Segmentation and grid generation for numerical simulations of vad connections with patient-specific data

Objectives: We are interested in the numerical simulation of the anastomotic region comprised between outflow canula of LVAD and the aorta. Segmenta¬tion, geometry reconstruction and grid generation from patient-specific data remain an issue because of the variable quality of DICOM images, in particular CT-scan (e.g. metallic noise of the device, non-aortic contrast phase). We pro¬pose a general framework to overcome this problem and create suitable grids for numerical simulations.Methods: Preliminary treatment of images is performed by reducing the level window and enhancing the contrast of the greyscale image using contrast-limited adaptive histogram equalization. A gradient anisotropic diffusion filter is applied to reduce the noise. Then, watershed segmentation algorithms and mathematical morphology filters allow reconstructing the patient geometry. This is done using the InsightToolKit library (www.itk.org). Finally the Vascular Model¬ing ToolKit (www.vmtk.org) and gmsh (www.geuz.org/gmsh) are used to create the meshes for the fluid (blood) and structure (arterial wall, outflow canula) and to a priori identify the boundary layers. The method is tested on five different patients with left ventricular assistance and who underwent a CT-scan exam.Results: This method produced good results in four patients. The anastomosis area is recovered and the generated grids are suitable for numerical simulations. In one patient the method failed to produce a good segmentation because of the small dimension of the aortic arch with respect to the image resolution.Conclusions: The described framework allows the use of data that could not be otherwise segmented by standard automatic segmentation tools. In particular the computational grids that have been generated are suitable for simulations that take into account fluid-structure interactions. Finally the presented method features a good reproducibility and fast application.

An electronic portfolio for quantitative assessment of surgical skills in undergraduate medical education.

BACKGROUND We evaluated a newly designed electronic portfolio (e-Portfolio) that provided quantitative evaluation of surgical skills. Medical students at the University of Seville used the e-Portfolio on a voluntary basis for evaluation of their performance in undergraduate surgical subjects. METHODS Our new web-based e-Portfolio was designed to evaluate surgical practical knowledge and skills targets. Students recorded each activity on a form, attached evidence, and added their reflections. Students self-assessed their practical knowledge using qualitative criteria (yes/no), and graded their skills according to complexity (basic/advanced) and participation (observer/assistant/independent). A numerical value was assigned to each activity, and the values of all activities were summated to obtain the total score. The application automatically displayed quantitative feedback. We performed qualitative evaluation of the perceived usefulness of the e-Portfolio and quantitative evaluation of the targets achieved. RESULTS Thirty-seven of 112 students (33%) used the e-Portfolio, of which 87% reported that they understood the methodology of the portfolio. All students reported an improved understanding of their learning objectives resulting from the numerical visualization of progress, all students reported that the quantitative feedback encouraged their learning, and 79% of students felt that their teachers were more available because they were using the e-Portfolio. Only 51.3% of students reported that the reflective aspects of learning were useful. Individual students achieved a maximum of 65% of the total targets and 87% of the skills targets. The mean total score was 345 ± 38 points. For basic skills, 92% of students achieved the maximum score for participation as an independent operator, and all achieved the maximum scores for participation as an observer and assistant. For complex skills, 62% of students achieved the maximum score for participation as an independent operator, and 98% achieved the maximum scores for participation as an observer or assistant. CONCLUSIONS Medical students reported that use of an electronic portfolio that provided quantitative feedback on their progress was useful when the number and complexity of targets were appropriate, but not when the portfolio offered only formative evaluations based on reflection. Students felt that use of the e-Portfolio guided their learning process by indicating knowledge gaps to themselves and teachers.

Endometriosis: alternative methods of medical treatment.

Endometriosis is an inflammatory estrogen-dependent disease defined by the presence of endometrial glands and stroma at extrauterine sites. The main purpose of endometriosis management is alleviating pain associated to the disease. This can be achieved surgically or medically, although in most women a combination of both treatments is required. Long-term medical treatment is usually needed in most women. Unfortunately, in most cases, pain symptoms recur between 6 months and 12 months once treatment is stopped. The authors conducted a literature search for English original articles, related to new medical treatments of endometriosis in humans, including articles published in PubMed, Medline, and the Cochrane Library. Keywords included "endometriosis" matched with "medical treatment", "new treatment", "GnRH antagonists", "Aromatase inhibitors", "selective progesterone receptor modulators", "anti-TNF α", and "anti-angiogenic factors". Hormonal treatments currently available are effective in the relief of pain associated to endometriosis. Among new hormonal drugs, association to aromatase inhibitors could be effective in the treatment of women who do not respond to conventional therapies. GnRH antagonists are expected to be as effective as GnRH agonists, but with easier administration (oral). There is a need to find effective treatments that do not block the ovarian function. For this purpose, antiangiogenic factors could be important components of endometriosis therapy in the future. Upcoming researches and controlled clinical trials should focus on these drugs.

