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

Methods and instrumentation for raman characterization of bladder cancer tumor

High incidence and recurrence rates make bladder cancer the most common malignant tumor in the urinary system. Cystoscopy is the gold standard test used for diagnosis, nevertheless small flat tumors might be missed, and the procedure still represents discomfort to patients and high recurrence can result from of urethral injuries. During cystoscopy, suspicious tumors are detected through white light endoscopy and resected tissue is further examined by histopathology. after resection, the pathologist provides information on the differentiation of the cells and the penetration depth of the tumor in the tissue, known as grading and staging of tumor, respectively. During cystoscopy, information on tumor grading and morphological depth characterization can assist onsite diagnosis and significantly reduce the amount of unnecessarily resected tissue. Recently, new developments in optical imaging and spectroscopic approaches have been demonstrated to improve the results of standard techniques by providing real-time detection of macroscopic and microscopic biomedical information. Different applications to detect anomalies in tissues and cells based on the chemical composition and structure at the microscopic level have been successfully tested. There is, nevertheless, the need to cope with the demands for clinical translation. This doctoral thesis presents the investigations, clinical studies and approaches applied to filling the main open research questions when applying Raman spectroscopy as a diagnostic tool for bladder cancer tumor grading and general Raman spectroscopy-based oncological clinical studies

In-vivo Raman spectroscopy: from basics to applications

For more than two decades, Raman spectroscopy has found widespread use in biological and medical applications. The instrumentation and the statistical evaluation procedures have matured, enabling the lengthy transition from ex-vivo demonstration to in-vivo examinations. This transition goes hand-in-hand with many technological developments and tightly bound requirements for a successful implementation in a clinical environment, which are often difficult to assess for novice scientists in the field. This review outlines the required instrumentation and instrumentation parameters, designs, and developments of fiber optic probes for the in-vivo applications in a clinical setting. It aims at providing an overview of contemporary technology and clinical trials and attempts to identify future developments necessary to bring the emerging technology to the clinical end users. A comprehensive overview of in-vivo applications of fiber optic Raman probes to characterize different tissue and disease types is also given

Optical Diagnostics in Human Diseases

Optical technologies provide unique opportunities for the diagnosis of various pathological disorders. The range of biophotonics applications in clinical practice is considerably wide given that the optical properties of biological tissues are subject to significant changes during disease progression. Due to the small size of studied objects (from μm to mm) and despite some minimum restrictions (low-intensity light is used), these technologies have great diagnostic potential both as an additional tool and in cases of separate use, for example, to assess conditions affecting microcirculatory bed and tissue viability. This Special Issue presents topical articles by researchers engaged in the development of new methods and devices for optical non-invasive diagnostics in various fields of medicine. Several studies in this Special Issue demonstrate new information relevant to surgical procedures, especially in oncology and gynecology. Two articles are dedicated to the topical problem of breast cancer early detection, including during surgery. One of the articles is devoted to urology, namely to the problem of chronic or recurrent episodic urethral pain. Several works describe the studies in otolaryngology and dentistry. One of the studies is devoted to diagnosing liver diseases. A number of articles contribute to the studying of the alterations caused by diabetes mellitus and cardiovascular diseases. The results of all the presented articles reflect novel innovative research and emerging ideas in optical non-invasive diagnostics aimed at their wider translation into clinical practice

Histopathological image analysis: a review,”

Abstract-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

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Advances in Bioengineering

The technological approach and the high level of innovation make bioengineering extremely dynamic and this forces researchers to continuous updating. It involves the publication of the results of the latest scientific research. This book covers a wide range of aspects and issues related to advances in bioengineering research with a particular focus on innovative technologies and applications. The book consists of 13 scientific contributions divided in four sections: Materials Science; Biosensors. Electronics and Telemetry; Light Therapy; Computing and Analysis Techniques

