Direct identification of breast cancer pathologies using blind separation of label-free localized reflectance measurements

Breast tumors are blindly identified using Principal (PCA) and Independent Component Analysis (ICA) of localized reflectance measurements. No assumption of a particular theoretical model for the reflectance needs to be made, while the resulting features are proven to have discriminative power of breast pathologies. Normal, benign and malignant breast tissue types in lumpectomy specimens were imaged ex vivo and a surgeon-guided calibration of the system is proposed to overcome the limitations of the blind analysis. A simple, fast and linear classifier has been proposed where no training information is required for the diagnosis. A set of 29 breast tissue specimens have been diagnosed with a sensitivity of 96% and specificity of 95% when discriminating benign from malignant pathologies. The proposed hybrid combination PCA-ICA enhanced diagnostic discrimination, providing tumor probability maps, and intermediate PCA parameters reflected tissue optical properties.This work has been supported by the Spanish Government through CYCIT projects DA2TOI (FIS2010-19860), TFS (TEC2010-20224-C02-02) and Alma’s Eguizabal PhD Grant (FPU12/04130) and by Dartmouth College

Clasificación de lesiones de piel basada en filtros de Gabor y color

CONGRESO ANUAL DE LA SOCIEDAD ESPAÑOLA DE INGENIERÍA BIOMÉDICA (CASEIB 2009) (27) (27.2009.CADIZ, ESPAÑA)Cuando se pretende diagnosticar un posible cáncer de piel, los médicos evalúan la lesión siguiendo diferentes reglas. Aunque la más famosa es la regla ABCD (Asimetría, Borde, Color, Diámetro), una técnica muy empleada en Dermatología es clasificar las lesiones siguiendo un análisis de patrones. Este artículo presenta un método novedoso basado en técnicas de filtrado que clasifica imágenes de color correspondientes a diferentes patrones dermatoscópicos. Hemos evaluado nuestro método usando filtros de Gabor y hemos comparado los resultados obtenidos cuando usamos dos espacios diferentes de color (RGB y L*a*b*) y también cuando consideramos o no la información de color. Para implementar esta tarea hemos evaluado la tasa de clasificación usando 8 vectores diferentes de características. Para cada tipo de vector de características hemos usado el 80% de las imágenes de la base de datos para entrenar una red neuronal fuzzy ARTMAP. El restante 20% de las imágenes fue usado para testear la red. La mejor tasa de clasificación es del 90% cuando usamos el espacio de color L*a*b* para la representación de las imágenes.Junta de Andalucía P06-TIC-0141

