Directional kernel density estimation for classification of breast tissue spectra

In Breast Conserving Therapy, surgeons measure the thickness of healthy tissue surrounding an excised tumor (surgical margin) via post-operative histological or visual assessment tests that, for lack of enough standardization and reliability, have recurrence rates in the order of 33%. Spectroscopic interrogation of these margins is possible during surgery, but algorithms are needed for parametric or dimension reduction processing. One methodology for tumor discrimination based on dimensionality reduction and nonparametric estimation - in particular, Directional Kernel Density Estimation - is proposed and tested on spectral image data from breast samples. Once a hyperspectral image of the tumor has been captured, a surgeon assists by establishing Regions of Interest where tissues are qualitatively differentiable. After proper normalization, Directional KDE is used to estimate the likelihood of every pixel in the image belonging to each specified tissue class. This information is enough to yield, in almost real time and with 98% accuracy, results that coincide with those provided by histological H&E validation performed after the surgery.Research reported in this paper was funded by projects DA2TOI (codename FIS 2010-19860), FOS4 (codename TEC 2013-47264-C2-1-R) and an undergraduate Research Assistant Fellowship (Beca de Colaboración) entitled “Multispectralenhancement systems for tissue diagnosis in oncology and cardiovascularmedicine,” the latter granted to themain author by the SpanishMinistry of Education, Culture and Sports

Automated skin lesion segmentation with kernel density estimation

Skin lesion segmentation is a complex step for dermoscopy pathological diagnosis. Kernel density estimation is proposed as a segmentation technique based on the statistic distribution of color intensities in the lesion and non-lesion regions.This work is supported by the “Ministerio de Economía, Industria y Competitividad” (MINECO) under projects DA2TOI (FIS2010-19860), SENSA (TEC2016-76021-C2-2-R), the “Instituto de Salud Carlos III” (ISCIII) through projects FUSIODERM (DTS15/00238) and CIBERBBN and the co-financed by FEDER funds

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

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Hyperspectral imaging for resection margin assessment during breast cancer surgery

Complete tumor removal during breast-conserving surgery remains challenging due to the lack of optimal intraoperative margin assessment techniques. This thesis investigates the potential of hyperspectral imaging to assess the resection margin during surgery. Hyperspectral imaging is a non-invasive, optical imaging technique that measures differences in the optical properties of tissue. These differences in optical properties are measured in the form of diffuse reflectance spectra and can be used to differentiate tumor from healthy tissue. By imaging and analyzing the resection margin of a specimen during surgery, direct feedback can be given to the surgeon.We started our research with imaging breast tissue slices, that were obtained after gross-sectioning lumpectomy specimen. We developed a registration method to obtain a high correlation of these optical measurements with histopathology and, thereby, created an extensive hyperspectral database that was used to research the maximum capability of hyperspectral imaging to differentiate tissue types. The highest classification results were obtained using both the visual and near-infrared wavelength range. On hyperspectral signals, representing a single tissue type, we report a sensitivity and specificity above 98%, which indicates that the optical differences in tissue composition and morphology can be used to distinguish tumor from healthy breast tissue. On hyperspectral signals, representing a mixture of tissue classes, the sensitivity and specificity decrease to 80% and 93%, respectively. This is related to the percentage of a specific tissue class in the measured volume. The next step was to image lumpectomy specimen during surgery to verify the feasibility of using hyperspectral imaging during surgery. Hyperspectral imaging was fast and could provide feedback over the entire resection surface of one side of the specimen in 3 minutes. In combination with the classification performance on the tissue slices, these findings support that hyperspectral imaging can become a powerful tool for margin assessment during breast-conserving surgery. Original promotion date: April 24, 2020 (COVID-19)<br/

Development of deep learning methods for head and neck cancer detection in hyperspectral imaging and digital pathology for surgical guidance

Surgeons performing routine cancer resections utilize palpation and visual inspection, along with time-consuming microscopic tissue analysis, to ensure removal of cancer. Despite this, inadequate surgical cancer margins are reported for up to 10-20% of head and neck squamous cell carcinoma (SCC) operations. There exists a need for surgical guidance with optical imaging to ensure complete cancer resection in the operating room. The objective of this dissertation is to evaluate hyperspectral imaging (HSI) as a non-contact, label-free optical imaging modality to provide intraoperative diagnostic information. For comparison of different optical methods, autofluorescence, RGB composite images synthesized from HSI, and two fluorescent dyes are also acquired and investigated for head and neck cancer detection. A novel and comprehensive dataset was obtained of 585 excised tissue specimens from 204 patients undergoing routine head and neck cancer surgeries. The first aim was to use SCC tissue specimens to determine the potential of HSI for surgical guidance in the challenging task of head and neck SCC detection. It is hypothesized that HSI could reduce time and provide quantitative cancer predictions. State-of-the-art deep learning algorithms were developed for SCC detection in 102 patients and compared to other optical methods. HSI detected SCC with a median AUC score of 85%, and several anatomical locations demonstrated good SCC detection, such as the larynx, oropharynx, hypopharynx, and nasal cavity. To understand the ability of HSI for SCC detection, the most important spectral features were calculated and correlated with known cancer physiology signals, notably oxygenated and deoxygenated hemoglobin. The second aim was to evaluate HSI for tumor detection in thyroid and salivary glands, and RGB images were synthesized using the spectral response curves of the human eye for comparison. Using deep learning, HSI detected thyroid tumors with 86% average AUC score, which outperformed fluorescent dyes and autofluorescence, but HSI-synthesized RGB imagery performed with 90% AUC score. The last aim was to develop deep learning algorithms for head and neck cancer detection in hundreds of digitized histology slides. Slides containing SCC or thyroid carcinoma can be distinguished from normal slides with 94% and 99% AUC scores, respectively, and SCC and thyroid carcinoma can be localized within whole-slide images with 92% and 95% AUC scores, respectively. In conclusion, the outcomes of this thesis work demonstrate that HSI and deep learning methods could aid surgeons and pathologists in detecting head and neck cancers.Ph.D