Deep learning-based detection of anthropometric landmarks in 3D infants head models

Deformational plagiocephaly (DP) is a cranial deformity characterized by an asymmetrical distortion of an infant's skull. The diagnosis and evaluation of DP are performed using cranial asymmetry indexes obtained from cranial measurements, which can be estimated using anthropometric landmarks of the infant's head. However, manual labeling of these landmarks is a time-consuming and tedious task, being also prone to observer variability. In this paper, a novel framework to automatically detect anthropometric landmarks of 3D infant's head models is described. The proposed method is divided into two stages: (i) unfolding of the 3D head model surface; and (ii) landmarks' detection through a deep learning strategy. In the first stage, an unfolding strategy is used to transform the 3D mesh of the head model to a flattened 2D version of it. From the flattened mesh, three 2D informational maps are generated using specific head characteristics. In the second stage, a deep learning strategy is used to detect the anthropometric landmarks in a 3-channel image constructed using the combination of informational maps. The proposed framework was validated in fifteen 3D synthetic models of infant's head, being achieved, in average for all landmarks, a mean distance error of 3.5 mm between the automatic detection and a manually constructed ground-truth. Moreover, the estimated cranial measurements were comparable to the ones obtained manually, without statistically significant differences between them for most of the indexes. The obtained results demonstrated the good performance of the proposed method, showing the potential of this framework in clinical practice.The present submission corresponds to original research work of the authors and has never been submitted elsewhere. Moreover, this work was funded by the project NORTE-01-0145-FEDER-024300, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). Moreover, this work has been also supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019. Furthermore, the authors acknowledge FCT, Portugal, and the European Social Found, European Union, for funding support through the "Programa Operacional Capital Humano" (POCH) in the scope of the PhD grants SFRH/BD/136670/2018 (Helena R. Torres), SFRH/BD/136721/2018 (Bruno Oliveira), and SFRH/BD/131545/2017 (Fernando Veloso)

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

3D statistical shape analysis of the face in Apert syndrome

Timely diagnosis of craniofacial syndromes as well as adequate timing and choice of surgical technique are essential for proper care management. Statistical shape models and machine learning approaches are playing an increasing role in Medicine and have proven its usefulness. Frameworks that automate processes have become more popular. The use of 2D photographs for automated syndromic identification has shown its potential with the Face2Gene application. Yet, using 3D shape information without texture has not been studied in such depth. Moreover, the use of these models to understand shape change during growth and its applicability for surgical outcome measurements have not been analysed at length. This thesis presents a framework using state-of-the-art machine learning and computer vision algorithms to explore possibilities for automated syndrome identification based on shape information only. The purpose of this was to enhance understanding of the natural development of the Apert syndromic face and its abnormality as compared to a normative group. An additional method was used to objectify changes as result of facial bipartition distraction, a common surgical correction technique, providing information on the successfulness and on inadequacies in terms of facial normalisation. Growth curves were constructed to further quantify facial abnormalities in Apert syndrome over time along with 3D shape models for intuitive visualisation of the shape variations. Post-operative models were built and compared with age-matched normative data to understand where normalisation is coming short. The findings in this thesis provide markers for future translational research and may accelerate the adoption of the next generation diagnostics and surgical planning tools to further supplement the clinical decision-making process and ultimately to improve patients’ quality of life

Facial Curvature Detects and Explicates Ethnic Differences in Effects of Prenatal Alcohol Exposure

Background Our objective is to help clinicians detect the facial effects of prenatal alcohol exposure by developing computer-based tools for screening facial form. Methods All 415 individuals considered were evaluated by expert dysmorphologists and categorized as (i) healthy control (HC), (ii) fetal alcohol syndrome (FAS), or (iii) heavily prenatally alcohol exposed (HE) but not clinically diagnosable as FAS; 3D facial photographs were used to build models of facial form to support discrimination studies. Surface curvature-based delineations of facial form were introduced. Results (i) Facial growth in FAS, HE, and control subgroups is similar in both cohorts. (ii) Cohort consistency of agreement between clinical diagnosis and HC-FAS facial form classification is lower for midline facial regions and higher for nonmidline regions. (iii) Specific HC-FAS differences within and between the cohorts include: for HC, a smoother philtrum in Cape Coloured individuals; for FAS, a smoother philtrum in Caucasians; for control-FAS philtrum difference, greater homogeneity in Caucasians; for control-FAS face difference, greater homogeneity in Cape Coloured individuals. (iv) Curvature changes in facial profile induced by prenatal alcohol exposure are more homogeneous and greater in Cape Coloureds than in Caucasians. (v) The Caucasian HE subset divides into clusters with control-like and FAS-like facial dysmorphism. The Cape Coloured HE subset is similarly divided for nonmidline facial regions but not clearly for midline structures. (vi) The Cape Coloured HE subset with control-like facial dysmorphism shows orbital hypertelorism. Conclusions Facial curvature assists the recognition of the effects of prenatal alcohol exposure and helps explain why different facial regions result in inconsistent control-FAS discrimination rates in disparate ethnic groups. Heavy prenatal alcohol exposure can give rise to orbital hypertelorism, supporting a long-standing suggestion that prenatal alcohol exposure at a particular time causes increased separation of the brain hemispheres with a concomitant increase in orbital separation

