No description available.

Cranial vault thickness (CVT) has been reported at many different osteometric landmarks and features on the vault. Historically, only a few landmarks are used, often bregma, lambda, vertex, and right and left euryon, and frequently comparisons are based only on “thick” versus “thin” to describe the vault overall. What is inherent in this strategy is the use of a few locations to characterize the entire vault. The problem remains that there is little information concerning CVT variation throughout an individual's vault, and the causes of variation within recent Homo sapiens important to investigating thickness variation between species in Homo. This work describes thickness variation over the entire superior vault and compares the sexes, age groups and populations in recent H. sapiens. A proportional grid is applied to the superior vault to measure thickness at 219 sampling points in a geographically diverse sample of recent H. sapiens. Thickness values are analyzed in their two-dimensional spatial relationships to determine patterns of vault thickness. Males were identified to be thicker than females at more lateral locations and along the midsagittal plane, although this finding is not statistically significant. Individuals over the age of 45 years are found to be statistically significantly thicker than individuals younger than 31 years at more lateral locations of the vault. Aboriginal Australians are statistically significantly thicker at more lateral locations of the vault than any other populations, whereas Northern Canada/Greenland individuals were thinner than other populations at these locations. The trend of thicker vaults in the older age group and the Australians is identified across the vault, although is not statistically significant at more locations. Several thickness patterns are identified. The boss thickening pattern is the most common pattern, followed by a midsagittal pattern, a posterior pattern, and an anterior pattern. Some specimens do not demonstrate thickness variation and are coded as undifferentiated. Each pattern is observed alone and in combination with others, signifying that pattern causes are not mutually exclusive. Boss thickening is interpreted as the result of passive bone thickening during normal bone and brain growth during fetal and adolescent development. The midsagittal thickness pattern coincides with inferred strain along the sagittal suture from nuchal muscle engagement during mastication. Previous researchers have proposed adaptive explanations for thickness variation, such as protection from interpersonal violence; the patterns of cranial vault thickness reported here point to normal growth and development of the brain as a driving force, a relationship that could drive thickness variation in other Homo species. Comparing thickness at bregma, and the frontal and parietal eminences for recent H. sapiens and H. erectus, there is no statistical difference between African and Asian H. erectus, and between the on average thicker H. sapiens populations and H. erectus, based on published data. Future work will investigate the presence or absence of thickness patterns in these fossil species.

One of the serious public health problems is the Traumatic Brain Injury, also known as silent epidemic, affecting millions every year. Management of these patients essentially involves neuroimaging and noncontrast CT scans are the first choice amongst doctors. Significant anatomical changes identified on the neuroimages and volumetric assessment of haemorrhages and haematomas are of critical importance for assessing the patients’ condition for targeted therapeutic and/or surgical interventions. Manual demarcation and annotation by experts is still considered gold standard, however, the interpretation of neuroimages is fraught with inter-observer variability and is considered ’Achilles heel’ amongst radiologists. Errors and variability can be attributed to factors such as poor perception, inaccurate deduction, incomplete knowledge or the quality of the image and only a third of doctors confidently report the findings. The applicability of computer aided dianosis in segmenting the apposite regions and giving ’second opinion’ has been positively appraised to assist the radiologists, however, results of the approaches vary due to parameters of algorithms and manual intervention required from doctors and this presents a gap for automated segmentation and estimation of measurements of noncontrast brain CT scans. The Pattern Driven, Content Aware Active Contours (PDCAAC) Framework developed in this thesis provides robust and efficient segmentation of significant anatomical landmarks, estimations of their sizes and correlation to CT rating to assist the radiologists in establishing the diagnosis and prognosis more confidently. The integration of clinical profile of the patient into image segmentation algorithms has significantly improved their performance by highlighting characteristics of the region of interest. The modified active contour method in the PDCAAC framework achieves Jaccard Similarity Index (JI) of 0.87, which is a significant improvement over the existing methods of active contours achieving JI of 0.807 with Simple Linear Iterative Clustering and Distance Regularized Level Set Evolution. The Intraclass Correlation Coefficient of intracranial measurements is >0.97 compared with radiologists. Automatic seeding of the initial seed curve within the region of interest is incorporated into the method which is a novel approach and alleviates limitation of existing methods. The proposed PDCAAC framework can be construed as a contribution towards research to formulate correlations between image features and clinical variables encompassing normal development, ageing, pathological and traumatic cases propitious to improve management of such patients. Establishing prognosis usually entails survival but the focus can also be extended to functional outcomes, residual disability and quality of life issues.

