Méthodes de segmentation du ventricule gauche basée sur l'algorithme graph cut pour les images par résonance magnétique et échocardiographiques

L’échocardiographie et l’imagerie par résonance magnétique sont toutes deux des techniques non invasives utilisées en clinique afin de diagnostiquer ou faire le suivi de maladies cardiaques. La première mesure un délai entre l’émission et la réception d’ultrasons traversant le corps, tandis que l’autre mesure un signal électromagnétique généré par des protons d’hydrogène présents dans le corps humain. Les résultats des acquisitions de ces deux modalités d’imagerie sont fondamentalement différents, mais contiennent dans les deux cas de l’information sur les structures du coeur humain. La segmentation du ventricule gauche consiste à délimiter les parois internes du muscle cardiaque, le myocarde, afin d’en calculer différentes métriques cliniques utiles au diagnostic et au suivi de différentes maladies cardiaques, telle la quantité de sang qui circule à chaque battement de coeur. Suite à un infarctus ou autre condition, les performances ainsi que la forme du coeur en sont affectées. L’imagerie du ventricule gauche est utilisée afin d’aider les cardiologues à poser les bons diagnostics. Cependant, dessiner les tracés manuels du ventricule gauche requiert un temps non négligeable aux cardiologues experts, d’où l’intérêt pour une méthode de segmentation automatisée fiable et rapide. Ce mémoire porte sur la segmentation du ventricule gauche. La plupart des méthodes existantes sont spécifiques à une seule modalité d’imagerie. Celle proposée dans ce document permet de traiter rapidement des acquisitions provenant de deux modalités avec une précision de segmentation équivalente au tracé manuel d’un expert. Pour y parvenir, elle opère dans un espace anatomique, induisant ainsi une forme a priori implicite. L’algorithme de Graph Cut, combiné avec des stratégies telles les cartes probabilistes et les enveloppes convexes régionales, parvient à générer des résultats qui équivalent (ou qui, pour la majorité des cas, surpassent) l’état de l’art ii Sommaire au moment de la rédaction de ce mémoire. La performance de la méthode proposée, quant à l’état de l’art, a été démontrée lors d’un concours international. Elle est également validée exhaustivement via trois bases de données complètes en se comparant aux tracés manuels de deux experts et des tracés automatisés du logiciel Syngovia. Cette recherche est un projet collaboratif avec l’Université de Bourgogne, en France.

Field-oriented assessment of agricultural crops through temporal segmentation of modis VI data.

Monitoring agricultural crops constitutes a vital task for the general understanding of land use spatio-temporal dynamics. This paper presents an approach for the enhancement of current crop monitoring capabilities on a regional scale, in order to allow for the analysis of environmental and socio-economic drivers and impacts of agricultural land use. This work discusses the advantages and current limitations of using 250m VI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for this purpose, with emphasis in the difficulty of correctly analyzing pixels whose temporal responses are disturbed due to certain sources of interference such as mixed or heterogeneous land cover. It is shown that the influence of noisy or disturbed pixels can be minimized, and a much more consistent and useful result can be attained, if individual agricultural fields are identified and each field's pixels are analyzed in a collective manner. As such, a method is proposed that makes use of image segmentation techniques based on MODIS temporal information in order to identify portions of the study area that agree with actual agricultural field borders. The pixels of each portion or segment are then analyzed individually in order to estimate the reliability of the temporal signal observed and the consequent relevance of any estimation of land use from that data. The proposed method was applied in the state of Mato Grosso, in mid-western Brazil, where extensive ground truth data was available. Experiments were carried out using several supervised classification algorithms as well as different subsets of land cover classes, in order to test the methodology in a comprehensive way. Results show that the proposed method is capable of consistently improving classification results not only in terms of overall accuracy but also qualitatively by allowing a better understanding of the land use patterns detected. It thus provides a practical and straightforward procedure for enhancing crop-mapping capabilities using temporal series of moderate resolution remote sensing data.

Machine Learning methods for hepatocellular malignancies segmentation and MVI prediction

