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

Ex Vivo and In Vivo Noninvasive Imaging of Epidermal Growth Factor Receptor Inhibition on Colon Tumorigenesis Using Activatable Near-Infrared Fluorescent Probes

Near-infrared fluorescence (NIRF) imaging combined with enzyme-activatable NIRF probes has yielded promising results in cancer detection

A Patient-Specific Approach for Breast Cancer Detection and Tumor Localization Using Infrared Imaging

Breast cancer (BC) is the most common cancer among women in the United States; approximately one out of every 24 women die of related causes. BC screening is a critical factor for improving patient prognosis and survival rate. Infrared (IR) thermography is an accurate, inexpensive and operator independent modality that is not affected by tissue density as it captures surface temperature variations induced by the presence of tumors. A novel patient-specific approach for IR imaging and simulation is proposed. In this work, multi-view IR images of isolated breasts are obtained in the prone position (face down), which allows access to the entire breast surface because the breasts hang freely. The challenge of accurately determining size and location of tumors within the breasts is addressed through numerical simulations of a patient-specific digital breast model. The digital breast models for individual patients are created from clinical images of the breast, such as IR imaging, digital photographs or magnetic resonance images. The numerical simulations of the digital breast model are conducted using ANSYS Fluent, where computed temperature images are generated in the same corresponding views as clinical IRI images. The computed and clinical IRI images are aligned and compared to measure their match. The determination of tumor size and location was conducted through the Levenberg-Marquardt algorithm, which iteratively minimized the mean squared error. The methodology was tested on the breasts of seven patients with biopsy-proven breast cancer with tumor diameters ranging from 8 mm to 27 mm. The method successfully predicted the equivalent tumor diameter within 2 mm and the location was predicted within 6.3 mm in all cases. The time required for the estimation is 48 minutes using a 10-core, 3.41 GHz workstation. The method presented is accurate, fast and has potential to be used as an adjunct modality to mammography in BC screening, especially for dense breasts

A clinically translatable hyperspectral endoscopy (HySE) system for imaging the gastrointestinal tract.

Hyperspectral imaging (HSI) enables visualisation of morphological and biochemical information, which could improve disease diagnostic accuracy. Unfortunately, the wide range of image distortions that arise during flexible endoscopy in the clinic have made integration of HSI challenging. To address this challenge, we demonstrate a hyperspectral endoscope (HySE) that simultaneously records intrinsically co-registered hyperspectral and standard-of-care white light images, which allows image distortions to be compensated computationally and an accurate hyperspectral data cube to be reconstructed as the endoscope moves in the lumen. Evaluation of HySE performance shows excellent spatial, spectral and temporal resolution and high colour fidelity. Application of HySE enables: quantification of blood oxygenation levels in tissue mimicking phantoms; differentiation of spectral profiles from normal and pathological ex vivo human tissues; and recording of hyperspectral data under freehand motion within an intact ex vivo pig oesophagus model. HySE therefore shows potential for enabling HSI in clinical endoscopy

Raman Spectroscopy in Nanomedicine: Current Status and Future Perspectives

Raman spectroscopy is a branch of vibration spectroscopy which is capable of probing the chemical composition of materials. Recent advances in Raman microscopy have added significantly to the range of applications which now extend from medical diagnostics to exploring interfaces between biological organisms and nanomaterials. In this review, Raman is introduced in a general context, highlighting some of the areas in which the technique has found success in the past, as well as some of the potential benefits it offers over other analytical modalities. The subset of Raman techniques which specifically probe the nanoscale, namely Surface Enhanced and Tip Enhanced Raman Spectroscopy, will be described and specific applications relevant to nanomedical applications will be reviewed. Progress in the use of traditional label-free Raman applied to investigation of nanoscale interactions will be described, and recent developments in Coherent Anti-Stokes Raman Scattering will be explored, particularly applications to biomedical and nanomedical fields

Imaging ductal carcinoma using a hyperspectral imaging system

Hyperspectral Imaging (HSI) is a non-invasive optical imaging modality that shows the potential to aid pathologists in breast cancer diagnoses cases. In this study, breast cancer tissues from different patients were imaged by a hyperspectral system to detect spectral differences between normal and breast cancer tissues, as well as early and late stages of breast cancer. If the spectral differences in these tissue types can be measured, automated systems can be developed to help the pathologist identify suspect biopsy samples, which will improve sample throughput and assist in making critical treatment decisions. Tissue samples from ten different patients were provided by the WVU Pathology Department. The samples from each patient included both normal and ductal carcinoma tissue, both stained and unstained. These cells were imaged using a snapshot HSI system, and the spectral reflectances were evaluated to see if there was a measurable spectral difference between the various cell types. Analysis of the spectral reflectance values indicated that wavelengths near 550nm show the best differentiation between tissue types. This information was used to train image processing algorithms using supervised and unsupervised data. K-Means and Support Vector Machine (SVM) approaches were applied to the hyperspectral data cubes, and successfully detected spectral tissue differences with sensitivity of 85.45%, and specificity of 94.64% with TNR of 95.8%, and FPR of 4.2%. These results were verified by ground truth marking of the tissue samples by a pathologist. This interdisciplinary work will build a bridge between pathology and hyperspectral optical diagnostic imaging in order to reduce time and workload on the pathologist, which can lead to benefit of lead reducing time, and increasing the accuracy of diagnoses

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

Fluorescence molecular tomography: Principles and potential for pharmaceutical research