Yes / Age at death estimation of human skeletal remains is one of the key issues in constructing a biological profile both in forensic and archaeological contexts. The traditional adult osteological methods evaluate macroscopically the morphological changes that occur with increasing age of specific skeletal indicators, such as the cranial sutures, the pubic bone, the auricular surface of the ilium and the sternal end of the ribs. Technologies such as CT and laser scanning are becoming more widely used in anthropology, and several new methods have been developed. This review focuses on how the osteological age-related changes have been evaluated in digital data. Firstly, the 3D virtual copies of the bones have been used to mimic the appearance of the dry bones and the application of the traditional methods. Secondly, the information directly extrapolated from CT scan has been used to qualitatively or quantitatively assess the changes of the trabecular bones, the thickness of the cortical bones, and to perform morphometric analyses. Lastly, the most innovative approach has been the mathematical quantification of the changes of the pelvic joints, calculating the complexity of the surface. The importance of new updated reference datasets, created thanks to the use of CT scanning in forensic settings, is also discussed. / CV was supported from the Danish Council for Independent Research (DFF – 4005-00102B – FTP)

A characteristic feature of Chronic Obstructive Pulmonary Disease (COPD) is an abnormal inflammatory response in the lungs to inhaled particles or gases. The ability to assess and monitor this response in the lungs of COPD patients is important for understanding the pathogenic mechanisms, but also provides a measure of the activity of the disease. Disease activity is more likely to relate to lung inflammation rather than the degree of airflow limitation as measured by the FEV1. Preliminary studies have shown the 18F fluorodeoxyglucose positron emission tomography (18F FDG-PET) signal, as a measure of lung inflammation, is quantifiable in the lungs and is increased in COPD patients compared to controls. However, the methodology requires standardisation and any further enhancement of the methodology would improve its application to assess inflammation in the lungs. I investigated various methods of assessing FDG uptake in the lungs and assessed the reproducibility of these methods, and particularly evaluated whether the data was reproducible or not in the COPD patients (smokers and ex-smokers). This data was then compared with a group of healthy controls to assess the role of dynamic 18F FDG-PET scanning as a surrogate marker of lung inflammation. My data showed a good reproducibility of all methods of assessing FDG lung uptake. However, using conventional Patlak analysis, the uptake was not statistically different between COPD and the control group. Encouraging results in favour of COPD patients were nonetheless shown using compartmental methods of assessing the FDG lung uptake, suggesting the need to correct for the effect of air and blood (tissue fraction effect) when assessing this in a highly vascular organ like the lungs. A prospective study analysis involving a bigger cohort of COPD patients would be desirable to investigate this further.

