Fibropapillomatosis Prevalence and Distribution in Immature Green Turtles (Chelonia mydas) in Martinique Island (Lesser Antilles).

Fibropapillomatosis (FP) threatens the survival of green turtle (Chelonia mydas) populations at a global scale, and human activities are regularly pointed as causes of high FP prevalence. However, the association of ecological factors with the disease's severity in complex coastal systems has not been well established and requires further studies. Based on a set of 405 individuals caught over ten years, this preliminary study provides the first insight of FP in Martinique Island, which is a critical development area for immature green turtles. Our main results are: (i) 12.8% of the individuals were affected by FP, (ii) FP has different prevalence and temporal evolution between very close sites, (iii) green turtles are more frequently affected on the upper body part such as eyes (41.4%), fore flippers (21.9%), and the neck (9.4%), and (iv) high densities of individuals are observed on restricted areas. We hypothesise that turtle's aggregation enhances horizontal transmission of the disease. FP could represent a risk for immature green turtles' survival in the French West Indies, a critical development area, which replenishes the entire Atlantic population. Continuing scientific monitoring is required to identify which factors are implicated in this panzootic disease and ensure the conservation of the green turtle at an international scale.

Behavioural inference from signal processing using animal-borne multi-sensor loggers: a novel solution to extend the knowledge of sea turtle ecology.

The identification of sea turtle behaviours is a prerequisite to predicting the activities and time-budget of these animals in their natural habitat over the long term. However, this is hampered by a lack of reliable methods that enable the detection and monitoring of certain key behaviours such as feeding. This study proposes a combined approach that automatically identifies the different behaviours of free-ranging sea turtles through the use of animal-borne multi-sensor recorders (accelerometer, gyroscope and time-depth recorder), validated by animal-borne video-recorder data. We show here that the combination of supervised learning algorithms and multi-signal analysis tools can provide accurate inferences of the behaviours expressed, including feeding and scratching behaviours that are of crucial ecological interest for sea turtles. Our procedure uses multi-sensor miniaturized loggers that can be deployed on free-ranging animals with minimal disturbance. It provides an easily adaptable and replicable approach for the long-term automatic identification of the different activities and determination of time-budgets in sea turtles. This approach should also be applicable to a broad range of other species and could significantly contribute to the conservation of endangered species by providing detailed knowledge of key animal activities such as feeding, travelling and resting.

Fine scale geographic residence and annual primary production drive body condition of wild immature green turtles (Chelonia mydas) in Martinique Island (Lesser Antilles).

The change of animal biometrics (body mass and body size) can reveal important information about their living environment as well as determine the survival potential and reproductive success of individuals and thus the persistence of populations. However, weighing individuals like marine turtles in the field presents important logistical difficulties. In this context, estimating body mass (BM) based on body size is a crucial issue. Furthermore, the determinants of the variability of the parameters for this relationship can provide information about the quality of the environment and the manner in which individuals exploit the available resources. This is of particular importance in young individuals where growth quality might be a determinant of adult fitness. Our study aimed to validate the use of different body measurements to estimate BM, which can be difficult to obtain in the field, and explore the determinants of the relationship between BM and size in juvenile green turtles. Juvenile green turtles were caught, measured, and weighed over 6 years (2011-2012; 2015-2018) at six bays to the west of Martinique Island (Lesser Antilles). Using different datasets from this global database, we were able to show that the BM of individuals can be predicted from body measurements with an error of less than 2%. We built several datasets including different morphological and time-location information to test the accuracy of the mass prediction. We show a yearly and north-south pattern for the relationship between BM and body measurements. The year effect for the relationship of BM and size is strongly correlated with net primary production but not with sea surface temperature or cyclonic events. We also found that if the bay locations and year effects were removed from the analysis, the mass prediction degraded slightly but was still less than 3% on average. Further investigations of the feeding habitats in Martinique turtles are still needed to better understand these effects and to link them with geographic and oceanographic conditions.

Projection-based motion compensation and reconstruction of coronary segments and cardiac implantable devices using rotational X-ray angiography.

Cardiologists use two-dimensional projection images in conventional X-ray coronary angiography for the assessment of three-dimensional structures. During minimally invasive interventions there is a need to clearly visualize and analyze contrast filled coronary arteries, surrounding tissue, and implanted devices. Three-dimensional reconstruction of these structures is challenging due to the cardiac and respiratory motion. In this paper we describe a method to automatically generate motion compensated reconstructions of various structures using rotational X-ray angiography. The method uses markers on a device or guide wire to identify and estimate the motion of an object or region of interest in order to register and motion compensate the projection images to generate a motion compensated reconstruction. The method is evaluated on 20 rotational acquisitions and the average marker couple detection rate is 84% for cardiac stents, 90% for closure devices and 20% for contrast filled coronaries. The projection images are motion compensated based on the semi-automatically detected markers and subsequently used for reconstruction. We conclude that it is feasible to reconstruct cardiac stents, closure devices, contrast filled coronaries, and calcified plaques using rotational X-ray angiography.

The effect of automated marker detection on in vivo volumetric stent reconstruction.

New drug eluting stents are less radiopaque than bare metal stents and therefore difficult to see with conventional X-ray coronary angiography. 2D StentBoost and intravascular ultrasound (IVUS) are routinely used to evaluate stent deployment and vessel apposition during a percutaneous coronary intervention. IVUS images give cross-sectional information about the stent lumen and surrounding tissue. 2D StentBoost is a boosted angiogram sequence and visualizes the geometry of the deployed stent from a fixed viewing direction. Three-dimensional motion compensated volumetric stent reconstruction has been developed to give insight into the 3D geometry of the stent. Markers on the balloon wire are used to motion compensate cardiac rotational angiography acquisitions. In this paper we present the effect of automated marker detection on in vivo volumetric cardiac stent reconstructions. Automated or semi-automated marker detection reduces user interaction, potentially reduces total processing time, and increases detection results which leads to higher quality of stent reconstructions.

Semi-automatic landmark detection in digital X-ray images of the spine.

Quantitative diagnosis of 3D scoliotic deformities depends on a number of dedicated measurements. Existing methods rely on the manual determination of a series of anatomical landmarks in X-ray images. We have developed an automatic method to alleviate the burden of this tedious task. Our method looks for a compromise between local image information and global prior constraints and finds the most probable points using dynamic programming optimization. Remaining errors can be quickly corrected by effective user interaction. The first results are promising.

3D reconstruction and analysis of the vertebral body line.

Quantitative analysis of 3D spinal deformities includes measurements related to individual vertebrae and measurements related to the overall shape of the spine. For the latter aspect, we propose to build a 3D model of the line that connects all vertebrae centres. We present two methods that allow reconstructing this vertebral body line in a quick and easy way. Various new descriptors of the spinal shape can be automatically computed. A study is under way to assess their clinical relevance and reliability.