Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A novel Statistical Shape Model for treatment planning of Retinoblastoma

Purpose: Proper delineation of ocular anatomy in 3D imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic Resonance Imaging (MRI) is nowadays utilized in clinical practice for the diagnosis confirmation and treatment planning of retinoblastoma in infants, where it serves as a source of information, complementary to the Fundus or Ultrasound imaging. Here we present a framework to fully automatically segment the eye anatomy in the MRI based on 3D Active Shape Models (ASM), we validate the results and present a proof of concept to automatically segment pathological eyes. Material and Methods: Manual and automatic segmentation were performed on 24 images of healthy children eyes (3.29±2.15 years). Imaging was performed using a 3T MRI scanner. The ASM comprises the lens, the vitreous humor, the sclera and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens and the optic nerve, then aligning the model and fitting it to the patient. We validated our segmentation method using a leave-one-out cross validation. The segmentation results were evaluated by measuring the overlap using the Dice Similarity Coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90±2.12% for the sclera and the cornea, 94.72±1.89% for the vitreous humor and 85.16±4.91% for the lens. The mean distance error was 0.26±0.09mm. The entire process took 14s on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor and the lens using MRI. We additionally present a proof of concept for fully automatically segmenting pathological eyes. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor.

Vitamin D and central hypersensitivity in patients with chronic pain.

BACKGROUND Low vitamin D is implicated in various chronic pain conditions with, however, inconclusive findings. Vitamin D might play an important role in mechanisms being involved in central processing of evoked pain stimuli but less so for spontaneous clinical pain. OBJECTIVE This study aims to examine the relation between low serum levels of 25-hydroxyvitamin D3 (25-OH D) and mechanical pain sensitivity. DESIGN We studied 174 patients (mean age 48 years, 53% women) with chronic pain. A standardized pain provocation test was applied, and pain intensity was rated on a numerical analogue scale (0-10). The widespread pain index and symptom severity score (including fatigue, waking unrefreshed, and cognitive symptoms) following the 2010 American College of Rheumatology preliminary diagnostic criteria for fibromyalgia were also assessed. Serum 25-OH D levels were measured with a chemiluminescent immunoassay. RESULTS Vitamin deficiency (25-OH D < 50 nmol/L) was present in 71% of chronic pain patients; another 21% had insufficient vitamin D (25-OH D < 75 nmol/L). After adjustment for demographic and clinical variables, there was a mean ± standard error of the mean increase in pain intensity of 0.61 ± 0.25 for each 25 nmol/L decrease in 25-OH D (P = 0.011). Lower 25-OH D levels were also related to greater symptom severity (r = -0.21, P = 0.008) but not to the widespread pain index (P = 0.83) and fibromyalgia (P = 0.51). CONCLUSIONS The findings suggest a role of low vitamin D levels for heightened central sensitivity, particularly augmented pain processing upon mechanical stimulation in chronic pain patients. Vitamin D seems comparably less important for self-reports of spontaneous chronic pain.

Reconstruction of 3D Vertebral Models from a Single 2D Lateral Fluoroscopic Image

Accurate three-dimensional (3D) models of lumbar vertebrae are required for image-based 3D kinematics analysis. MRI or CT datasets are frequently used to derive 3D models but have the disadvantages that they are expensive, time-consuming or involving ionizing radiation (e.g., CT acquisition). In this chapter, we present an alternative technique that can reconstruct a scaled 3D lumbar vertebral model from a single two-dimensional (2D) lateral fluoroscopic image and a statistical shape model. Cadaveric studies are conducted to verify the reconstruction accuracy by comparing the surface models reconstructed from a single lateral fluoroscopic image to the ground truth data from 3D CT segmentation. A mean reconstruction error between 0.7 and 1.4 mm was found.

