential of mesenchymal stem cells

BACKGROUND

Multipotent mesenchymal stem cells (MSCs) have the potential to repair and regenerate damaged tissues and are considered as attractive candidates for the development of cell-based regenerative therapies. Currently, there are more than 200 clinical trials involving the use of MSCs for a wide variety of indications. However, variations in their isolation, expansion, and particularly characterization have made the interpretation of study outcomes or the rigorous assessment of therapeutic efficacy difficult. An unbiased characterization of MSCs is of major importance and essential to guaranty that only the most suitable cells will be used. The development of standardized and reproducible assays to predict MSC potency is therefore mandatory. The currently used quantification methodologies for the determination of the trilineage potential of MSCs are usually based on absorbance measurements which are imprecise and prone to errors. We therefore aimed at developing a methodology first offering a standardized way to objectively quantify the trilineage potential of MSC preparations and second allowing to discriminate functional differences between clonally expanded cell populations.

METHOD

MSCs originating from several patients were differentiated into osteoblasts, adipocytes, and chondroblasts for 14, 17, and 21 days. Differentiated cells were then stained with the classical dyes: Alizarin Red S for osteoblasts, Oil Red O for adipocytes, and Alcian Blue 8GX for chondroblasts. Quantification of differentiation was then performed with our newly developed digital image analysis (DIA) tool followed by the classical absorbance measurement. The results from the two techniques were then compared.

RESULT

Quantification based on DIA allowed highly standardized and objective dye quantification with superior sensitivity compared to absorbance measurements. Furthermore, small differences between MSC lines in the differentiation potential were highlighted using DIA whereas no difference was detected using absorbance quantification.

CONCLUSION

Our approach represents a novel method that simplifies the laboratory procedures not only for the quantification of histological dyes and the degree of differentiation of MSCs, but also due to its color independence, it can be easily adapted for the quantification of a wide range of staining procedures in histology. The method is easily applicable since it is based on open source software and standard light microscopy.

Persistent Homology for the Quantitative Evaluation of Architectural Features in Prostate Cancer Histology

The current system for evaluating prostate cancer architecture is the Gleason grading system which divides the morphology of cancer into five distinct architectural patterns, labeled 1 to 5 in increasing levels of cancer aggressiveness, and generates a score by summing the labels of the two most dominant patterns. The Gleason score is currently the most powerful prognostic predictor of patient outcomes; however, it suffers from problems in reproducibility and consistency due to the high intra-observer and inter-observer variability amongst pathologists. In addition, the Gleason system lacks the granularity to address potentially prognostic architectural features beyond Gleason patterns. We evaluate prostate cancer for architectural subtypes using techniques from topological data analysis applied to prostate cancer glandular architecture. In this work we demonstrate the use of persistent homology to capture architectural features independently of Gleason patterns. Specifically, using persistent homology, we compute topological representations of purely graded prostate cancer histopathology images of Gleason patterns 3,4 and 5, and show that persistent homology is capable of clustering prostate cancer histology into architectural groups through a ranked persistence vector. Our results indicate the ability of persistent homology to cluster prostate cancer histopathology images into unique groups with dominant architectural patterns consistent with the continuum of Gleason patterns. In addition, of particular interest, is the sensitivity of persistent homology to identify specific sub-architectural groups within single Gleason patterns, suggesting that persistent homology could represent a robust quantification method for prostate cancer architecture with higher granularity than the existing semi-quantitative measures. The capability of these topological representations to segregate prostate cancer by architecture makes them an ideal candidate for use as inputs to future machine learning approaches with the intent of augmenting traditional approaches with topological features for improved diagnosis and prognosis.