Segmentation of Pathology Images: A Deep Learning Strategy with Annotated Data

Cancer has significantly threatened human life and health for many years. In the clinic, histopathology image segmentation is the golden stand for evaluating the prediction of patient prognosis and treatment outcome. Generally, manually labelling tumour regions in hundreds of high-resolution histopathological images is time-consuming and expensive for pathologists. Recently, the advancements in hardware and computer vision have allowed deep-learning-based methods to become mainstream to segment tumours automatically, significantly reducing the workload of pathologists. However, most current methods rely on large-scale labelled histopathological images. Therefore, this research studies label-effective tumour segmentation methods using deep-learning paradigms to relieve the annotation limitations. Chapter 3 proposes an ensemble framework for fully-supervised tumour segmentation. Usually, the performance of an individual-trained network is limited by significant morphological variances in histopathological images. We propose a fully-supervised learning ensemble fusion model that uses both shallow and deep U-Nets, trained with images of different resolutions and subsets of images, for robust predictions of tumour regions. Noise elimination is achieved with Convolutional Conditional Random Fields. Two open datasets are used to evaluate the proposed method: the ACDC@LungHP challenge at ISBI2019 and the DigestPath challenge at MICCAI2019. With a dice coefficient of 79.7 %, the proposed method takes third place in ACDC@LungHP. In DigestPath 2019, the proposed method achieves a dice coefficient 77.3 %. Well-annotated images are an indispensable part of training fully-supervised segmentation strategies. However, large-scale histopathology images are hardly annotated finely in clinical practice. It is common for labels to be of poor quality or for only a few images to be manually marked by experts. Consequently, fully-supervised methods cannot perform well in these cases. Chapter 4 proposes a self-supervised contrast learning for tumour segmentation. A self-supervised cancer segmentation framework is proposed to reduce label dependency. An innovative contrastive learning scheme is developed to represent tumour features based on unlabelled images. Unlike a normal U-Net, the backbone is a patch-based segmentation network. Additionally, data augmentation and contrastive losses are applied to improve the discriminability of tumour features. A convolutional Conditional Random Field is used to smooth and eliminate noise. Three labelled, and fourteen unlabelled images are collected from a private skin cancer dataset called BSS. Experimental results show that the proposed method achieves better tumour segmentation performance than other popular self-supervised methods. However, by evaluated on the same public dataset as chapter 3, the proposed self-supervised method is hard to handle fine-grained segmentation around tumour boundaries compared to the supervised method we proposed. Chapter 5 proposes a sketch-based weakly-supervised tumour segmentation method. To segment tumour regions precisely with coarse annotations, a sketch-supervised method is proposed, containing a dual CNN-Transformer network and a global normalised class activation map. CNN-Transformer networks simultaneously model global and local tumour features. With the global normalised class activation map, a gradient-based tumour representation can be obtained from the dual network predictions. We invited experts to mark fine and coarse annotations in the private BSS and the public PAIP2019 datasets to facilitate reproducible performance comparisons. Using the BSS dataset, the proposed method achieves 76.686 % IOU and 86.6 % Dice scores, outperforming state-of-the-art methods. Additionally, the proposed method achieves a Dice gain of 8.372 % compared with U-Net on the PAIP2019 dataset. The thesis presents three approaches to segmenting cancers from histology images: fully-supervised, unsupervised, and weakly supervised methods. This research effectively segments tumour regions based on histopathological annotations and well-designed modules. Our studies comprehensively demonstrate label-effective automatic histopathological image segmentation. Experimental results prove that our works achieve state-of-the-art segmentation performances on private and public datasets. In the future, we plan to integrate more tumour feature representation technologies with other medical modalities and apply them to clinical research

Unravelling cylindromas : a molecular dissection of CYLD defective tumours

Ph. D.Patients with germline mutations in the tumour suppressor gene CYLD develop multiple cutaneous tumours on the head and neck; historically this has been termed “turban tumour” syndrome. Cylindromas and spiradenomas, hair follicle related tumours seen in this syndrome, cause significant clinical morbidity. Here we characterise the clinical phenotype of these patients, utilising tumour mapping to determine the location of tumours in mutation carriers from two large pedigrees. We demonstrate the disease often affects sites outwith the head and neck, and that androgen stimulated hair follicles are particularly vulnerable to tumour formation. The impact of this disease is severe, with 1 in 4 carriers of this gene undergoing complete scalp removal. To improve this outcome, we performed whole genome profiling of CYLD defective tumours, characterising genomic and transcriptomic changes to determine targetable signalling pathways. High resolution analysis using whole genome array based comparative genomic hybridisation and single nucleotide polymorphism analysis suggest that loss of heterozygosity at the CYLD locus may be the only change required for tumour phenotype. Gene expression profiling highlighted transcriptomic similarity between cylindromas and spiradenomas. Threedimensional reconstruction in silico from serial sections of tumours demonstrated contiguous growth between cylindromas and spiradenomas, in support of this finding. In both tumour types, dysregulated tropomyosin receptor kinase (TRK) signalling was found. Consistent with this, was the finding that TRKB and TRKC protein was overexpressed selectively in the tumour samples, demonstrated on a tissue microarray. Therapeutic utility of targeting this pathway was demonstrated by reduced viability of CYLD defective primary cell cultures in the presence of TRK inhibitors. These preliminary data support the use of TRK inhibitors as a therapeutic strategy in severely affected CYLD mutation carriers.North East Skin Research fund, The Newcastle Hospital Trustees, Breakthrough Breast Cancer Research, The Medical Research Counci

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Surgical Ophthalmic Oncology

