Histopathology

Summary

Histopathology (compound of three Greek words: ἱστός histos 'tissue', πάθος pathos 'suffering', and -λογία -logia 'study of') refers to the microscopic examination of tissue in order to study the manifestations of disease. Specifically, in clinical medicine, histopathology refers to the examination of a biopsy or surgical specimen by a pathologist, after the specimen has been processed and histological sections have been placed onto glass slides. In contrast, cytopathology examines free cells or tissue micro-fragments (as "cell blocks"). Collection of tissues Histopathological examination of tissues starts with surgery, biopsy, or autopsy. The tissue is removed from the body or plant, and then, often following expert dissection in the fresh state, placed in a fixative which stabilizes the tissues to prevent decay. The most common fixative is 10% neutral buffered formalin (corresponding to 3.7% w/v formaldehyde in neutral buffered water, such as phosphate buffered saline).

Official source

https://en.wikipedia.org/wiki/Histopathology

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HISTOBREAST, a collection of brightfield microscopy images of Haematoxylin and Eosin stained breast tissue

Tiberiu Totu

Modern histopathology workflows rely on the digitization of histology slides. The quality of the resulting digital representations, in the form of histology slide image mosaics, depends on various specific acquisition conditions and on the image processing steps that underlie the generation of the final mosaic, e.g. registration and blending of the contained image tiles. We introduce HISTOBREAST, an extensive collection of brightfield microscopy images that we collected in a principled manner under different acquisition conditions on Haematoxylin - Eosin (H&E) stained breast tissue. HISTOBREAST is comprised of neighbour image tiles and ensemble of mosaics composed from different combinations of the available image tiles, exhibiting progressively degraded quality levels. HISTOBREAST can be used to benchmark image processing and computer vision techniques with respect to their robustness to image modifications specific to brightfield microscopy of H&E stained tissues. Furthermore, HISTOBREAST can serve in the development of new image processing methods, with the purpose of ensuring robustness to typical image artefacts that raise interpretation problems for expert histopathologists and affect the results of computerized image analysis. H&E-stained fixed tissue slide specimen center dot breast carcinoma center dot histology images Technology Type(s) brightfield microscopy center dot histological assay Factor Type(s)Exposure center dot Gain center dot Gamma center dot magnification center dot image quality center dot image overlap center dot image mosaics Sample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data

2020

Colorectal carcinoma (CRC) as a major health problem in industrialized countries is highly preventable and can be successfully treated in the early stages. However, incidence and mortality of CRC has increased over the last two decades. The reason could be that the current recommended options for screening are costly, unpleasant for patients, have low sensitivity and poor accessibility for screening. These reasons provide a strong rationale for the development of a new method. Opto-magnetic imaging spectroscopy (OMIS) as a new imaging method for the characterisation of various materials, including human tissues, is based on light-matter interaction, using a Poincare sphere for light properties and a Bloch sphere for electron properties, and allows the detection of biophysical characteristics within human tissue samples. Compared with histopathology examination, the OMIS method achieved an accuracy of 92.59% using Multilayer Perceptron Neural Network as a classifier, and 89.87% using Naive-Bayes, respectively. The obtained results, based on the investigation of 316 samples, both tumour and normal mucosa (162 cancer cases), strongly suggest that the new non-invasive OMIS method might be used for tissue characterization ex vivo to discriminate between the healthy and carcinoma state of the colon. However, it opens up the possibility of using the same method in in vivo studies to assist physicians in targeting biopsies of colorectal tissue.

2019

Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma

Adrien Raphaël Depeursinge

Medulloblastoma (MB) is a type of brain cancer that represent roughly 25% of all brain tumors in children. In the anaplastic medulloblastoma subtype, it is important to identify the degree of irregularity and lack of organizations of cells as this correlates to disease aggressiveness and is of clinical value when evaluating patient prognosis. This paper presents an image representation to distinguish these subtypes in histopathology slides. The approach combines learned features from (i) an unsupervised feature learning method using topographic independent component analysis that captures scale, color and translation invariances, and (ii) learned linear combinations of Riesz wavelets calculated at several orders and scales capturing the granularity of multiscale rotation-covariant information. The contribution of this work is to show that the combination of two complementary approaches for feature learning (unsupervised and supervised) improves the classification performance. Our approach outperforms the best methods in literature with statistical significance, achieving 99% accuracy over region-based data comprising 7,500 square regions from 10 patient studies diagnosed with medulloblastoma (5 anaplastic and 5 non-anaplastic).

Springer Int Publishing Ag2015