The aim of this thesis project is to automatically localize HCC tumors in the human liver and subsequently predict if the tumor will undergo microvascular infiltration (MVI), the initial stage of metastasis development. The input data for the work have been partially supplied by Sant'Orsola Hospital and partially downloaded from online medical databases. Two Unet models have been implemented for the automatic segmentation of the livers and the HCC malignancies within it. The segmentation models have been evaluated with the Intersection-over-Union and the Dice Coefficient metrics. The outcomes obtained for the liver automatic segmentation are quite good (IOU = 0.82; DC = 0.35); the outcomes obtained for the tumor automatic segmentation (IOU = 0.35; DC = 0.46) are, instead, affected by some limitations: it can be state that the algorithm is almost always able to detect the location of the tumor, but it tends to underestimate its dimensions. The purpose is to achieve the CT images of the HCC tumors, necessary for features extraction. The 14 Haralick features calculated from the 3D-GLCM, the 120 Radiomic features and the patients' clinical information are collected to build a dataset of 153 features. Now, the goal is to build a model able to discriminate, based on the features given, the tumors that will undergo MVI and those that will not. This task can be seen as a classification problem: each tumor needs to be classified either as “MVI positive” or “MVI negative”. Techniques for features selection are implemented to identify the most descriptive features for the problem at hand and then, a set of classification models are trained and compared. Among all, the models with the best performances (around 80-84% ± 8-15%) result to be the XGBoost Classifier, the SDG Classifier and the Logist Regression models (without penalization and with Lasso, Ridge or Elastic Net penalization).

Supervised and weakly supervised counting-by-segmentation: the fluorescent microscopy use case

This thesis focuses on automating the time-consuming task of manually counting activated neurons in fluorescent microscopy images, which is used to study the mechanisms underlying torpor. The traditional method of manual annotation can introduce bias and delay the outcome of experiments, so the author investigates a deep-learning-based procedure to automatize this task. The author explores two of the main convolutional-neural-network (CNNs) state-of-the-art architectures: UNet and ResUnet family model, and uses a counting-by-segmentation strategy to provide a justification of the objects considered during the counting process. The author also explores a weakly-supervised learning strategy that exploits only dot annotations. The author quantifies the advantages in terms of data reduction and counting performance boost obtainable with a transfer-learning approach and, specifically, a fine-tuning procedure. The author released the dataset used for the supervised use case and all the pre-training models, and designed a web application to share both the counting process pipeline developed in this work and the models pre-trained on the dataset analyzed in this work.

Application of active and transfer learning to wound image segmentation

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications on wound management for pets. The importance of a precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for the chronic wounds. The goal of the research was to propose an automated pipeline capable of segmenting natural light-reflected wound images of animals. Two datasets composed by light-reflected images were used in this work: Deepskin dataset, 1564 human wound images obtained during routine dermatological exams, with 145 manual annotated images; Petwound dataset, a set of 290 wound photos of dogs and cats with 0 annotated images. Two implementations of U-Net Convolutioal Neural Network model were proposed for the automated segmentation. Active Semi-Supervised Learning techniques were applied for human-wound images to perform segmentation from 10% of annotated images. Then the same models were trained, via Transfer Learning, adopting an Active Semi- upervised Learning to unlabelled animal-wound images. The combination of the two training strategies proved their effectiveness in generating large amounts of annotated samples (94% of Deepskin, 80% of PetWound) with the minimal human intervention. The correctness of automated segmentation were evaluated by clinical experts at each round of training thus we can assert that the results obtained in this thesis stands as a reliable solution to perform a correct wound image segmentation. The use of Transfer Learning and Active Semi-Supervied Learning allows to minimize labelling effort from clinicians, even requiring no starting manual annotation at all. Moreover the performances of the model with limited number of parameters suggest the implementation of smartphone-based application to this topic, helping the future standardization of light-reflected images as acknowledge medical images.

Preference Mapping Of Dulce De Leche Commercialized In Brazilian Markets.

Dulce de leche samples available in the Brazilian market were submitted to sensory profiling by quantitative descriptive analysis and acceptance test, as well sensory evaluation using the just-about-right scale and purchase intent. External preference mapping and the ideal sensory characteristics of dulce de leche were determined. The results were also evaluated by principal component analysis, hierarchical cluster analysis, partial least squares regression, artificial neural networks, and logistic regression. Overall, significant product acceptance was related to intermediate scores of the sensory attributes in the descriptive test, and this trend was observed even after consumer segmentation. The results obtained by sensometric techniques showed that optimizing an ideal dulce de leche from the sensory standpoint is a multidimensional process, with necessary adjustments on the appearance, aroma, taste, and texture attributes of the product for better consumer acceptance and purchase. The optimum dulce de leche was characterized by high scores for the attributes sweet taste, caramel taste, brightness, color, and caramel aroma in accordance with the preference mapping findings. In industrial terms, this means changing the parameters used in the thermal treatment and quantitative changes in the ingredients used in formulations.

Lateral Pterygoid Muscle Volume And Migraine In Patients With Temporomandibular Disorders.