3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

Computer Vision-Based Carbohydrate Estimation for Type 1 Diabetic Patients Using Smartphones

Background: Individuals with type 1 diabetes (T1D) have to count the carbohydrates (CHOs) of their meal to estimate the prandial insulin dose needed to compensate for the meal’s effect on blood glucose levels. CHO counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control. Method: The GoCARB system is a smartphone application designed to support T1D patients with CHO counting of nonpacked foods. In a typical scenario, the user places a reference card next to the dish and acquires 2 images with his/her smartphone. From these images, the plate is detected and the different food items on the plate are automatically segmented and recognized, while their 3D shape is reconstructed. Finally, the food volumes are calculated and the CHO content is estimated by combining the previous results and using the USDA nutritional database. Results: To evaluate the proposed system, a set of 24 multi-food dishes was used. For each dish, 3 pairs of images were taken and for each pair, the system was applied 4 times. The mean absolute percentage error in CHO estimation was 10 ± 12%, which led to a mean absolute error of 6 ± 8 CHO grams for normal-sized dishes. Conclusion: The laboratory experiments demonstrated the feasibility of the GoCARB prototype system since the error was below the initial goal of 20 grams. However, further improvements and evaluation are needed prior launching a system able to meet the inter- and intracultural eating habits.

Temperature Prediction Model for Bone Drilling Based on Density Distribution and In Vivo Experiments for Minimally Invasive Robotic Cochlear Implantation

Surgical robots have been proposed ex vivo to drill precise holes in the temporal bone for minimally invasive cochlear implantation. The main risk of the procedure is damage of the facial nerve due to mechanical interaction or due to temperature elevation during the drilling process. To evaluate the thermal risk of the drilling process, a simplified model is proposed which aims to enable an assessment of risk posed to the facial nerve for a given set of constant process parameters for different mastoid bone densities. The model uses the bone density distribution along the drilling trajectory in the mastoid bone to calculate a time dependent heat production function at the tip of the drill bit. Using a time dependent moving point source Green's function, the heat equation can be solved at a certain point in space so that the resulting temperatures can be calculated over time. The model was calibrated and initially verified with in vivo temperature data. The data was collected in minimally invasive robotic drilling of 12 holes in four different sheep. The sheep were anesthetized and the temperature elevations were measured with a thermocouple which was inserted in a previously drilled hole next to the planned drilling trajectory. Bone density distributions were extracted from pre-operative CT data by averaging Hounsfield values over the drill bit diameter. Post-operative [Formula: see text]CT data was used to verify the drilling accuracy of the trajectories. The comparison of measured and calculated temperatures shows a very good match for both heating and cooling phases. The average prediction error of the maximum temperature was less than 0.7 °C and the average root mean square error was approximately 0.5 °C. To analyze potential thermal damage, the model was used to calculate temperature profiles and cumulative equivalent minutes at 43 °C at a minimal distance to the facial nerve. For the selected drilling parameters, temperature elevation profiles and cumulative equivalent minutes suggest that thermal elevation of this minimally invasive cochlear implantation surgery may pose a risk to the facial nerve, especially in sclerotic or high density mastoid bones. Optimized drilling parameters need to be evaluated and the model could be used for future risk evaluation.

Evaluating atmospheric methane inversion model results for Pallas, northern Finland

A state-of-the-art inverse model, CarbonTracker Data Assimilation Shell (CTDAS), was used to optimize estimates of methane (CH4) surface fluxes using atmospheric observations of CH4 as a constraint. The model consists of the latest version of the TM5 atmospheric chemistry-transport model and an ensemble Kalman filter based data assimilation system. The model was constrained by atmospheric methane surface concentrations, obtained from the World Data Centre for Greenhouse Gases (WDCGG). Prior methane emissions were specified for five sources: biosphere, anthropogenic, fire, termites and ocean, of which bio-sphere and anthropogenic emissions were optimized. Atmospheric CH 4 mole fractions for 2007 from northern Finland calculated from prior and optimized emissions were compared with observations. It was found that the root mean squared errors of the posterior esti - mates were more than halved. Furthermore, inclusion of NOAA observations of CH 4 from weekly discrete air samples collected at Pallas improved agreement between posterior CH 4 mole fraction estimates and continuous observations, and resulted in reducing optimized biosphere emissions and their uncertainties in northern Finland.