Delirium is a severe neuropsychiatric syndrome, characterised by the acute onset of inattention, altered level of arousal, and other mental status abnormalities. Delirium is extremely common in acute stroke, affecting at least 1 in 5 such patients admitted to hospital. It is a serious complication of stroke, being associated with higher mortality, longer length of hospital stay and higher dependency at discharge. The pathophysiology of delirium is not completely understood, and there are no specific treatments. This thesis investigated the role of cortisol in the development of delirium after stroke and also investigated the role of delirium and of cortisol in the development of cognitive impairment in the 12 months after stroke. The thesis specifically investigated whether levels of cortisol in saliva are elevated in delirium and also whether there is a loss of the normal diurnal rhythm in delirium, evidenced by elevated afternoon salivary cortisol levels and reduced morning level to afternoon level ratio. The thesis also investigated whether cortisol levels are persistently elevated in the year after stroke in those who developed delirium and whether cortisol levels are associated with cognitive decline. Finally it investigated whether acute and/or chronic changes seen on Computed Tomography (CT) brain scans taken around the time of stroke onset are associated with the development of delirium after stroke A longitudinal cohort study was conducted in 95 participants aged 60 years or over, who were admitted to hospital with a clinically confirmed stroke. Participants gave informed consent, or proxy consent was obtained if they lacked capacity to consent. At baseline participants underwent brief cognitive testing and were then assessed for the presence of delirium, using DSM IV criteria, at regular intervals during the first two weeks after stroke. At each assessment a saliva sample was collected in the morning and in the afternoon, to measure cortisol. Participants were then visited at 1 month, 4 months and 12 months after stroke onset, at which point they were assessed for the presence of delirium, further saliva samples were taken and a cognitive test battery was completed. 26 (27%) participants developed delirium during the course of the study period. The study found elevated salivary cortisol levels in those with delirium at up to 4 months after stroke, but at 12 months there was no difference between the delirium and no delirium group. A loss of the diurnal rhythm was seen in those who developed delirium at 5 days after stroke, but the diurnal variation had returned to a normal pattern at follow-up. However, in a multivariate analysis, controlling for age, sex, stroke severity (NIHSS), current illness burden (APACHE II), chronic illness burden (CCI) and prior cognitive impairment (IQCODE), neither median salivary cortisol levels in the first two weeks after stroke, nor the ratio of morning to afternoon cortisol levels were independent predictors of delirium diagnosis, although median 9am cortisol approached significance (OR=0.95, 95% confidence interval (CI) 0.89-1.01, p=0.08). In a random effects logistic regression analysis, the probability of developing delirium decreased over time from stroke onset and increased per unit increase in salivary cortisol (nmol/L), however this effect was not statistically significant (OR 1.02, CI 0.84-1.19 P=0.70 for morning cortisol and OR 1.05, CI 0.82-1.25 p=0.46 for afternoon cortisol). Global cognition, measured by the MoCA, was significantly poorer in the delirium group at each time point throughout the 12 months after stroke. However, there was a trend towards improvement in MoCA scores in the whole cohort throughout the 12 month follow-up, with the exception of those who developed the most severe delirium. The presence of delirium at any point during the 12 month follow-up did not affect the rate of change of the MoCA scores over the 12 months after stroke. The presence of brain atrophy identified on admission CT brain scans was independently associated with delirium (OR 3.7, CI 1.15-11.88, p=0.02), however the presence of a visible acute or chronic stroke lesion and the presence of white matter lesions were not. Finally, those who developed delirium had a worse functional outcome, longer length of hospital stay and were more likely to require institutional care or a package of care at home, compared with those who did not develop delirium. This thesis has contributed to our understanding of the mechanisms and phenomenology of delirium after stroke, and has also highlighted areas for further research which will be required to unpick the complex pathophysiology of delirium.

