Segmentação de pele e reconhecimento de gestos para interação humano-computador

This paper presents the development of a new approach to skin segmentation and hand gesture recognition in order to compose applications for Human Computer Interaction requiring real-time computing. Tests performed indicate the possibility of using the approach with low-cost equipment

Automated Vascular Smooth Muscle Segmentation, Reconstruction, Classification and Simulation on Whole-Slide Histology

Histology of the microvasculature depicts detailed characteristics relevant to tissue perfusion. One important histologic feature is the smooth muscle component of the microvessel wall, which is responsible for controlling vessel caliber. Abnormalities can cause disease and organ failure, as seen in hypertensive retinopathy, diabetic ischemia, Alzheimer’s disease and improper cardiovascular development. However, assessments of smooth muscle cell content are conventionally performed on selected fields of view on 2D sections, which may lead to measurement bias. We have developed a software platform for automated (1) 3D vascular reconstruction, (2) detection and segmentation of muscularized microvessels, (3) classification of vascular subtypes, and (4) simulation of function through blood flow modeling. Vessels were stained for α-actin using 3,3\u27-Diaminobenzidine, assessing both normal (n=9 mice) and regenerated vasculature (n=5 at day 14, n=4 at day 28). 2D locally adaptive segmentation involved vessel detection, skeletonization, and fragment connection. 3D reconstruction was performed using our novel nucleus landmark-based registration. Arterioles and venules were categorized using supervised machine learning based on texture and morphometry. Simulation of blood flow for the normal and regenerated vasculature was performed at baseline and during demand based on the structural measures obtained from the above tools. Vessel medial area and vessel wall thickness were found to be greater in the normal vasculature as compared to the regenerated vasculature (p\u3c0.001) and a higher density of arterioles was found in the regenerated tissue (p\u3c0.05). Validation showed: a Dice coefficient of 0.88 (compared to manual) for the segmentations, a 3D reconstruction target registration error of 4 μm, and area under the receiver operator curve of 0.89 for vessel classification. We found 89% and 67% decreases in the blood flow through the network for the regenerated vasculature during increased oxygen demand as compared to the normal vasculature, respectively for 14 and 28 days post-ischemia. We developed a software platform for automated vasculature histology analysis involving 3D reconstruction, segmentation, and arteriole vs. venule classification. This advanced the knowledge of conventional histology sampling compared to whole slide analysis, the morphological and density differences in the regenerated vasculature, and the effect of the differences on blood flow and function

Development And Utilization Of Ultrasound Imaging Techniques To Evaluate The Role Of Vascularity In Adult And Aged Rat Achilles Tendon Healing

Tendons are hypovascular tissues, undergoing angiogenesis only during development, wound healing, and pathogenesis. Injured tendons exhibit a healing response with vascular ingrowth during the proliferative phase and vascular regression during remodeling. Despite this normal healing response, tendons never fully regain their original structure or composition. Additionally, aging further increases tendon risk of rupture and impairs healing response. Since the optimal vascularization level and timing during tendon healing is currently unknown, modulating the vascular response during healing could elucidate the role of angiogenesis in tendon injury and ultimately improve the tendon healing outcome. Furthermore, new ultrasound technologies allow for the evaluation of vascular response after injury, which could provide a measure for evaluating tendon healing in vivo. Therefore, the objective of this study is to develop methods for, and evaluate the effect of, vascular modulation in adult and aged rat Achilles tendons during healing using both in vivo ultrasound imaging measures of vascularity and structure and ex vivo measures of tendon compositional and mechanical properties. In Specific Aim 1, we will validate the use of in vivo high-frequency ultrasound technologies to measure vascular changes in rat Achilles tendons. In Specific Aim 2, we will develop methodologies for vascular modulation in an Achilles tendon injury model using the delivery of pro- and anti-angiogenic factors. Finally, in Specific Aim 3, we will apply methods of vascular modulation and ultrasound imaging to determine the role of angiogenesis in adult and aged Achilles tendon healing models. To achieve these goals, we will perform bilateral partial-rupture injuries in the Achilles tendons of adult and aged rats, followed by injections to modulate their vascular response after injury. The animals will receive vascular endothelial growth factor (VEGF), anti-VEGF antibody (B20.4-1-1, Genentech), or saline injections following injury. They will be evaluated using B-mode, color Doppler, photoacoustic, and contrast-enhanced ultrasound imaging weekly post-injury. Additionally, they will undergo in vivo functional assays to assess gait and passive ankle motion. Animals will be sacrificed for histological and mechanical analyses. This study will validate new in vivo methods for evaluating vascularity in tendon injury models, develop potential angiogenic therapies for improved healing outcome, and elucidate the differences in vascular response with age after tendon injury and vascular modulation

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Machine Learning Assisted Digital Pathology