Designed as an easy-to-use, practical guide to tumors of the eye, lids, and orbit, this Open Access book comprehensively addresses surgical treatment and management of diseases related to ophthalmic oncology. Surgical Ophthalmic Oncology: A Collaborative Open Access Reference is an ideal reference for general ophthalmologists, surgeons, fellows and trainees around the world who encounter these diseases in the care of their patients. Notably, this book includes considerations for those ophthalmologists offering subspecialty care in environments with limited access to advanced technology and instrumentation. Individual chapters address diagnostic indications, pre-operative and post-operative concerns, and provide detailed explanations of surgical techniques required to manage various eye cancer ailments with help of ample illustrations. High-quality videos included throughout the book provide readers with the opportunity to review surgical steps in real-time as a learning tool. Chapters thoroughly cover tumors of eyelid, cornea and conjunctiva, orbit as well as intraocular tumors, while later chapters discuss ophthalmic radiation therapy. The book concludes with a section on ophthalmic pathology which details essential guidelines on relevant aspects from specimen collection and transport, to interpretation of the pathology report. Surgical Ophthalmic Oncology: A Collaborative Open Access Reference is a unique and necessary valuable resource for ophthalmologists, trainees, and related medical professionals working in underserved areas in providing quality care for patients suffering from ocular cancers. ; Open Access text that discusses Preferred Practice Guidelines for common surgeries performed on tumors of the eye and adnexa Written for general ophthalmologists providing oncology care and specialists practicing in areas with limited access to advanced technology and instrumentation Includes chapters on eyelid tumors, conjunctival and corneal tumors, intraocular tumors, brachytherapy, and ocular pathology Each chapter includes extensive color pictures and relevant video to assist the clinician in the various surgical procedures discusse

Segmentation d'images couleurs et multispectrales de la peau

La délimitation précise du contour des lésions pigmentées sur des images est une première étape importante pour le diagnostic assisté par ordinateur du mélanome. Cette thèse présente une nouvelle approche de la détection automatique du contour des lésions pigmentaires sur des images couleurs ou multispectrales de la peau. Nous présentons d'abord la notion de minimisation d'énergie par coupes de graphes en terme de Maxima A-Posteriori d'un champ de Markov. Après un rapide état de l'art, nous étudions l'influence des paramètres de l'algorithme sur les contours d'images couleurs. Dans ce cadre, nous proposons une fonction d'énergie basée sur des classifieurs performants (Machines à support de vecteurs et Forêts aléatoires) et sur un vecteur de caractéristiques calculé sur un voisinage local. Pour la segmentation de mélanomes, nous estimons une carte de concentration des chromophores de la peau, indices discriminants du mélanomes, à partir d'images couleurs ou multispectrales, et intégrons ces caractéristiques au vecteur. Enfin, nous détaillons le schéma global de la segmentation automatique de mélanomes, comportant une étape de sélection automatique des "graines" utiles à la coupure de graphes ainsi que la sélection des caractéristiques discriminantes. Cet outil est comparé favorablement aux méthodes classiques à base de coupure de graphes en terme de précision et de robustesse.Accurate border delineation of pigmented skin lesion (PSL) images is a vital first step in computer-aided diagnosis (CAD) of melanoma. This thesis presents a novel approach of automatic PSL border detection on color and multispectral skin images. We first introduce the concept of energy minimization by graph cuts in terms of maximum a posteriori estimation of a Markov random field (MAP-MRF framework). After a brief state of the art in interactive graph-cut based segmentation methods, we study the influence of parameters of the segmentation algorithm on color images. Under this framework, we propose an energy function based on efficient classifiers (support vector machines and random forests) and a feature vector calculated on a local neighborhood. For the segmentation of melanoma, we estimate the concentration maps of skin chromophores, discriminating indices of melanomas from color and multispectral images, and integrate these features in a vector. Finally, we detail an global framework of automatic segmentation of melanoma, which comprises two main stages: automatic selection of "seeds" useful for graph cuts and the selection of discriminating features. This tool is compared favorably to classic graph-cut based segmentation methods in terms of accuracy and robustness.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

Assessment of monthly rain fade in the equatorial region at C & KU-band using measat-3 satellite links

C & Ku-band satellite communication links are the most commonly used for equatorial satellite communication links. Severe rainfall rate in equatorial regions can cause a large rain attenuation in real compared to the prediction. ITU-R P. 618 standards are commonly used to predict satellite rain fade in designing satellite communication network. However, the prediction of ITU-R is still found to be inaccurate hence hinder a reliable operational satellite communication link in equatorial region. This paper aims to provide an accurate insight by assessment of the monthly C & Ku-band rain fade performance by collecting data from commercial earth stations using C band and Ku-band antenna with 11 m and 13 m diameter respectively. The antennas measure the C & Ku-band beacon signal from MEASAT-3 under equatorial rain conditions. The data is collected for one year in 2015. The monthly cumulative distribution function is developed based on the 1-year data. RMSE analysis is made by comparing the monthly measured data of C-band and Ku-band to the ITU-R predictions developed based on ITU-R’s P.618, P.837, P.838 and P.839 standards. The findings show that Ku-band produces an average of 25 RMSE value while the C-band rain attenuation produces an average of 2 RMSE value. Therefore, the ITU-R model still under predicts the rain attenuation in the equatorial region and this call for revisit of the fundamental quantity in determining the rain fade for rain attenuation to be re-evaluated

