3D Deep Learning for Anatomical Structure Segmentation in Multiple Imaging Modalities

Accurate, automated quantitative segmentation of anatomical structures in radiological scans, such as Magnetic Resonance Imaging (MRI) and Computer Tomography (CT), can produce significant biomarkers and can be integrated into computer-aided diagnosis (CADx) systems to support the in- terpretation of medical images from multi-protocol scanners. However, there are serious challenges towards developing robust automated segmentation techniques, including high variations in anatomical structure and size, varying image spatial resolutions resulting from different scanner protocols, and the presence of blurring artefacts. This paper presents a novel computing ap- proach for automated organ and muscle segmentation in medical images from multiple modalities by harnessing the advantages of deep learning techniques in a two-part process. (1) a 3D encoder-decoder, Rb-UNet, builds a localisation model and a 3D Tiramisu network generates a boundary-preserving segmentation model for each target structure; (2) the fully trained Rb-UNet predicts a 3D bounding box encapsulating the target structure of interest, after which the fully trained Tiramisu model performs segmentation to reveal organ or muscle boundaries for every protrusion and indentation. The proposed approach is evaluated on six different datasets, including MRI, Dynamic Contrast Enhanced (DCE) MRI and CT scans targeting the pancreas, liver, kidneys and iliopsoas muscles. We achieve quantitative measures of mean Dice similarity coefficient (DSC) that surpasses or are comparable with the state-of-the-art and demonstrate statistical stability. A qualitative evaluation performed by two independent experts in radiology and radiography verified the preservation of detailed organ and muscle boundaries

Automatic Quantification of Epidermis Curvature in H&E Stained Microscopic Skin Image of Mice

Changes in the curvature of the epidermis layer is often associated with many skin disorders, such as ichthyoses and generic effects of ageing. Therefore, methods to quantify changes in the curvature are of a scientific and clinical interest. Manual methods to determine curvature are both laborious and intractable to large scale investigations. This paper proposes an automatic algorithm to quantify curvature of microscope images of H&E-stained murine skin. The algorithm can be divided into three key stages. First, skin layers segmentation based on colour deconvolution to separate the original image into three channels of different representations to facilitate segmenting the image into multiple layers, namely epidermis, dermis and subcutaneous layers. The algorithm then further segments the epidermis layer into cornified and basal sub-layers. Secondly, it quantifies the curvature of the epidermis layer by measuring the difference between the epidermis edge and a straight line (theoretical reference line) connecting the two far sides of the epidermis edge. Finally, the curvature measurements extracted from a large number of images of mutant mice are used to identify a list of genes responsible for changes in the epidermis curvature. A dataset of 5714 H&E microscopic images of mutant and wild type mice were used to evaluate the effectiveness of the algorithm

Burden of rotavirus gastroenteritis in the Middle Eastern and North African pediatric population

<p>Abstract</p> <p>Background</p> <p>Rotavirus gastroenteritis (RVGE) is the most common cause of severe childhood diarrhea worldwide. Objectives were to estimate the burden of RVGE among children less than five years old in the Middle East (Bahrain, Iran, Iraq, Israel, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, Syria, UAE, Yemen), North Africa (Algeria, Egypt, Libya, Morocco, Tunisia) and Turkey.</p> <p>Methods</p> <p>A comprehensive literature search was conducted in major databases on the epidemiology and burden of rotavirus among children less than five years old between 1999 and 2009. Data from each country was extracted and compared.</p> <p>Results</p> <p>The search identified 43 studies. RVGE was identified in 16-61% of all cases of acute gastroenteritis, with a peak in the winter. RVGE-related hospitalization rates ranged from 14% to 45%, compared to 14%-28% for non-RVGE. Annually, RVGE caused up to 112 fatalities per 100,000 in certain countries in the region. Hospitalization costs ranged from $1.8 to$4.6 million annually, depending on the country. The most recent literature available showed that G1P[8] was the most prevalent genotype combination in 8 countries (range 23%-56%). G2P[4] was most prevalent in 4 countries (26%-48%). G9P[8] and G4P[8] were also frequently detected.</p> <p>Conclusions</p> <p>RVGE is a common disease associated with significant morbidity, mortality, and economic burden. Given the variety and diverse rotavirus types in the region, use of a vaccine with broad and consistent serotype coverage would be important to help decrease the burden of RVGE in the Middle East and North Africa.</p