Dense Deformation Field Estimation for Atlas Registration using the Active Contour Framework

In this paper, we propose a new paradigm to carry outthe registration task with a dense deformation fieldderived from the optical flow model and the activecontour method. The proposed framework merges differenttasks such as segmentation, regularization, incorporationof prior knowledge and registration into a singleframework. The active contour model is at the core of ourframework even if it is used in a different way than thestandard approaches. Indeed, active contours are awell-known technique for image segmentation. Thistechnique consists in finding the curve which minimizesan energy functional designed to be minimal when thecurve has reached the object contours. That way, we getaccurate and smooth segmentation results. So far, theactive contour model has been used to segment objectslying in images from boundary-based, region-based orshape-based information. Our registration technique willprofit of all these families of active contours todetermine a dense deformation field defined on the wholeimage. A well-suited application of our model is theatlas registration in medical imaging which consists inautomatically delineating anatomical structures. Wepresent results on 2D synthetic images to show theperformances of our non rigid deformation field based ona natural registration term. We also present registrationresults on real 3D medical data with a large spaceoccupying tumor substantially deforming surroundingstructures, which constitutes a high challenging problem.

A Novel Breast Tissue Density Classification Methodology

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment

Non-medical prescription drug and illicit street drug use among young Swiss men and associated mental health issues.

Non-medical use of prescription drugs (NMUPD) is increasing among the general population, particularly among teenagers and young adults. Although prescription drugs are considered safer than illicit street drugs, NMUPD can lead to detrimental consequences. The aim of the present study was to investigate the relationship between drug use (NMUPD on the one side, illicit street drugs on the other side) with mental health issues and then compare these associations. A representative sample of 5719 young Swiss men aged around 20 years filled in a questionnaire as part of the ongoing baseline Cohort Study on Substance Use Risk Factors (C-SURF). Drug use (16 illicit street drugs and 5 NMUPDs, including sleeping pills, sedatives, pain killers, antidepressants, stimulants) and mental health issues (depression, SF12) were assessed. Simple and multiple linear regressions were employed. In simple regressions, all illicit and prescription drugs were associated with poorer mental health. In multiple regressions, most of the NMUPDs, except for stimulants, were significantly associated with poorer mental health and with depression. On the contrary, the only associations that remained significant between illicit street drugs and mental health involved cannabis. NMUPD is of growing concern not only because of its increasing occurrence, but also because of its association with depression and mental health problems, which is stronger than the association observed between these problems and illicit street drug use, excepted for cannabis. Therefore, NMUPD must be considered in screening for substance use prevention purposes.

"S'il vous plaît docteur, mettez-moi en arrêt"! Certificats médicaux pour raisons psychologiques ["Doctor please, give me a sick leave"!: medical certificates for psychological reasons].

General practitioners are regularly called to evaluate the psychological work capacity of patients. The implicit motivation behind the explicit reason for requesting a sick leave is linked to the subject's history and the way he transfers it in his professional life. An incapacity to work harbours a variety of challenges for the patient, the physician and their relationship. In order to get a better understanding of all the issues at stake, the doctor should understand the significances that represents the work to the patient and the consequences of a sick leave and its associated transference and countertransference issues.

Delay for admission of unstable hospitalized patients to intensive care unit: a need for a medical emergency team?

INTRODUCTION. Patients admitted in Intensive Care Unit (ICU) from general wards are more severe and have a higher mortality than those admitted from emergency department as reported [1]. The majority of them develop signs of instability (e.g. tachypnea, tachycardia, hypotension, decreased oxygen saturation and change in conscious state) several hours before ICU admission. Considering this fact and that in-hospital cardiac arrests and unexpected deaths are usually preceded by warning signs, immediate on site intervention by specialists may be effective. This gave an impulse to medical emergency team (MET) implementation, which has been shown to decrease cardiac arrest, morbidity and mortality in several hospitals. OBJECTIVES AND METHODS. In order to verify if the same was true in our hospital and to determine if there was a need for MET, we prospectively collected all non elective ICU admissions of already hospitalized patients (general wards) and of patients remaining more than 3 h in emergency department (considered hospitalized). Instability criteria leading to MET call correspond to those described in the literature. The delay between the development of one criterion and ICU admission was registered. RESULTS. During an observation period of 12 months, 321 patients with our MET criteria were admitted to ICU. 88 patients came from the emergency department, 115 from the surgical and 113 from the medical ward. 65% were male. The median age was 65 years (range 17-89). The delay fromMETcriteria development to ICU admission was higher than 8 h in 155 patients, with a median delay of 32 h and a range of 8.4 h to 10 days. For the remaining 166 patients, an early MET criterion was present up to 8 h (median delay 3 h) before ICU admission. These results are quite concordant with the data reported in the literature (ref 1-8). 122 patients presented signs of sepsis or septic shock, 70 patients a respiratory failure, 58 patients a cardiac emergency. Cardiac arrest represent 5% of our collective of patients. CONCLUSIONS.Similar to others observations, the majority of hospitalized patients admitted on emergency basis in our ICU have warning signs lasting for several hours. More than half of them were unstable for more than 8 h. This shows there is plenty of time for early acute management by dedicated and specialized team such as MET. However, further studies are required to determine if MET implementation can reduce in-hospital cardiac arrests and influence the morbidity, the length of stay and the mortality.

Learning spatial filters for multispectral image segmentation

We present a novel filtering method for multispectral satellite image classification. The proposed method learns a set of spatial filters that maximize class separability of binary support vector machine (SVM) through a gradient descent approach. Regularization issues are discussed in detail and a Frobenius-norm regularization is proposed to efficiently exclude uninformative filters coefficients. Experiments carried out on multiclass one-against-all classification and target detection show the capabilities of the learned spatial filters.