Object detection on natural images has become a single-stage end-to-end process thanks to recent breakthroughs on deep neural networks. By contrast, automated pulmonary nodule detection is usually a three steps method: lung segmentation, generation of nodule candidates and false positive reduction. This project tackles the nodule detection problem with a single stage modelusing a deep neural network. Pulmonary nodules have unique shapes and characteristics which are not present outside of the lungs. We expect the model to capture these characteristics and to only focus on elements inside the lungs when working on raw CT scans (without the segmentation). Nodules are small, distributed and infrequent. We show that a well trained deep neural network can spot relevantfeatures and keep a low number of region proposals without any extra preprocessing or post-processing. Due to the visual nature of the task, we designed a three-dimensional convolutional neural network with residual connections. It was inspired by the region proposal network of the Faster R-CNN detection framework. The evaluation is performed on the LUNA16 dataset. The final score is 0.826 which is the average sensitivity at 0.125, 0.25, 0.5, 1, 2, 4, and 8 false positives per scan. It can be considered as an average score compared to other submissions to the challenge. However, the solution described here was trained end-to-end and has fewer trainable parameters. / Objektdetektering i naturliga bilder har reducerates till en enstegs process tack vare genombrott i djupa neurala nätverk. Automatisk detektering av pulmonella nodulärer är vanligtvis ett trestegsproblem: segmentering av lunga, generering av nodulärkandidater och reducering av falska positiva utfall. Det här projektet tar sig an nodulärdetektering med en enstegsmodell med hjälp av ett djupt neuralt nätverk. Pulmonella nodulärer har unika karaktärsdrag som inte finns utanför lungorna. Modellen förväntas fånga dessa drag och enbart fokusera på element inuti lungorna när den arbetar med datortomografibilder. Nodulärer är små och glest föredelade. Vi visar att ett vältränat nätverk kan finna relevanta särdrag samt föreslå ett lågt antal intresseregioner utan extra för- eller efter- behandling. På grund av den visuella karaktären av det här problemet så designade vi ett tredimensionellt s.k. convolutional neural network med residualkopplingar. Projektet inspirerades av Faster R-CNN, ett nätverk som utmärker sig i sin förmåga att detektera intresseregioner. Nätverket utvärderades på ett dataset vid namn LUNA16. Det slutgiltiga nätverket testade 0.826, vilket är genomsnittlig sensitivitet vid 0.125, 0.25, 0.5, 1, 2, 4, och 8 falska positiva per utvärdering. Detta kan anses vara genomsnittligt jämfört med andra deltagande i tävlingen, men lösningen som föreslås här är en enstegslösning som utför detektering från början till slut och har färre träningsbara parametrar. / La détection d’objets sur les images naturelles est devenue au fil du temps un processus réalisé de bout en bout en une seule étape grâce aux évolutions récentes des architectures de neurones artificiels profonds. En revanche, la détection automatique de nodules pulmonaires est généralement un processus en trois étapes : la segmentation des poumons (pré-traitement), la génération de zones d’intérêt (modèle) et la réduction des faux positifs (post-traitement). Ce projet s’attaque à la détection des nodules pulmonaires en une seule étape avec un réseau profond de neurones artificiels. Les nodules pulmonaires ont des formes et des structures uniques qui ne sont pas présentes en dehors de cet organe. Nous nous attendons à ce qu’un modèle soit capable de capturer ces caractéristiques et de se focaliser uniquement sur les éléments à l’intérieur des poumons alors même qu’il reçoit des images brutes (sans segmentation des poumons). Les nodules sont petits, peu fréquents et répartis aléatoirement. Nous montrons qu’un modèle correctement entraîné peut repérer les éléments caractéristiques des nodules et générer peu de localisations sans pré-traitement ni post-traitement. Du fait de la nature visuelle de la tâche, nous avons développé un réseau neuronal convolutif tridimensionnel. L’architecture utilisée est inspirée du méta-algorithme de détection Faster R-CNN. L’évaluation est réalisée avec le jeu de données du challenge LUNA16. Le score final est de 0.826 qui représente la sensibilité moyenne pour les valeurs de 0.125, 0.25, 0.5, 1, 2, 4 et 8 faux positifs par scanner. Il peut être considéré comme un score moyen comparé aux autres contributions du challenge. Cependant, la solution décrite montre la faisabilité d’un modèle en une seule étape, entraîné de bout en bout. Le réseau comporte moins de paramètres que la majorité des solutions.

Lung cancer is the most commonly diagnosed cancer in the world and its finding is mainly incidental. New technologies and more specifically artificial intelligence has lately acquired big interest in the medical field as it can automate or bring new information to the medical staff. Many research have been done on the detection or classification of lung cancer. These works are done on local region of interest but only a few of them have been done looking at a full CT-scan. The aim of this thesis was to assess lung damages from CT images using new machine learning methods. First, single predictors had been learned by a 3D resnet architecture: cancer, emphysema, and opacities. Emphysema was learned by the network reaching an AUC of 0.79 whereas cancer and opacity predictions were not really better than chance AUC = 0.61 and AUC = 0.61. Secondly, a multi-task network was used to predict the factors altogether. A training with no prior knowledge and a transfer learning approach using self-supervision were compared. The transfer learning approach showed similar results in the multi-task approach for emphysema with AUC=0.78 vs 0.60 without pre-training and opacities with an AUC=0.61. Moreover using the pre-training approach enabled the network to reach the same performance as each of single factor predictor but with only one multi-task network which saves a lot of computational time. Finally a risk score can be derived from the training to use this information in a clinical context.