Lake ice phenology from AVHRR data for European lakes: An automated two-step extraction method

Several lake ice phenology studies from satellite data have been undertaken. However, the availability of long-term lake freeze-thaw-cycles, required to understand this proxy for climate variability and change, is scarce for European lakes. Long time series from space observations are limited to few satellite sensors. Data of the Advanced Very High Resolution Radiometer (AVHRR) are used in account of their unique potential as they offer each day global coverage from the early 1980s expectedly until 2022. An automatic two-step extraction was developed, which makes use of near-infrared reflectance values and thermal infrared derived lake surface water temperatures to extract lake ice phenology dates. In contrast to other studies utilizing thermal infrared, the thresholds are derived from the data itself, making it unnecessary to define arbitrary or lake specific thresholds. Two lakes in the Baltic region and a steppe lake on the Austrian–Hungarian border were selected. The later one was used to test the applicability of the approach to another climatic region for the time period 1990 to 2012. A comparison of the extracted event dates with in situ data provided good agreements of about 10 d mean absolute error. The two-step extraction was found to be applicable for European lakes in different climate regions and could fill existing data gaps in future applications. The extension of the time series to the full AVHRR record length (early 1980 until today) with adequate length for trend estimations would be of interest to assess climate variability and change. Furthermore, the two-step extraction itself is not sensor-specific and could be applied to other sensors with equivalent near- and thermal infrared spectral bands.

Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Good reproducibility of heart rate variability after orthostatic challenge in patients with a history of acute coronary syndrome

BACKGROUND: Several parameters of heart rate variability (HRV) have been shown to predict the risk of sudden cardiac death (SCD) in cardiac patients. There is consensus that risk prediction is increased when measuring HRV during specific provocations such as orthostatic challenge. For the first time, we provide data on reproducibility of such a test in patients with a history of acute coronary syndrome. METHODS: Sixty male patients (65+/-8years) with a history of acute coronary syndrome on stable medication were included. HRV was measured in supine (5min) and standing (5min) position on 2 occasions separated by two weeks. For risk assessment relevant time-domain [standard deviation of all R-R intervals (SDNN) and root mean squared standard differences between adjacent R-R intervals (RMSSD)], frequency domain [low-frequency power (LF), high-frequency power (HF) and LF/HF power ratio] and short-term fractal scaling component (DF1) were computed. Absolute reproducibility was assessed with the standard errors of the mean (SEM) and 95% limits of random variation, and relative reproducibility by the intraclass correlation coefficient (ICC). RESULTS: We found comparable SEMs and ICCs in supine position and after an orthostatic challenge test. All ICCs were good to excellent (ICCs between 0.636 and 0.869). CONCLUSIONS: Reproducibility of HRV parameters during orthostatic challenge is good and comparable with supine position.

Natural History of Geographic Atrophy Progression Secondary to Age-Related Macular Degeneration (Geographic Atrophy Progression Study).

