Hierarchical prediction of registration misalignment using a convolutional LSTM: application to chest CT scans

In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: "correct" 0-3 mm, "poor" 3-6 mm and "wrong" over 6 mm. Rather than a direct prediction, we propose a hierarchical approach, where the prediction is gradually refined from coarse to fine. Our solution is based on a convolutional Long Short-Term Memory (LSTM), using hierarchical misalignment predictions on three resolutions of the image pair, leveraging the intrinsic strengths of an LSTM for this problem. The convolutional LSTM is trained on a set of artificially generated image pairs obtained from artificial displacement vector fields (DVFs). Results on chest CT scans show that incorporating multi-resolution information, and the hierarchical use via an LSTM for this, leads to overall better F1 scores, with fewer misclassifications in a well-tuned registration setup. The final system yields an accuracy of 87.1%, and an average F1 score of 66.4% aggregated in two independent chest CT scan studies.Radiolog

Occupational Health Services Integrated in Primary Health Care in Iran

Introduction A healthy workforce is vital for maintaining social and economic development on a global, national and local level. Around half of the world's people are economically active and spend at least one third of their time in their place of work while only 15 of workers have access to basic occupational health services. According to WHO report, since the early 1980s, health indicators in Iran have consistently improved, to the extent that it is comparable with those in developed countries. In this paper it was tried to briefly describe about Health care system and occupational Health Services as part of Primary Health care in Iran. Methods To describe the health care system in the country and the status of occupational health services to the workers and employers, its integration into Primary Health Care (PHC) and outlining the challenges in provision of occupational health services to the all working population. Findings Iran has fairly good health indicators. More than 85 percent of the population in rural and deprived regions, for instance, have access to primary healthcare services. The PHC centers provide essential healthcare and public-health services for the community. Providing, maintaining and improving of the workers' health are the main goals of occupational health services in Iran that are presented by different approaches and mostly through Workers' Houses in the PHC system. Conclusions Iran has developed an extensive network of PHC facilities with good coverage in most rural areas, but there are still few remote areas that might suffer from inadequate services. It seems that there is still no transparent policy to collaborate with the private sector, train managers or provide a sustainable mechanism for improving the quality of services. Finally, strengthening national policies for health at work, promotion of healthy work and work environment, sharing healthy work practices, developing updated training curricula to improve human resource knowledge including occupational health professionals are recommended. © 2015 The Authors

Fast Learning-based Registration of Sparse 3D Clinical Images

We introduce SparseVM, a method that registers clinical-quality 3D MR scans both faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. However, most registration algorithms are designed for high-resolution research-quality scans. In contrast to research-quality scans, clinical scans are often sparse, missing up to 86% of the slices available in research-quality scans. Existing methods for registering these sparse images are either inaccurate or extremely slow. We present a learning-based registration method, SparseVM, that is more accurate and orders of magnitude faster than the most accurate clinical registration methods. To our knowledge, it is the first method to use deep learning specifically tailored to registering clinical images. We demonstrate our method on a clinically-acquired MRI dataset of stroke patients and on a simulated sparse MRI dataset. Our code is available as part of the VoxelMorph package at http://voxelmorph.mit.edu/.Comment: This version was accepted to CHIL. It builds on the previous version of the paper and includes more experimental result

HDL Particle Subspecies and Their Association With Incident Type 2 Diabetes:The PREVEND Study

Context: High-density lipoproteins (HDL) may be protective against type 2 diabetes (T2D) development, but HDL particles vary in size and function, which could lead to differential associations with incident T2D. A newly developed nuclear magnetic resonance (NMR)derived algorithm provides concentrations for 7 HDL subspecies. Objective: We aimed to investigate the association of HDL particle subspecies with incident T2D in the general population. Methods: Among 4828 subjects of the Prevention of Renal and Vascular End-Stage Disease (PREVEND) study without T2D at baseline, HDL subspecies with increasing size from H1P to H7P were measured by NMR (LP4 algorithm of the Vantera NMR platform). Results: A total of 265 individuals developed T2D (median follow-up of 7.3 years). In Cox regression models, HDL size and H4P (hazard ratio [HR] per 1 SD increase 0.83 [95% CI, 0.690.99] and 0.85 [95% CI, 0.75-0.95], respectively) were inversely associated with incident T2D, after adjustment for relevant covariates. In contrast, levels of H2P were positively associated with incidentT2D (HR 1.15 [95% CI, 1.01-1.32]). In secondary analyses, associations with large HDL particles and H6P were modified by body mass index (BMI) in such a way that they were particularly associated with a lower risk of incident T2D, in subjects with BMI < 30 kg/m(2). Conclusion: Greater HDL size and lower levels of H4P were associated with a lower risk, whereas higher levels of H2P were associated with a higher risk of developing T2D. In addition, large HDL particles and H6P were inversely associated with T2D in nonobese subjects

1360 Differences between peritoneal and pleural mesothelioma in lombardy, italy

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Joint registration and segmentation via multi-task learning for adaptive radiotherapy of prostate cancer

Medical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them simultaneously in a joint manner. In this paper, we formulate registration and segmentation as a joint problem via a Multi-Task Learning (MTL) setting, allowing these tasks to leverage their strengths and mitigate their weaknesses through the sharing of beneficial information. We propose to merge these tasks not only on the loss level, but on the architectural level as well. We studied this approach in the context of adaptive image-guided radiotherapy for prostate cancer, where planning and follow-up CT images as well as their corresponding contours are available for training. At testing time the contours of the follow-up scans are not available, which is a common scenario in adaptive radiotherapy. The study involves two datasets from different manufacturers and institutes. The first dataset was divided into training (12 patients) and validation (6 patients), and was used to optimize and validate the methodology, while the second dataset (14 patients) was used as an independent test set. We carried out an extensive quantitative comparison between the quality of the automatically generated contours from different network architectures as well as loss weighting methods. Moreover, we evaluated the quality of the generated deformation vector field (DVF). We show that MTL algorithms outperform their Single-Task Learning (STL) counterparts and achieve better generalization on the independent test set. The best algorithm achieved a mean surface distance of 1.06 +/- 0.3 mm, 1.27 +/- 0.4 mm, 0.91 +/- 0.4 mm, and 1.76 +/- 0.8 mm on the validation set for the prostate, seminal vesicles, bladder, and rectum, respectively. The high accuracy of the proposed method combined with the fast inference speed, makes it a promising method for automatic re-contouring of follow-up scans for adaptive radiotherapy, potentially reducing treatment related complications and therefore improving patients quality-of-life after treatment. The source code is available at https://github.com/moelmahdy/JRS-MTL.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Sorveglianza sanitaria in agricoltura : l\u2019esperienza del Centro Internazionale per la Salute Rurale dell\u2019Azienda Ospedaliera San Paolo di Milano

OCCUPATIONAL HEALTH SURVEILLANCE IN AGRICULTURE: THE EXPERIENCE OF THE INTERNATIONAL CENTRE FOR RURAL HEALTH. The results of the activities of occupational health surveillance in agriculture carried out by the International Centre for Rural Health since 2008 are described.The activities involve 800 workers employed in 260 farms in the Region of Lombardy (Italy). The types of farms reflect the vocation toward agricultural sector of the Po Valley and the most representative tasks are related to animal care and use of agricultural machinery. Based on the specific risks, workers are provided with preventive and periodic examinations, and complementary laboratory and instrumental evaluations (hearing and respiratory functions, electrocardiography), related to the different risk factors present in the enterprises. The occupational health priorities identified are, for the time being, noise-induced hearing loss and insufficient immunization against tetanus