Image-based Detection of Neuro-facial Differences in Foetal Alcohol Spectrum Disorders

Prenatal exposure to alcohol remains as one of the leading, yet preventable, causes of birth defects and neurodevelopmental disorders in the Western world. Over the past 50 years, since the first documented report on the impact of in utero alcohol exposure, a broad spectrum of associated effects have been recognised. Foetal alcohol spectrum disorders is the collective term encompassing a range of diagnostic classifications that can be identified. At the most severe end of this spectrum are foetal alcohol syndrome (FAS), recognisable by a characteristic set of facial features, growth delay, neurocognitive deficit, and behavioural impairments. Criteria for either of these diagnostic categories typically requires at least two ‘cardinal’ facial features: short palpebral fissure length; thin upper lip-vermillion; and, a smooth philtrum. Methods for identifying these features typically rely on subjective observation. This subjectivity means that accuracy of diagnosis is reliant on the skill and experience of the clinician. However, the main clinical challenges arise when an individual presents with confirmed or suspected prenatal alcohol exposure, but without the facial criteria required for FAS diagnoses. These individuals make up the vast majority of the affected population, and clinical recognition can be extremely challenging. Identification and recognition of facial features associated with prenatal alcohol exposure remain a key area of study. This thesis establishes a novel perspective on the issue of subjective clinical assessment and recognition using 3D face and brain shape analysis. We utilise data from 3D facial imaging, MRI brain images and neurocognitive measures to assess subtle facial differences, face-brain associations and the relationships between face, brain and cognition. Development of innovative techniques and methodologies have allowed us to develop a set of analysis tools applicable to craniofacial assessment, and potentially contribute to the analysis of other facially affected conditions in both clinical and research environments

Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years

Maturation of the human fetal brain should follow precisely scheduled structural growth and folding of the cerebral cortex for optimal postnatal function1 . We present a normative digital atlas of fetal brain maturation based on a prospective international cohort of healthy pregnant women2 , selected using World Health Organization recommendations for growth standards3 . Their fetuses were accurately dated in the first trimester, with satisfactory growth and neurodevelopment from early pregnancy to 2 years of age4,5 . The atlas was produced using 1,059 optimal quality, three dimensional ultrasound brain volumes from 899 of the fetuses and an automated analysis pipeline6–8 . The atlas corresponds structurally to published magnetic resonance images9 , but with finer anatomical details in deep grey matter. The between study site variability represented less than 8.0% of the total variance of all brain measures, supporting pooling data from the eight study sites to produce patterns of normative maturation. We have thereby generated an average representation of each cerebral hemisphere between 14 and 31 weeks’ gestation with quantification of intracranial volume variability and growth patterns. Emergent asymmetries were detectable from as early as 14 weeks, with peak asymmetries in regions associated with language development and functional lateralization between 20 and 26 weeks’ gestation. These patterns were validated in 1,487 three-dimensional brain volumes from 1,295 different fetuses in the same cohort. We provide a unique spatiotemporal benchmark of fetal brain maturation from a large cohort with normative postnatal growth and neurodevelopment

Characterization and Classification of Faces across Age Progression

Facial aging, a new dimension that has recently been added to the problem of face recognition, poses interesting theoretical and practical challenges to the research community . How do humans perceive age ? What constitutes an age-invariant signature for faces ? How do we model facial growth across different ages ? How does facial aging effects impact recognition performance ? This thesis provides a thorough overview of the problem of facial aging and addresses the aforementioned questions. We propose a craniofacial growth model that characterizes growth related shape variations observed in human faces during formative years (0 - 18 yrs). The craniofacial growth model draws inspiration from the `revised' cardioidal strain transformation model proposed in psychophysics and further, incorporates age-based anthropometric evidences collected on facial growth during formative years. Identifying a set of fiducial features on faces, we characterize facial growth by means of growth parameters estimated on the fiducial features. We illustrate how the growth related transformations observed on facial proportions can be studied by means of linear and non-linear equations in facial growth parameters, which subsequently help in computing the growth parameters. The proposed growth model implicitly accounts for factors such as gender, ethnicity, the individual's age group etc. Predicting one's appearance across ages, performing face verification across ages etc. are some of the intended applications of the model. Next, we propose a two-fold approach towards modeling facial aging in adults. Firstly, we develop a shape transformation model that is formulated as a physically-based parametric muscle model that captures the subtle deformations facial features undergo with age. The model implicitly accounts for the physical properties and geometric orientations of the individual facial muscles. Next, we develop an image gradient based texture transformation function that characterizes facial wrinkles and other skin artifacts often observed during different ages. Facial growth statistics (both in terms of shape and texture) play a crucial role in developing the aforementioned transformation models. From a database that comprises of pairs of age separated face images of many individuals, we extract age-based facial measurements across key fiducial features and further, study textural variations across ages. We present experimental results that illustrate the applications of the proposed facial aging model in tasks such as face verification and facial appearance prediction across aging. How sensitive are face verification systems to facial aging effects ? How does age progression affect the similarity between a pair of face images of an individual ? We develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose pre-processing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as 9 years, attains an equal error rate of 8.5%