Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis

Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H\&E) is a standard stain routinely employed worldwide. It is easy to acquire and cost effective, but cells and tissue components show low-contrast with varying tones of dark blue and pink, which makes difficult visual assessments, digital image analysis, and quantifications. These limitations can be overcome by IHC staining of target proteins of the tissue slide. IHC provides a selective, high-contrast imaging of cells and tissue components, but their use is largely limited by a significantly more complex laboratory processing and high cost. We proposed a conditional CycleGAN (cCGAN) network to transform the H\&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide. This data-driven method requires only a limited amount of labelled data but will generate pixel level segmentation results. The proposed cCGAN model improves the original network \cite{zhu_unpaired_2017} by adding category conditions and introducing two structural loss functions, which realize a multi-subdomain translation and improve the translation accuracy as well. % need to give reasons here. Experiments demonstrate that the proposed model outperforms the original method in unpaired image translation with multi-subdomains. We also explore the potential of unpaired images to image translation method applied on other histology images related tasks with different staining techniques

Modular video endoscopy for in vivo cross-polarized and vital-dye fluorescence imaging of Barrett's-associated neoplasia

A modular video endoscope is developed and tested to allow imaging in different modalities. This system incorporates white light imaging (WLI), cross-polarized imaging (CPI), and vital-dye fluorescence imaging (VFI), using interchangeable filter modules. CPI and VFI are novel endoscopic modalities that probe mucosal features associated with Barrett's neoplasia. CPI enhances vasculature, while VFI enhances glandular architecture. In this pilot study, we demonstrate the integration of these modalities by imaging areas of Barrett's metaplasia and neoplasia in an esophagectomy specimen. We verify that those key image features are also observed during an in vivo surveillance procedure. CPI images demonstrate improved visualization of branching blood vessels associated with neoplasia. VFI images show glandular architecture with increased glandular effacement associated with neoplasia. Results suggests that important pathologic features seen in CPI and VFI are not visible during standard endoscopic white light imaging, and thus the modalities may be useful in future in vivo studies for discriminating neoplasia from Barrett's metaplasia. We further demonstrate that the integrated WLI/CPI/VFI endoscope is compatible with complementary high-resolution endomicroscopy techniques such as the high-resolution microendoscope, potentially enabling two-step (“red-flag” widefield plus confirmatory high-resolution imaging) protocols to be enhanced

A Guide to Perform 3D Histology of Biological Tissues with Fluorescence Microscopy

The analysis of histological alterations in all types of tissue is of primary importance in pathology for highly accurate and robust diagnosis. Recent advances in tissue clearing and fluorescence microscopy made the study of the anatomy of biological tissue possible in three dimensions. The combination of these techniques with classical hematoxylin and eosin (H&E) staining has led to the birth of three-dimensional (3D) histology. Here, we present an overview of the state-of-the-art methods, highlighting the optimal combinations of different clearing methods and advanced fluorescence microscopy techniques for the investigation of all types of biological tissues. We employed fluorescence nuclear and eosin Y staining that enabled us to obtain hematoxylin and eosin pseudo-coloring comparable with the gold standard H&E analysis. The computational reconstructions obtained with 3D optical imaging can be analyzed by a pathologist without any specific training in volumetric microscopy, paving the way for new biomedical applications in clinical pathology

