Broadband hyperspectral imaging for breast tumor detection using spectral and spatial information

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. Here, we use hyperspectral imaging for tumor detection in fresh breast tissue. We evaluated different wavelength ranges and two classification algorithms; a pixel-wise classification algorithm and a convolutional neural network that combines spectral and spatial information. The highest classification performance was obtained using the full wavelength range (450-1650nm). Adding spatial information mainly improved the differentiation of tissue classes within the malignant and healthy classes. High sensitivity and specificity were accomplished, which offers potential for hyperspectral imaging as a margin assessment technique to improve surgical outcome. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreemen

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

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Automated pectoral muscle identification on MLOâ view mammograms: Comparison of deep neural network to conventional computer vision

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149204/1/mp13451_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149204/2/mp13451.pd

Automated evaluation of radiodensities in a digitized mammogram database using local contrast estimation

Mammographic radiodensity is one of the strongest risk factors for developing breast cancer and there exists an urgent need to develop automated methods for predicting this marker. Previous attempts for automatically identifying and quantifying radiodense tissue in digitized mammograms have fallen short of the ideal. Many algorithms require significant heuristic parameters to be evaluated and set for predicting radiodensity. Many others have not demonstrated the efficacy of their techniques with a sufficient large and diverse patient database. This thesis has attempted to address both of these drawbacks in previous work. Novel automated digital image processing algorithms are proposed that have demonstrated the ability to rapidly sift through digitized mammogram databases for accurately estimating radiodensity. A judicious combination of point-processing, statistical, neural and contrast enhancement techniques have been employed for addressing this formidable problem. The algorithms have been developed and exercised using over 700 mammograms obtained from multiple age and ethnic groups and digitized using more than one type of X-ray digitizer. The automated algorithms developed in this thesis have been validated by comparing the estimation results using 40 of these mammograms with those predicted by a previously established manual segmentation technique. The automated algorithms developed in this thesis show considerable promise to be extremely useful in epidemiological studies when correlating other behavioral and genetic risk factors with mammographic radiodensity

Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms

abstract: Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. The purpose of this study is to design and test the global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in the next sequential screening mammography examination. The image dataset includes mammograms acquired from 90 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under curve (AUC) is 0.754 ± 0.024 when applying the new computerized aided diagnosis (CAD) scheme to our testing dataset. The positive predictive value and the negative predictive value were 0.58 and 0.80, respectively.NOTICE: this is the author's version of a work that was accepted for publication in . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in , 38, 348-357. DOI: 10.1016/j.compmedimag.2014.03.00

Cuantificación de glándulas en imágenes histopatológicas de cáncer gástrico

Automatic detection and quantification of glands in gastric cancer may contribute to objectively measure the lesion severity, to develop strategies for early diagnosis, and most importantly to improve the patient categorization; however, gland quantification is a highly subjective task, prone to error due to the high biopsy traffic and the experience of each expert. The present master’s dissertation is composed by three chapters that carry to an objective identification of glands. In the first chapter of this document we present a new approach for segmentation of glandular nuclei based on nuclear local and contextual (neighborhood) information “NLCI”. A Gradient-BoostedRegression-Tree classifier is trained to distinguish between glandular nuclei and non glandular nuclei. Validation was carried out using 45.702 annotated nuclei from 90 fields of view (patches) extracted from whole slide images of patients diagnosed with gastric cancer. NLCI achieved an accuracy of 0.977 and an F-measure of 0.955, while R-CNN yielded corresponding accuracy and F-measures of 0.923 and 0.719, respectively. In second chapter we presents an entire framework for automatic detection of glands in gastric cancer images. By selecting gland candidates from a binarized version of the hematoxylin channel. Next, the gland’s shape and nuclei are characterized using local features which feed a Random-Cross-validation method classifier trained previously with images manually annotated by an expert. Validation was carried out using a data-set with 1.330 from seven fields of view extracted from patients diagnosed with gastric cancer whole slide images. Results showed an accuracy of 93 % using a linear classifier. Finally, in the third chapter analyzing gland and their glandular nuclei most relevant features, since predict if a patient will survive more than a year after being diagnosed with gastric cancer. A feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy “mRMR” approach selects those features that correlated better with patient survival. A data set with 668 Fields of View (FoV), 2.076 glandular structures from 14 whole slide images were extracted from patient diagnosed with gastric cancer. Results showed an accuracy of 78.57 % using a QDA Linear & Quadratic Discriminant Analysis was training with Leave-one-out e.g training with thirteen cases and leaving a separate case to validate.La detección y cuantificación automática de las glándulas en el cáncer gástrico puede contribuir a medir objetivamente la gravedad de la lesión, desarrollar estrategias para el diagnóstico precoz y lo que es más importante, mejorar la categorización del paciente; sin embargo, su cuantificación es una tarea altamente subjetiva, propensa a errores debido al alto tráfico de biopsias y a la experiencia de cada experto. La presente disertación de maestría está compuesta por tres capítulos los cuales llevan a la cuantificación objetiva de glándulas. En el primer capítulo del documento se presenta un nuevo enfoque para la segmentación de los núcleos glandulares en base a la información nuclear local y contextual (vecindario). Se entrenó un Gradient-Boosted-Regression-Tree para distinguir entre núcleos glandulares y núcleos no glandulares. La validación se llevó con 45.702 núcleos anotados manualmente de 90 campos de visión (parches) extraídos de imágenes de biopsias completas de pacientes diagnosticados con cáncer gástrico. NLCI logró una precisión de 0.977% y un F-Score de 0.955%, mientras que fast R-CNN arrojó una precisión de 0.923% y un F-Score y 0.719%. En el segundo capítulo se presenta un marco completo para detección automática de glándulas en imágenes de cáncer gástrico. Las glándulas candidatas se seleccionan de una versión binarizada del canal de hematoxilina. A continuación, la forma y los núcleos de las glándulas se caracterizan y se alimenta un clasificador Random Cross Validation, entrenado previamente con imágenes anotadas manualmente por un experto. La validación se realizó en un conjunto de datos con 1.330 parches extraídos de siete biopsias de pacientes diagnosticados con cáncer gástrico. Los resultados mostraron una precisión del 93% utilizando un clasificador lineal. Finalmente, el tercer capítulo analiza las características más relevantes de las glándulas y sus núcleos glandulares, para predecir la sobrevida a un año de un paciente diagnosticado con cáncer gástrico. Una selección de características basada en información mutua: criterios de dependencia máxima, máxima relevancia y mínima redundancia (mRMR) escogen las características correlacionadas con la supervivencia del paciente. Se extrajo un conjunto de datos con 668 campos de visión (FoV), 2.076 estructuras glandulares de 14 imágenes completas de pacientes diagnosticados con cáncer gástrico. Los resultados mostraron una precisión del 76.3% usando un Análisis Discriminante Lineal y Cuadrático (QDA) y un esquema de evaluación entrenando con trece casos y dejando un caso aparte para validar.Magíster en Ingeniería Biomédica. Línea de investigación: Procesamiento de señale