Lateral pterygoid muscle (LPM) plays an important role in jaw movement and has been implicated in Temporomandibular disorders (TMDs). Migraine has been described as a common symptom in patients with TMDs and may be related to muscle hyperactivity. This study aimed to compare LPM volume in individuals with and without migraine, using segmentation of the LPM in magnetic resonance (MR) imaging of the TMJ. Twenty patients with migraine and 20 volunteers without migraine underwent a clinical examination of the TMJ, according to the Research Diagnostic Criteria for TMDs. MR imaging was performed and the LPM was segmented using the ITK-SNAP 1.4.1 software, which calculates the volume of each segmented structure in voxels per cubic millimeter. The chi-squared test and the Fisher's exact test were used to relate the TMD variables obtained from the MR images and clinical examinations to the presence of migraine. Logistic binary regression was used to determine the importance of each factor for predicting the presence of a migraine headache. Patients with TMDs and migraine tended to have hypertrophy of the LPM (58.7%). In addition, abnormal mandibular movements (61.2%) and disc displacement (70.0%) were found to be the most common signs in patients with TMDs and migraine. In patients with TMDs and simultaneous migraine, the LPM tends to be hypertrophic. LPM segmentation on MR imaging may be an alternative method to study this muscle in such patients because the hypertrophic LPM is not always palpable.

Spinal Cord Damage In Machado-joseph Disease.

Machado-Joseph disease (SCA3) is the most frequent spinocerebellar ataxia worldwide and characterized by remarkable phenotypic heterogeneity. MRI-based studies in SCA3 focused in the cerebellum and connections, but little is known about cord damage in the disease and its clinical relevance. To evaluate the spinal cord damage in SCA3 through quantitative analysis of MRI scans. A group of 48 patients with SCA3 and 48 age and gender-matched healthy controls underwent MRI on a 3T scanner. We used T1-weighted 3D images to estimate the cervical spinal cord area (CA) and eccentricity (CE) at three C2/C3 levels based on a semi-automatic image segmentation protocol. The scale for assessment and rating of ataxia (SARA) was employed to quantify disease severity. The two groups-SCA3 and controls-were significantly different regarding CA (49.5 ± 7.3 vs 67.2 ± 6.3 mm(2), p < 0.001) and CE values (0.79 ± 0.06 vs 0.75 ± 0.05, p = 0.005). In addition, CA presented a significant correlation with SARA scores in the patient group (p = 0.010). CE was not associated with SARA scores (p = 0.857). In the multiple variable regression, we found that disease duration was the only variable associated with CA (coefficient = -0.629, p = 0.025). SCA3 is characterized by cervical cord atrophy and antero-posterior flattening. In addition, the spinal cord areas did correlate with disease severity. This suggests that quantitative analyses of the spinal cord MRI might be a useful biomarker in SCA3.

Representação da área de responsabilidade de jogadores de futebol através do Diagrama de Voronoi

Universidade Estadual de Campinas . Faculdade de Educação Física

Desenvolvimento de ferramentas de análise de desempenho de atletas em 3 diferentes modalidades : Handebol, Provas Combinadas e Salto com Vara

Universidade Estadual de Campinas. Faculdade de Educação Física

Analise cinematica das trajetorias de jogadores de handebol obtidas por rastreamento automatico

Universidade Estadual de Campinas . Faculdade de Educação Física

Analise de marcha : protocolo experimental a partir de variaveis cinematicas e antropometricas

Universidade Estadual de Campinas . Faculdade de Educação Física

Desenvolvimento de uma metodologia para reconstrução da superficie do pe humano por videogrametria

Universidade Estadual de Campinas . Faculdade de Educação Física

Segmental LeFort I osteotomy for treatment of a class III malocclusion with temporomandibular disorder

This article reports the case of a 19-year-old young man with Class III malocclusion and posterior crossbite with concerns about temporomandibular disorder (TMD), esthetics and functional problems. Surgical-orthodontic treatment was carried out by decompensation of the mandibular incisors and segmentation of the maxilla in 4 pieces, which allowed expansion and advancement. Remission of the signs and symptoms occurred after surgical-orthodontic intervention. The maxillary dental arch presented normal transverse dimension. Satisfactory static and functional occlusion and esthetic results were achieved and remained stable. Three years after the surgical-orthodontic treatment, no TMD sign or symptom was observed and the occlusal results had not changed. When vertical or horizontal movements of the maxilla in the presence of moderate maxillary constriction are necessary, segmental LeFort I osteotomy can be an important part of treatment planning.

Border figure detection using a phase oscillator network with dynamical coupling

Oscillator networks have been developed in order to perform specific tasks related to image processing. Here we analytically investigate the existence of synchronism in a pair of phase oscillators that are short-range dynamically coupled. Then, we use these analytical results to design a network able of detecting border of black-and-white figures. Each unit composing this network is a pair of such phase oscillators and is assigned to a pixel in the image. The couplings among the units forming the network are also dynamical. Border detection emerges from the network activity.