PURPOSE The Geographic Atrophy Progression (GAP) study was designed to assess the rate of geographic atrophy (GA) progression and to identify prognostic factors by measuring the enlargement of the atrophic lesions using fundus autofluorescence (FAF) and color fundus photography (CFP). DESIGN Prospective, multicenter, noninterventional natural history study. PARTICIPANTS A total of 603 participants were enrolled in the study; 413 of those had gradable lesion data from FAF or CFP, and 321 had gradable lesion data from both FAF and CFP. METHODS Atrophic lesion areas were measured by FAF and CFP to assess lesion progression over time. Lesion size assessments and best-corrected visual acuity (BCVA) were conducted at screening/baseline (day 0) and at 3 follow-up visits: month 6, month 12, and month 18 (or early exit). MAIN OUTCOME MEASURES The GA lesion progression rate in disease subgroups and mean change from baseline visual acuity. RESULTS Mean (standard error) lesion size changes from baseline, determined by FAF and CFP, respectively, were 0.88 (0.1) and 0.78 (0.1) mm(2) at 6 months, 1.85 (0.1) and 1.57 (0.1) mm(2) at 12 months, and 3.14 (0.4) and 3.17 (0.5) mm(2) at 18 months. The mean change in lesion size from baseline to month 12 was significantly greater in participants who had eyes with multifocal atrophic spots compared with those with unifocal spots (P < 0.001) and those with extrafoveal lesions compared with those with foveal lesions (P = 0.001). The mean (standard deviation) decrease in visual acuity was 6.2 ± 15.6 letters for patients with image data available. Atrophic lesions with a diffuse (mean 0.95 mm(2)) or banded (mean 1.01 mm(2)) FAF pattern grew more rapidly by month 6 compared with those with the "none" (mean, 0.13 mm(2)) and focal (mean, 0.36 mm(2)) FAF patterns. CONCLUSIONS Although differences were observed in mean lesion size measurements using FAF imaging compared with CFP, the measurements were highly correlated with one another. Significant differences were found in lesion progression rates in participants stratified by hyperfluorescence pattern subtype. This large GA natural history study provides a strong foundation for future clinical trials.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

Changes in serum lipids with change and fluctuation in body mass index and body fat distribution in Mexican Americans with type 2 diabetes

This dissertation was written in the format of three journal articles. Paper 1 examined the influence of change and fluctuation in body mass index (BMI) over an eleven-year period, on changes in serum lipid levels (total, HDL, and LDL cholesterol, triglyceride) in a population of Mexican Americans with type 2 diabetes. Linear regression models containing initial lipid value, BMI and age, BMI change (slope of BMI), and BMI fluctuation (root mean square error) were used to investigate associations of these variables with change in lipids over time. Increasing BMI over time was associated with gains in total and LDL cholesterol and triglyceride levels in women. Fluctuation of BMI was not associated with detrimental lipid profiles. These effects were independent of age and were not statistically significant in men. In Mexican-American women with type 2 diabetes, weight reduction is likely to result in more favorable levels of total and LDL cholesterol and triglyceride, without concern for possible detrimental effects of weight fluctuation. Weight reduction may not be as effective in men, but does not appear to be harmful either. ^ Paper 2 examined the associations of upper and total body fat with total cholesterol, HDL and LDL cholesterol, and triglyceride levels in the same population. Multilevel analysis was used to predict serum lipid levels from total body fat (BMI and triceps skinfold) and upper body fat (subscapular skinfold), while controlling for the effects of sex, age and self-correlations across time. Body fat was not strikingly associated with trends in serum lipid levels. However, upper body fat was strongly associated with triglyceride levels. This suggests that loss of upper body fat may be more important than weight loss in management of the hypertriglyceridemia commonly seen in type 2 diabetes. ^ Paper 3 was a review of the literature reporting associations between weight fluctuation and lipid levels. Few studies have reported associations between weight fluctuation and total, LDL, and HDL cholesterol and triglyceride levels. The body of evidence to date suggests that weight fluctuation does not strongly influence levels of total, LDL and HDL cholesterol and triglyceride. ^

Validation of a Spatially Continuous EDEN Water-Surface Model for the Everglades, Florida