Bio-surfactants-based lipid architectures as nanomedicine platforms

The use of nanocarriers for drug delivery and imaging purposes have highly increased in the last decades. Both hard and soft matter-based formulations can provide selective and efficient treatment in several administration routes. Indeed, the biocompatibility and the biodegradability of the formulations represent a key requirement in order to translate the in vitro studies into in vivo investigations. Therefore, lipids are a safe choice as building blocks to formulate a large variety of liquid crystalline architectures in water. Vesicles, hexosomes and cubosomes have been adopted as nanomedicine platforms providing excellent biological performances. However, several drawbacks may impact the application of these carriers: the poor stability in the physiological environment and the biodegradability of the stabilizing agent required to sterically stabilized the nanoparticles (NPs) are few examples. Given the importance these materials have acquired nowadays in the nanomedicine field, this thesis is devoted to investigating on the factors that can enhance the physico-chemical and biological performances of these nanoparticles for systemic and topical administration. Most of the formulations presented in this thesis were prepared using monoolein as building block, given its biocompatibility and lower cytotoxicity in comparison with other surfactants. However, the potential application of cell-derived nanoparticles known as nanoerythrosomes for medical imaging was also explored. Therefore, the thesis evaluated different approaches: (i) evaluation of the effect of various stabilizers (modified poloxamers, hemicellulose and polyphosphoesters) on monoolein-based cubosomes features, in order to formulate nanoparticles suitable for systemic administration. This investigation was focused on the physico-chemical (bulk and surface) characterization of the empty carriers and of those loaded with antioxidants or fluorophores suitable for in vitro imaging. Bioassays (viability and uptake experiments) were conducted in order to evaluate the biological performance of the differently stabilized cubosomes. (ii) the effect of permeation enhancers and edge activators on monoolein-based vesicles and hexosomes for topical administration. In vitro permeation tests were performed to show the efficacy of these carriers into overcoming the stratum corneum, the first layer of the skin, to deliver antioxidants. (iii) the potential role of nanoparticles derived from red blood cells, nanoerythrosomes, as personal medicine for application in optical imaging. Cross-linking and Click Chemistry were employed to decorate the surface of the nanoparticles and their emission properties in a physiological buffer were evaluate

Surgical Ophthalmic Oncology

Designed as an easy-to-use, practical guide to tumors of the eye, lids, and orbit, this Open Access book comprehensively addresses surgical treatment and management of diseases related to ophthalmic oncology. Surgical Ophthalmic Oncology: A Collaborative Open Access Reference is an ideal reference for general ophthalmologists, surgeons, fellows and trainees around the world who encounter these diseases in the care of their patients. Notably, this book includes considerations for those ophthalmologists offering subspecialty care in environments with limited access to advanced technology and instrumentation. Individual chapters address diagnostic indications, pre-operative and post-operative concerns, and provide detailed explanations of surgical techniques required to manage various eye cancer ailments with help of ample illustrations. High-quality videos included throughout the book provide readers with the opportunity to review surgical steps in real-time as a learning tool. Chapters thoroughly cover tumors of eyelid, cornea and conjunctiva, orbit as well as intraocular tumors, while later chapters discuss ophthalmic radiation therapy. The book concludes with a section on ophthalmic pathology which details essential guidelines on relevant aspects from specimen collection and transport, to interpretation of the pathology report. Surgical Ophthalmic Oncology: A Collaborative Open Access Reference is a unique and necessary valuable resource for ophthalmologists, trainees, and related medical professionals working in underserved areas in providing quality care for patients suffering from ocular cancers. ; Open Access text that discusses Preferred Practice Guidelines for common surgeries performed on tumors of the eye and adnexa Written for general ophthalmologists providing oncology care and specialists practicing in areas with limited access to advanced technology and instrumentation Includes chapters on eyelid tumors, conjunctival and corneal tumors, intraocular tumors, brachytherapy, and ocular pathology Each chapter includes extensive color pictures and relevant video to assist the clinician in the various surgical procedures discusse