Fluorescence microscopic imaging is widely used in biomedical research to study molecular and cellular processes in cell culture or tissue samples. This is motivated by the high inherent sensitivity of fluorescence techniques, the spatial resolution that compares favorably with cellular dimensions, the stability of the fluorescent labels used and the sophisticated labeling strategies that have been developed for selectively labeling target molecules. More recently, two and three-dimensional optical imaging methods have also been applied to monitor biological processes in intact biological organisms such as animals or even humans. These whole body optical imaging approaches have to cope with the fact that biological tissue is a highly scattering and absorbing medium. As a consequence, light propagation in tissue is well described by a diffusion approximation and accurate reconstruction of spatial information is demanding. While in vivo optical imaging is a highly sensitive method, the signal is strongly surface weighted, i.e., the signal detected from the same light source will become weaker the deeper it is embedded in tissue, and strongly depends on the optical properties of the surrounding tissue. Derivation of quantitative information, therefore, requires tomographic techniques such as fluorescence molecular tomography (FMT), which maps the three-dimensional distribution of a fluorescent probe or protein concentration. The combination of FMT with a structural imaging method such as X-ray computed tomography (CT) or Magnetic Resonance Imaging (MRI) will allow mapping molecular information on a high definition anatomical reference and enable the use of prior information on tissue’s optical properties to enhance both resolution and sensitivity. Today many of the fluorescent assays originally developed for studies in cellular systems have been successfully translated for experimental studies in animals. The opportunity of monitoring molecular processes non-invasively in the intact organism is highly attractive from a diagnostic point of view but even more so for the drug developer, who can use the techniques for proof-of-mechanism and proof-of-efficacy studies. This review shall elucidate the current status and potential of fluorescence tomography including recent advances in multimodality imaging approaches for preclinical and clinical drug development

Ultrahigh resolution optical coherence tomography for the detection of early stage neoplastic pathologies

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 105-118).Identification of changes associated with early stage disease remains a critical objective of clinical detection and treatment. Effective screening and detection is important for improving outcome because advanced disease, such as metastatic cancer, can be difficult to impossible to cure. Many existing diagnostic modalities, including x-ray imaging, magnetic resonance imaging, ultrasound, and endoscopy do not have sufficient resolution to detect changes in architectural morphology associated with early neoplasia and other pathologies. Diagnostic modalities capable of identifying pre-malignant tissue at an early stage could therefore significantly improve treatment outcome. Optical coherence tomography (OCT) is an emerging biomedical imaging technique that can potentially be used as an in vivo tool for identifying early stage neoplastic pathologies. Recent advances in solid-state laser and nonlinear fiber technology have enabled the development of ultrahigh resolution and spectroscopic OCT techniques which promise to improve tissue differentiation and image contrast. Previous ex vivo, benchtop ultrahigh resolution OCT imaging studies suggest that differentiation of architectural morphology associated with pathology is feasible. This thesis covers the development and investigation of ultrahigh resolution OCT for studies of early neoplastic pathologies.(cont.) A section of this thesis will focus on development and evaluation of a novel turn-key broadband source for OCT. Feasibility studies were performed using ultrahigh resolution OCT for imaging human tissues ex vivo in the clinical pathology laboratory setting. Imaging results will be presented examining a variety of normal and neoplastic lesions in preliminary studies of the thyroid gland, large and small intestine, and breast. These experiments elucidate the optimal imaging parameters, potential and limitations of the technique, and establish the microstructural markers visible in OCT images that are characteristic of pathologic tissues. These studies establish a baseline which should help interpret future in vivo ultrahigh resolution OCT imaging studies.by Pei-Lin Hsiung.Ph.D

A Patient-Specific Infrared Imaging Technique for Adjunctive Breast Cancer Screening: A Clinical and Simulation - Based Approach

Breast cancer is currently the most prevalent form of cancer in women with over 266,000 new diagnoses every year. The various methods used for breast cancer screening range in accuracy and cost, however there is no easily reproducible, reliable, low-cost screening method currently available for detecting cancer in breasts, especially with dense tissue. Steady-state Infrared Imaging (IRI) is unaffected by tissue density and has the potential to detect tumors in the breast by measuring and capturing the thermal profile on the breast surface induced by increased blood perfusion and metabolic activity in a rapidly growing malignant tumor. The current work presents a better understanding of IRI as an accurate breast cancer detection modality. A detailed study utilizing IRI-MRI approach with clinical design and validation of an elaborate IRI-Mammo study are presented by considering patient population, clinical study design, image interpretation, and recommended future path. Clinical IRI images are obtained in this study and an ANSYS-based modeling process developed earlier at RIT is used to localize and detect tumor in seven patients without subjective human interpretation. Further, the unique thermal characteristics of tumors that make their signatures distinct from benign conditions are identified. This work is part of an ongoing multidisciplinary collaboration between a team of thermal engineers and numerical modelers at the Rochester Institute of Technology and a team of clinicians at the Rochester General Hospital. The following components were developed to ensure valid experimentation while considering ethical considerations: IRB documentation, patient protocols, an image acquisition system (camera setup and screening table), and the necessary tools needed for image analysis without human interpretation. IRI images in the prone position were obtained and were used in accurately detecting the presence of a cancerous tumor in seven subjects. The size and location of tumor was also confirmed within 7 mm as compared to biopsy-proven pathology information. The study indicates that the IRI-Mammo approach has potential to be a highly effective adjunctive screening tool that can improve the breast cancer detection rates especially for subjects with dense breast tissue. This method is low cost, no-touch, radiation-free and highly portable, making it an attractive candidate as a breast cancer detection modality. Further, the developed method provided insight into infrared features corresponding to other biological images, pathology reports and patient history