Yes / Age estimation methods are often based on the age-related morphological changes of the auricular surface and the pubic bone. In this study, a mathematical approach to quantify these changes has been tested analyzing the curvature variation on 3D models from CT and laser scans. The sample consisted of the 24 Suchey–Brooks (SB) pubic bone casts, 19 auricular surfaces from the Buckberry and Chamberlain (BC) “recording kit” and 98 pelvic bones from the Terry Collection (Smithsonian Institution). Strong and moderate correlations between phases and curvature were found in SB casts (ρ 0.60–0.93) and BC “recording kit” (ρ 0.47–0.75), moderate and weak correlations in the Terry Collection bones (pubic bones: ρ 0.29–0.51, auricular surfaces: ρ 0.33–0.50) but associated with large individual variability and overlap of curvature values between adjacent decades. The new procedure, requiring no expert judgment from the operator, achieved similar correlations that can be found in the classic methods.

A voz é a principal ferramenta de comunicação da espécie humana e quase 70% da população economicamente ativa dos países desenvolvidos dependem direta ou indiretamente dela em sua profissão. Sua produção deve-se ao funcionamento harmônico de sistemas fisiológicos distintos, nos quais a laringe desempenha um importante papel. É nela que as funções de deglutição, respiração e fonação se encontram e também onde o pulso glotal é formado durante a passagem do ar pelas pregas vocais. Se os nervos e músculos da região por alguma razão são lesionados, o funcionamento dessas funções é prejudicado, causando sérios danos à qualidade de vida do indivíduo. Em virtude disso, diversas pesquisas tem sido realizadas visando adquirir informações que auxiliem as tomadas de decisões clínicas e cirúrgicas. Embora diversos avanços tenham sido realizados no campo de modelagens das pregas vocais e nos estudos da laringe, modelos baseados em geometrias de pacientes específicos que possam colaborar mais ativamente no planejamento cirúrgico, permanecem um desafio. Nesse sentido, este trabalho apresenta o desenvolvimento de um modelo computacional tridimensional, com base em imagens de tomografia computacional. Tendo por objetivo impulsionar a modelagem das características fisiológicas de pacientes reais e assim proporcionar maiores informações para tomadas de decisões. Esse modelo foi utilizado em simulações de escoamento de fluido solucionadas por elementos finitos, apresentando possibilidades satisfatórias de contribuir para avanços na modelagem de pacientes com patologias e em abordagens interativas, tal como interferências nos modelos virtuais por interfaces hápticas e simulações virtuais de cirurgia da laringe. / The voice is the main instrument for communication of human beings and almost 70% of the economically active population in the developed countries depends, directly or indirectly, on it for their profession. Its production is due to the harmonious interaction of different physiological systems, in which the larynx plays an important role. The larynx is involved in the deglutition, breathing and phonation functions and it is where the glottal pulse is formed during airflow through the vocal folds. If the nerves and muscles in this region for some reason are injured, this functions are adversely affected, causing serious damages to the individuals quality of life. As a result, several researches have been carried out, aiming at acquiring information that help in the clinical and surgical decision making. Although many progresses had been reached in the field of vocal folds modeling and in larynx studies, patientspecific geometry modeling that may take an active part in the surgical planning are still a challenge. In this regard, this work presents the development of a threedimensional computational model, based on images from computed tomography (CT) scans. This model was used in fluid flow simulations, solved by finite element analysis, showing satisfactory possibilities for contributions to progresses in the modeling of patients with lesions and in interactive approaches, such as interferences in the models with haptic interface and virtual surgery of the larynx.