Towards multimodal nonlinear microscopy in clinics

Multimodal nonlinear microscopy combining two photon excited fluorescence (TPEF), second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) represents a promising and powerful tool for biomedical diagnostics. The method enables label-free visualization of morphology and chemical composition of complex tissues as well as disease related changes and is as such as detailed as staining histologic methods. In this work a compact microscope utilizing novel fiber laser sources and a new approach for data analysis based on colocalization have been developed and tested for detecting various disease patterns, e.g., atherosclerosis and brain tumors.Mit Hilfe der nichtlinearen Multikontrast-Mikroskopie basierend auf den Prozessen Zweiphotonenfluoreszenz (TPEF), Frequenzverdopplung (SHG) und kohärente anti-Stokes Raman-Streuung (CARS), können Morphologie, chemische Zusammensetzung sowie krankheitsbedingte Veränderungen komplexer Gewebe label-frei analog zu histologischen Färbungen dargestellt werden. Potentiell eignet sich die Methode daher für die in vivo Bildgebung und könnte die medizinische Diagnostik entscheidend verbessern. Im Rahmen dieser Arbeit wurde ein kompaktes TPEF/SHG/CARS-Forschungsmikroskop unter Verwendung neuer Faserlaserquellen speziell für die Verwendung in der Klinik entwickelt. Dabei wurde erforscht, wie sich der Bildkontrast durch nahinfrarote Laser sowie eine hohe spektrale Auflösung verbessern lässt. Zusätzlich wurde an Methoden der Datenanalyse multispektraler CARS-Daten gearbeitet, um mittels der Kolokalisationsanalyse die Verteilung verschiedener molekularer Marker in komplexen Geweben zu visualisieren. Das Potential für klinische Anwendungen wurde an verschiedenen Krankheitsbildern wie Arteriosklerose und Tumoren des Hirns demonstriert

Multimodal Multispectral Optical Endoscopic Imaging for Biomedical Applications

Optical imaging is an emerging field of clinical diagnostics that can address the growing medical need for early cancer detection and diagnosis. Various human cancers are amenable to better prognosis and patient survival if found and treated during early disease onset. Besides providing wide-field, macroscopic diagnostic information similar to existing clinical imaging techniques, optical imaging modalities have the added advantage of microscopic, high resolution cellular-level imaging from in vivo tissues in real time. This comprehensive imaging approach to cancer detection and the possibility of performing an ‘optical biopsy’ without tissue removal has led to growing interest in the field with numerous techniques under investigation. Three optical techniques are discussed in this thesis, namely multispectral fluorescence imaging (MFI), hyperspectral reflectance imaging (HRI) and fluorescence confocal endomicroscopy (FCE). MFI and HRI are novel endoscopic imaging-based extensions of single point detection techniques, such as laser induced fluorescence spectroscopy and diffuse reflectance spectroscopy. This results in the acquisition of spectral data in an intuitive imaging format that allows for quantitative evaluation of tissue disease states. We demonstrate MFI and HRI on fluorophores, tissue phantoms and ex vivo tissues and present the results as an RGB colour image for more intuitive assessment. This follows dimensionality reduction of the acquired spectral data with a fixed-reference isomap diagnostic algorithm to extract only the most meaningful data parameters. FCE is a probe-based point imaging technique offering confocal detection in vivo with almost histology-grade images. We perform FCE imaging on chemotherapy-treated in vitro human ovarian cancer cells, ex vivo human cancer tissues and photosensitiser-treated in vivo murine tumours to show the enhanced detection capabilities of the technique. Finally, the three modalities are applied in combination to demonstrate an optical viewfinder approach as a possible minimally-invasive imaging method for early cancer detection and diagnosis

Virtual histological staining of unlabeled autopsy tissue

Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.Comment: 24 Pages, 7 Figure

MR imaging features of high-grade gliomas in murine models: How they compare with human disease, reflect tumor biology, and play a role in preclinical trials

Murine models are the most commonly used and best investigated among the animal models of HGG. They constitute an important weapon in the development and testing of new anticancer drugs and have long been used in preclinical trials. Neuroimaging methods, particularly MR imaging, offer important advantages for the evaluation of treatment response: shorter and more reliable treatment end points and insight on tumor biology and physiology through the use of functional imaging DWI, PWI, BOLD, and MR spectroscopy. This functional information has been progressively consolidated as a surrogate marker of tumor biology and genetics and may play a pivotal role in the assessment of specifically targeted drugs, both in clinical and preclinical trials. The purpose of this Research Perspectives was to compile, summarize, and critically assess the available information on the neuroimaging features of different murine models of HGGs, and explain how these correlate with human disease and reflect tumor biology.This work was supported by the Programme for Advanced Medical Education from Fundaçâo Champalimaud, Fundaçâo Calouste Gulbenkian, Ministério da Saúde and Fundaçâo para a Ciência e Tecnologia, Portugal, to the first author (A.R.B.), and by grants from the Spanish Ministry of Science and Innovation SAF 2008–01327 and the Community of Madrid S-BIO-2006–0170, to the last author (S.G.C.).Peer Reviewe