Mumps seroprevalence in an unvaccinated population in Izmir, Turkey

WOS: 000086429500029PubMed ID: 1077229

Vitamin A deficiency in healthy children aged 6-59 months in Izmir Province of Turkey

WOS: 000085612000011PubMed ID: 10703036Vitamin A deficiency even at subclinical levels is associated with increased childhood mortality. There have been few studies related to vitamin A status of children in Turkey. The aim of this study was to assess vitamin A status of children aged 6-59 months in Izmir, Turkey, and to evaluate the relationship of these levels with nutritional status. One hundred and sixty children were selected for the study using the cluster sampling method. Serum retinol levels were measured by high-performance liquid chromatography (HPLC) and ranged from 9.8 to 59.2 mu g/dL (mean 29.3 +/- 9.5 mu g/dL). Levels were below the lower limit of the normal range in 15.6% of the children. Deficient and marginal serum retinol among stunted children were observed in 16% and 42% respectively. There was a statistically significant relationship between low serum retinol and stunting (P < 0.05). Although xerophthalmia and other clinical signs of vitamin A deficiency are rarely seen, subclinical vitamin A deficiency is a public health problem in Izmir, Turkey

Prospective motion correction in kidney MRI using FID navigators.

Abdominal MRI scans may require breath-holding to prevent image quality degradation, which can be challenging for patients, especially children. In this study, we evaluate whether FID navigators can be used to measure and correct for motion prospectively, in real-time. FID navigators were inserted into a 3D radial sequence with stack-of-stars sampling. MRI experiments were conducted on 6 healthy volunteers. A calibration scan was first acquired to create a linear motion model that estimates the kidney displacement due to respiration from the FID navigator signal. This model was then applied to predict and prospectively correct for motion in real time during deep and continuous deep breathing scans. Resultant images acquired with the proposed technique were compared with those acquired without motion correction. Dice scores were calculated between inhale/exhale motion states. Furthermore, images acquired using the proposed technique were compared with images from extra-dimensional golden-angle radial sparse parallel, a retrospective motion state binning technique. Images reconstructed for each motion state show that the kidneys' position could be accurately tracked and corrected with the proposed method. The mean of Dice scores computed between the motion states were improved from 0.93 to 0.96 using the proposed technique. Depiction of the kidneys was improved in the combined images of all motion states. Comparing results of the proposed technique and extra-dimensional golden-angle radial sparse parallel, high-quality images can be reconstructed from a fraction of spokes using the proposed method. The proposed technique reduces blurriness and motion artifacts in kidney imaging by prospectively correcting their position both in-plane and through-slice

A generally neglected threat in infant nutrition: incorrect preparation of infant formulae

WOS: 000179136500005PubMed ID: 12458804Breast milk is the most appropriate food for infants. At least 4-6 months of breast feeding is sufficient for all babies if appropriate growth is monitored monthly. However, for those infants unable to breast-feed sufficiently or at all, formula can be given as an alternative. However, serious health problems such as hypernatremic dehydration, malnutrition, and obesity may develop if powdered formula is not appropriately prepared. In the present study, our aim was to investigate whether or not mothers of formula-fed babies in Ozkanlar (Izmir, Turkey) district prepared powdered infant formula appropriately. For this purpose, we visited all (328) families with infants younger than 12 months of age. Forty-two (13%) of these 328 infants were still fed formula. The mothers of the infants were asked to prepare formula for two different meals, and duplicate samples from each prepared formula were taken for the measurement of dry matter. Fifty percent of the mothers diluted formula with 10% more or 10% less water for the second meal as compared with the first meal. Four (10%) mothers diluted formula with 10% or less of the required water, while 27 (64%) prepared formula with 10% or more of the required water. It is concluded that mothers must be informed about the preparation of formula when formula is prescribed for their babies