Histopatologiset kudosnäytteet sisältävät valtavan määrän tietoa biologisista mekanismeista, jotka vaikuttavat monien tautien ilmenemiseen ja etenemiseen. Tästä syystä histopatologisten näytteiden arviointi on ollut perustana monien tautien diagnostiikassa vuosikymmenien ajan. Perinteinen histopatologinen arviointi on kuitenkin työläs tehtävä ja lisäksi erittäin altis inhimillisille virheille ja voi siten johtaa virheelliseen tai viivästyneeseen diagnoosiin. Viime vuosien teknologinen kehitys on tuonut patologien käyttöön lasiskannerit ja tiedonhallintajarjestelmät ja sitä myötä mahdollistaneet näytelasien digitoinnin ja käynnistäneet koko patologian työnkulun digitalisaation. Histopatologisten näytteiden saatavuus digitaalisina kuvina on puolestaan mahdollistanut älykkäiden algoritmien ja automatisoitujen laskennallisten kuva-analyysityökalujen kehittämisen diagnostiikan tueksi. Koneoppiminen on tekoälyn osa-alue, joka voidaan määritellä datasta oppimiseksi. Kuva-analyysin sovelluksissa, kuvan pikseliarvot muutetaan kvantitatiiviseksi piirre-esitykseksi jonka pohjalta kuva voidaan muuntaa merkitykselliseksi tiedoksi hyvödyntämällä koneoppimista. Vuosien saatossa koneoppimiseen perustuvan kuva-analyysin menetelmät ovat kehittyneet manuaalisesta piirteidenirroituksesta kohti viimevuosien vallitsevia syväoppimiseen pohjautuvia konvoluutioneuroverkkoja. Koneoppimisen hyödyt histopatologisessa arvioinnissa ovat huomattavat, sillä koneoppiminen mahdollistaa kuvien tulkinnan patologiin verrattavalla tarkkuudella ja siten pystyy merkittävästi parantamaan kliinisen patologian diagnostiikan tarkkuutta, toistettavuutta ja tehokkuutta. Tämä väitöstyö esittelee koneoppimiseen pohjautuvia menetelmiä jotka on kehitetty avustamaan kudosnäytteen histopatologista arviointia, vaihetta joka on merkityksellinen niin kliinisessä diagnostiikassa kuin prekliinisissä tutkimuksissa. Työssä esitellään piirteenirroituksen ja koneoppimisen tehokkuus histopatologiseen arviointiin liittyvissä kuva-analyysitehtävissä kuten kudoksen karakterisoinnissa, sekä rintasyövän etäpesäkkeiden, epiteelikudoksen ja tumien tunnistuksessa. Menetelmien lisäksi tässä väitöstyössä on käsitelty keskeisiä haasteita jotka on huomioitava integroitaessa koneoppimismenetelmiä kliiniseen käyttöön. Ennen kaikkea nämä tutkimukset ovat kuitenkin osoittaneet koneoppimisen mahdollisuudet tulevaisuudessa parantaa patologian kliinisten rutiinitehtävien tehokkuutta ja toistettavuutta sekä diagnostiikan laatua.Histopathological tissue samples contain a vast amount of information on underlying biological mechanisms that contribute to disease manifestation and progression. Therefore, diagnosis from histopathological tissue samples has been the gold standard for decades. However, traditional histopathological assessment is a laborious task and prone to human errors, thereby leading to misdiagnosis or delayed diagnosis. The development of whole slide scanners for digitization of tissue glass slides has initiated the transition to a fully digital pathology workflow that allows scanning, interpretation, and management of digital tissue slides. These advances have been the cornerstone for developing intelligent algorithms and automated computational approaches for histopathological assessment and clinical diagnostics. Machine learning is a subcategory of artificial intelligence and can be defined as a process of learning from data. In image analysis tasks, the raw pixel values are transformed into quantitative feature representations. Based on the image data representation, a machine learning model learns a set of rules that can be used to extract meaningful information and knowledge. Over the years, the field of machine learning based image analysis has developed from manually handcrafting complex features to the recent revolution of deep learning and convolutional neural networks. Histopathological assessment can benefit greatly from the ability of machine learning models to discover patterns and connections from the data. Therefore, machine learning holds great promise to improve the accuracy, reproducibility, and efficiency of clinical diagnostics in the field of digital pathology. This thesis is focused on developing machine learning based methods for assisting in the process of histopathological assessment, which is a significant step in clinical diagnostics as well as in preclinical studies. The studies presented in this thesis show the effectiveness of feature engineering and machine learning in histopathological assessment related tasks, such as; tissue characterisation, metastasis detection, epithelial tissue detection, and nuclei detection. Moreover, the studies presented in this thesis address the key challenges related to variation presented in histopathological data as well as the generalisation problem that need to be considered in order to integrate machine learning approaches into clinical practice. Overall, these studies have demonstrated the potential of machine learning for bringing standardization and reproducibility to the process of histopathological assessment

Automated characterization of Tumor-Infiltrating Lymphocytes (TIL) in histological breast images

