Tools for Automated Histology Image Analysis

<p>In this thesis, we present three image processing tools inspired by and designed for histology image analysis. Histology, which is the examination of biological tissue under a microscope, is a critical technique in biomedical research and clinical practice. While slide preparation and imaging is increasingly becoming automated, the analysis of the resulting histology images is not: even routine analyses still require the trained eyes of a pathologist. In our work, we aim to understand histology images as a class of signals and develop tools to aid in the automated analysis of these signals. Our first contribution is in the area of histology image normalization, where the goal is to digitally remove the variation in staining between histology images, an important preprocessing step in many histology image analysis systems. To this end, we created a new benchmark dataset with which to compare normalization methods and proposed our own. Our second contribution is a tissue segmentation method, which delineates single-tissue regions in histology images. Along with this method, we propose a new mathematical model for histology images. Our final contribution is a method for describing distributions of angles, which is useful for segmentation as well as a variety of other image processing tasks.</p

Automated colitis detection from endoscopic biopsies as a tissue screening tool in diagnostic pathology

<p>We present a method for identifying colitis in colon biopsies as an extension of our framework for the automated identification of tissues in histology images. Histology is a critical tool in both clinical and research applications, yet even mundane histological analysis, such as the screening of colon biopsies, must be carried out by highly-trained pathologists at a high cost per hour, indicating a niche for potential automation. To this end, we build upon our previous work by extending the histopathology vocabulary (a set of features based on visual cues used by pathologists) with new features driven by the colitis application. We use the multiple-instance learning framework to allow our pixel-level classifier to learn from image-level training labels. The new system achieves accuracy comparable to state-of-the-art biological image classifiers with fewer and more intuitive features.</p

Algorithm and benchmark dataset for stain separation in histology images

<p>In this work, we present a new algorithm and benchmark dataset for stain separation in histology images. Histology is a critical and ubiquitous task in medical practice and research, serving as a gold standard of diagnosis for many diseases. Automating routine histology analysis tasks could reduce health care costs and improve diagnostic accuracy. One challenge in automation is that histology slides vary in their stain intensity and color; we therefore seek a digital method to normalize the appearance of histology images. As histology slides often have multiple stains on them that must be normalized independently, stain separation must occur before normalization. We propose a new digital stain separation method for the universally-used hematoxylin and eosin stain; this method improves on the state-of-the-art by adjusting the contrast of its eosin-only estimate and including a notion of stain interaction. To validate this method, we have collected a new benchmark dataset via chemical destaining containing ground truth images for stain separation, which we release publicly. Our experiments show that our method achieves more accurate stain separation than two comparison methods and that this improvement in separation accuracy leads to improved normalization.</p

Automated Histology Analysis: Opportunities for Signal Processing

<p>Histology is the microscopic inspection of plant or animal tissue. It is a critical component in diagnostic medicine and a tool for studying the pathogenesis and biology of processes such as cancer and embryogenesis. Tissue processing for histology has become increasingly automated, drastically increasing the speed at which histology labs can produce tissue slides for viewing. Another trend is the digitization of these slides, allowing them to be viewed on a computer rather than through a microscope. Despite these changes, much of the routine analysis of tissue sections remains a painstaking, manual task that can only be completed by highly trained pathologists at a high cost per hour. There is, therefore, a niche for image analysis methods that can automate some aspects of this analysis. These methods could also automate tasks that are prohibitively time-consuming for humans, e.g., discovering new disease markers from hundreds of whole-slide images (WSIs) or precisely quantifying tissues within a tumor.</p

A vocabulary for the identification and delineation of teratoma tissue components in hematoxylin and eosin-stained samples.

<p>We propose a methodology for the design of features mimicking the visual cues used by pathologists when identifying tissues in hematoxylin and eosin (H&E)-stained samples.</p> <p>BACKGROUND: H&E staining is the gold standard in clinical histology; it is cheap and universally used, producing a vast number of histopathological samples. While pathologists accurately and consistently identify tissues and their pathologies, it is a time-consuming and expensive task, establishing the need for automated algorithms for improved throughput and robustness.</p> <p>METHODS: We use an iterative feedback process to design a histopathology vocabulary (HV), a concise set of features that mimic the visual cues used by pathologists, e.g. "cytoplasm color" or "nucleus density". These features are based in histology and understood by both pathologists and engineers. We compare our HV to several generic texture-feature sets in a pixel-level classification algorithm.</p> <p>RESULTS: Results on delineating and identifying tissues in teratoma tumor samples validate our expert knowledge-based approach.</p> <p>CONCLUSIONS: The HV can be an effective tool for identifying and delineating teratoma components from images of H&E-stained tissue samples.</p

Indirect structural health monitoring in bridges: scale experiments

<p>In this paper, we use a scale model to experimentally validate an indirect approach to bridge structural health monitoring (SHM). In contrast to a traditional direct monitoring approach with sensors placed on a bridge, the indirect approach uses instrumented vehicles to collect data about the bridge. Indirect monitoring could offer a mobile, sustainable, and economical complementary solution to the traditional direct bridge SHM approach. Acceleration signals were collected from a vehicle and bridge system in a laboratory-scale experiment for four different bridge scenarios and five speeds. These signals were classified using a simple short-time Fourier transform technique meant to detect shifts in the fundamental frequency of the bridge due to changes in the bridge condition. Results show near-perfect detection of changes when this technique is applied to signals collected from the bridge (direct monitoring), and promising levels of detection when one uses signals from sensors on the vehicle (indirect monitoring) instead of those recorded on the bridge itself.</p