Enabling Histopathological Annotations on Immunofluorescent Images through Virtualization of Hematoxylin and Eosin

Context:

Medical diagnosis and clinical decisions rely heavily on the histopathological evaluation of tissue samples, especially in oncology. Historically, classical histopathology has been the gold standard for tissue evaluation and assessment by pathologists. The most widely and commonly used dyes in histopathology are hematoxylin and eosin (H&E) as most malignancies diagnosis is largely based on this protocol. H&E staining has been used for more than a century to identify tissue characteristics and structures morphologies that are needed for tumor diagnosis. In many cases, as tissue is scarce in clinical studies, fluorescence imaging is necessary to allow staining of the same specimen with multiple biomarkers simultaneously. Since fluorescence imaging is a relatively new technology in the pathology landscape, histopathologists are not used to or trained in annotating or interpreting these images.

Aims, Settings and Design:

To allow pathologists to annotate these images without the need for additional training, we designed an algorithm for the conversion of fluorescence images to brightfield H&E images.

Subjects and Methods:

In this algorithm, we use fluorescent nuclei staining to reproduce the hematoxylin information and natural tissue autofluorescence to reproduce the eosin information avoiding the necessity to specifically stain the proteins or intracellular structures with an additional fluorescence stain.

Statistical Analysis Used:

Our method is based on optimizing a transform function from fluorescence to H&E images using least mean square optimization.

Results:

It results in high quality virtual H&E digital images that can easily and efficiently be analyzed by pathologists. We validated our results with pathologists by making them annotate tumor in real and virtual H&E whole slide images and we obtained promising results.

Conclusions:

Hence, we provide a solution that enables pathologists to assess tissue and annotate specific structures based on multiplexed fluorescence images.

3D imaging of optically cleared tissue using a simplified CLARITY method and on-chip microscopy

High-throughput sectioning and optical imaging of tissue samples using traditional immunohistochemical techniques can be costly and inaccessible in resource-limited areas. We demonstrate three-dimensional (3D) imaging and phenotyping in optically transparent tissue using lens-free holographic on-chip microscopy as a low-cost, simple, and high-throughput alternative to conventional approaches. The tissue sample is passively cleared using a simplified CLARITY method and stained using 3,3'-diaminobenzidine to target cells of interest, enabling bright-field optical imaging and 3D sectioning of thick samples. The lens-free computational microscope uses pixel super-resolution and multi-height phase recovery algorithms to digitally refocus throughout the cleared tissue and obtain a 3D stack of complex-valued images of the sample, containing both phase and amplitude information. We optimized the tissue-clearing and imaging system by finding the optimal illumination wavelength, tissue thickness, sample preparation parameters, and the number of heights of the lens-free image acquisition and implemented a sparsity-based denoising algorithm to maximize the imaging volume and minimize the amount of the acquired data while also preserving the contrast-to-noise ratio of the reconstructed images. As a proof of concept, we achieved 3D imaging of neurons in a 200-μm-thick cleared mouse brain tissue over a wide field of view of 20.5 mm2. The lens-free microscope also achieved more than an order-of-magnitude reduction in raw data compared to a conventional scanning optical microscope imaging the same sample volume. Being low cost, simple, high-throughput, and data-efficient, we believe that this CLARITY-enabled computational tissue imaging technique could find numerous applications in biomedical diagnosis and research in low-resource settings.

Computer-based image analysis in breast pathology

Whole slide imaging (WSI) has the potential to be utilized in telepathology, teleconsultation, quality assurance, clinical education, and digital image analysis to aid pathologists. In this paper, the potential added benefits of computer-assisted image analysis in breast pathology are reviewed and discussed. One of the major advantages of WSI systems is the possibility of doing computer-based image analysis on the digital slides. The purpose of computer-assisted analysis of breast virtual slides can be (i) segmentation of desired regions or objects such as diagnostically relevant areas, epithelial nuclei, lymphocyte cells, tubules, and mitotic figures, (ii) classification of breast slides based on breast cancer (BCa) grades, the invasive potential of tumors, or cancer subtypes, (iii) prognosis of BCa, or (iv) immunohistochemical quantification. While encouraging results have been achieved in this area, further progress is still required to make computer-based image analysis of breast virtual slides acceptable for clinical practice.