The Everglades Depth Estimation Network (EDEN) is an integrated network of realtime water-level monitoring, ground-elevation modeling, and water-surface modeling that provides scientists and managers with current (2000-present), online water-stage and water-depth information for the entire freshwater portion of the Greater Everglades. Continuous daily spatial interpolations of the EDEN network stage data are presented on grid with 400-square-meter spacing. EDEN offers a consistent and documented dataset that can be used by scientists and managers to: (1) guide large-scale field operations, (2) integrate hydrologic and ecological responses, and (3) support biological and ecological assessments that measure ecosystem responses to the implementation of the Comprehensive Everglades Restoration Plan (CERP) (U.S. Army Corps of Engineers, 1999). The target users are biologists and ecologists examining trophic level responses to hydrodynamic changes in the Everglades. The first objective of this report is to validate the spatially continuous EDEN water-surface model for the Everglades, Florida developed by Pearlstine et al. (2007) by using an independent field-measured data-set. The second objective is to demonstrate two applications of the EDEN water-surface model: to estimate site-specific ground elevation by using the validated EDEN water-surface model and observed water depth data; and to create water-depth hydrographs for tree islands. We found that there are no statistically significant differences between model-predicted and field-observed water-stage data in both southern Water Conservation Area (WCA) 3A and WCA 3B. Tree island elevations were derived by subtracting field water-depth measurements from the predicted EDEN water-surface. Water-depth hydrographs were then computed by subtracting tree island elevations from the EDEN water stage. Overall, the model is reliable by a root mean square error (RMSE) of 3.31 cm. By region, the RMSE is 2.49 cm and 7.77 cm in WCA 3A and 3B, respectively. This new landscape-scale hydrological model has wide applications for ongoing research and management efforts that are vital to restoration of the Florida Everglades. The accurate, high-resolution hydrological data, generated over broad spatial and temporal scales by the EDEN model, provides a previously missing key to understanding the habitat requirements and linkages among native and invasive populations, including fish, wildlife, wading birds, and plants. The EDEN model is a powerful tool that could be adapted for other ecosystem-scale restoration and management programs worldwide.

Geochemical and physical sediment properties of sediment cores from the Portuguese shelf

Marine sediments from the Portuguese shelf are influenced by environmental changes in the surrounding continental and marine environment. These are largely controlled by the North Atlantic Oscillation, but additional impacts may arise from episodic tsunamis. In order to investigate these influences, a high resolution multi-proxy study has been carried out on a 5.4 m long gravity core and five box cores from the Tagus prodelta on the western Portuguese margin, incorporating geochemical (Corg/Ntotal ratios, d13Corg, d15N, d18O, Corg and CaCO3 content) and physical sediment properties (magnetic susceptibility, grain-size). Subsurface data of the five box cores indicate no major effect of early postdepositional alteration. Surface data show a higher fraction of terrigenous organic material close to the river mouth and in the southern prodelta. Gravity core GeoB 8903 covers the last 3.2 kyrs with a temporal resolution of at least 0.1 cm/yr. Very high sedimentation rates between 69 and 140 cm core depth indicate a possible disturbance of the record by the AD1755 tsunami, although no evidence for a disturbance is observed in the data. Sea surface temperature and salinity on the prodelta, the local budget of marine NO3- as well as the provenance of organic matter remained virtually constant during the past 3.2 kyrs. A positive correlation between magnetic susceptibility (MS) and North Atlantic Oscillation (NAO) is evident for the past 250 years, coinciding with a negative correlation between mean grain-size and NAO. This is assigned to a constant riverine supply of fine material with high MS, which is diluted by the riverine input of a coarser, low-MS component during NAO negative, high-precipitation phases. End-member modelling of the lithic grain-size spectrum supports this, revealing a third, coarse lithic component. The high abundance of this coarse end-member prior to 2 kyr BP is interpreted as the result of stronger bottom currents, concentrating the coarse sediment fraction by winnowing. As continental climate was more arid prior to 2 kyr BP (Subboreal), the coarse end-member may also consist of dust from local sources. A decrease in grain-size and CaCO3 content after 2 kyr BP is interpreted as a result of decreasing wind strength. The onset of a fining trend and a further decrease in CaCO3 around AD900 occurs simultaneous to climatic variations, reconstructed from eastern North Atlantic records. A strong increase in MS between AD1400 and AD1500 indicates higher lithic terrigenous input, caused by deforestation in the hinterland.