Raman Spectroscopy Techniques for the Detection and Management of Breast Cancer

Breast cancer has recently become the most common cancer worldwide, and with increased incidence, there is increased pressure on health services to diagnose and treat many more patients. Mortality and survival rates for this particular disease are better than other cancer types, and part of this is due to the facilitation of early diagnosis provided by screening programmes, including the National Health Service breast screening programme in the UK. Despite the benefits of the programme, some patients undergo negative experiences in the form of false negative mammograms, overdiagnosis and subsequent overtreatment, and even a small number of cancers are induced by the use of ionising radiation. In addition to this, false positive mammograms cause a large number of unnecessary biopsies, which means significant costs, both financially and in terms of clinicians' time, and discourages patients from attending further screening. Improvement in areas of the treatment pathway is also needed. Surgery is usually the first line of treatment for early breast cancer, with breast conserving surgery being the preferred option compared to mastectomy. This type of operation achieves the same outcome as mastectomy - removal of the tumour - while allowing the patient to retain the majority of their normal breast tissue for improved aesthetic and psychological results. Yet, re-excision operations are often required when clear margins are not achieved, i.e. not all of the tumour is removed. This again has implications on cost and time, and increases the risk to the patient through additional surgery. Currently lacking in both the screening and surgical contexts is the ability to discern specific chemicals present in the breast tissue being assessed/removed. Specifically relevant to mammography is the presence of calcifications, the chemistry of which holds information indicative of pathology that cannot be accessed through x-rays. In addition, the chemical composition of breast tumour tissue has been shown to be different to normal tissue in a variety of ways, with one particular difference being a significant increase in water content. Raman spectroscopy is a rapid, non-ionising, non-destructive technique based on light scattering. It has been proven to discern between chemical types of calcification and subtleties within their spectra that indicate the malignancy status of the surrounding tissue, and differentiate between cancerous and normal breast tissue based on the relative water contents. Furthermore, this thesis presents work aimed at exploring deep Raman techniques to probe breast calcifications at depth within tissue, and using a high wavenumber Raman probe to discriminate tumour from normal tissue predominantly via changes in tissue water content. The ability of transmission Raman spectroscopy to detect different masses and distributions of calcified powder inclusions within tissue phantoms was tested, as well as elucidating a signal profile of a similar inclusion through a tissue phantom of clinically relevant thickness. The technique was then applied to the measurement of clinically active samples of bulk breast tissue from informed and consented patients to try to measure calcifications. Ex vivo specimens were also measured with a high wavenumber Raman probe, which found significant differences between tumour and normal tissue, largely due to water content, resulting in a classification model that achieved 77.1% sensitivity and 90.8% specificity. While calcifications were harder to detect in the ex vivo specimens, promising results were still achieved, potentially indicating a much more widespread influence of calcification in breast tissue, and to obtain useful signal from bulk human tissue is encouraging in itself. Consequently, this work demonstrates the potential value of both deep Raman techniques and high wavenumber Raman for future breast screening and tumour margin assessment methods

Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics

Current research in the life sciences involves the analysis of such a huge amount of image data that automatization is required. This thesis presents several ways how pattern recognition techniques may contribute to improved tumor diagnostics and to the elucidation of vertebrate embryonic development. Chapter 1 studies an approach for exploiting spatial context for the improved estimation of metabolite concentrations from magnetic resonance spectroscopy imaging (MRSI) data with the aim of more robust tumor detection, and compares against a novel alternative. Chapter 2 describes a software library for training, testing and validating classification algorithms that estimate tumor probability based on MRSI. It allows flexible adaptation towards changed experimental conditions, classifier comparison and quality control without need for expertise in pattern recognition. Chapter 3 studies several models for learning tumor classifiers that allow for the common unreliability of human segmentations. For the first time, models are used for this task that additionally employ the objective image information. Chapter 4 encompasses two contributions to an image analysis pipeline for automatically reconstructing zebrafish embryonic development based on time-resolved microscopy: Two approaches for nucleus segmentation are experimentally compared, and a procedure for tracking nuclei over time is presented and evaluated