Cancer illness has a big influence on society. Its extended proliferation and high aggressiveness make it a difficult problem to solve and therefore a big deal for science. Recently, a research trend has been focusing on how 3D tumor structure affects the development of the cancer and its outcome, especially metastasis. Stromal structure and tumor cell signaling are processes that highly influence tumor migration. Thus, histological analysis becomes a fundamental tool to study tumor structure, which provides valuable information about cell characteristics and organization. The relevance of histological study is supported by the increasing interest of anatomopathologists to have good automatic solutions to support the specialist’s diagnosis. For this purpose, the current thesis proposes an automated approach to analyze hematoxylin and eosin (H&E) stained histological images, particularly coming from breast cancer patients. The proposed method consists on the classification of the nuclei in H&E-stained histological images and the further analysis of tumor-infiltrating lymphocytes (TIL) present on the visualized section. The starting point of the approach is the automatic nuclei-segmented binary mask. Each of the segmented nuclei is classified into two types, cancerous or healthy. The classification is performed by a trained artificial neural network to give two binary masks, each of them containing one type of nuclei. Then, the algorithm can follow two different paths: classification of zones or TIL analysis. Classification of zones has the aim to provide a more comfortable support to perform cancer diagnosis, because it provides quantitative information of tumor lobule size. To achieve it, a nuclei correction step is executed, by which each nucleus class depends on the area surrounding it. In this way, a clearer vision of the existing zones is provided (tumor lobule or tumor microenvironment). The other approach is to perform TIL analysis. This technique is based on the nuclei classified binary masks and analyzes the immune system response against the tumor. This way, healthy cells of tumor microenvironment are detected and quantified. The ratio of TIL occupied area to free microenvironment area is computed as informational parameter. This ratio is calculated by the combination of a manually-segmented zone binary mask and the nuclei classified binary mask. In this way, only healthy nuclei of microenvironment zone are considered, dividing the sum of their area by the free sections of the microenvironment zone (i.e. area of microenvironment zone where nuclei are not present). Moreover, the TIL dispersion factor is computed to study their distribution throughout the area by dividing the microenvironment area in several zones and calculate the standard deviation of the area of lymphocytes within each of them. Afterward, the opposed of standard deviation is computed to obtain the dispersion factor. Automatic results are found to match the gold standard (the pathologist’s diagnosis), although some error is observed after evaluation. The approach taken in this work has a positive outlook, even though some aspects need to be polished, like the algorithm accuracy and the use of a larger set of images to claim a proper functionality for global cases.Ingeniería Biomédic

When the machine does not know measuring uncertainty in deep learning models of medical images

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRecently, Deep learning (DL), which involves powerful black box predictors, has outperformed human experts in several medical diagnostic problems. However, these methods focus exclusively on improving the accuracy of point predictions without assessing their outputs’ quality and ignore the asymmetric cost involved in different types of misclassification errors. Neural networks also do not deliver confidence in predictions and suffer from over and under confidence, i.e. are not well calibrated. Knowing how much confidence there is in a prediction is essential for gaining clinicians’ trust in the technology. Calibrated uncertainty quantification is a challenging problem as no ground truth is available. To address this, we make two observations: (i) cost-sensitive deep neural networks with Dropweights models better quantify calibrated predictive uncertainty, and (ii) estimated uncertainty with point predictions in Deep Ensembles Bayesian Neural Networks with DropWeights can lead to a more informed decision and improve prediction quality. This dissertation focuses on quantifying uncertainty using concepts from cost-sensitive neural networks, calibration of confidence, and Dropweights ensemble method. First, we show how to improve predictive uncertainty by deep ensembles of neural networks with Dropweights learning an approximate distribution over its weights in medical image segmentation and its application in active learning. Second, we use the Jackknife resampling technique to correct bias in quantified uncertainty in image classification and propose metrics to measure uncertainty performance. The third part of the thesis is motivated by the discrepancy between the model predictive error and the objective in quantified uncertainty when costs for misclassification errors or unbalanced datasets are asymmetric. We develop cost-sensitive modifications of the neural networks in disease detection and propose metrics to measure the quality of quantified uncertainty. Finally, we leverage an adaptive binning strategy to measure uncertainty calibration error that directly corresponds to estimated uncertainty performance and address problematic evaluation methods. We evaluate the effectiveness of the tools on nuclei images segmentation, multi-class Brain MRI image classification, multi-level cell type-specific protein expression prediction in ImmunoHistoChemistry (IHC) images and cost-sensitive classification for Covid-19 detection from X-Rays and CT image dataset. Our approach is thoroughly validated by measuring the quality of uncertainty. It produces an equally good or better result and paves the way for the future that addresses the practical problems at the intersection of deep learning and Bayesian decision theory. In conclusion, our study highlights the opportunities and challenges of the application of estimated uncertainty in deep learning models of medical images, representing the confidence of the model’s prediction, and the uncertainty quality metrics show a significant improvement when using Deep Ensembles Bayesian Neural Networks with DropWeights