Schwarzsong6209

Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences. Since breast is sensitive to x-ray radiation, dose reduction of breast CT is an important topic, and for this purpose, few-view scanning is a main approach. In this article, we propose a Deep Efficient End-to-end Reconstruction (DEER) network for few-view breast CT image reconstruction. The major merits of our network include high dose efficiency, excellent image quality, and low model complexity. By the design, the proposed network can learn the reconstruction process with as few as O ( N ) parameters, where N is the side length of an image to be reconstructed, which represents orders of magnitude improvements relative to the state-of-the-art deep-learning-based reconstruction methods that map raw data to tomographic images directly. Also, validated on a cone-beam breast CT dataset prepared by Koning Corporation on a commercial scanner, our method demonstrates a competitive performance over the state-of-the-art reconstruction networks in terms of image quality. The source code of this paper is available at https//github.com/HuidongXie/DEER.Smartphone wound image analysis has recently emerged as a viable way to assess healing progress and provide actionable feedback to patients and caregivers between hospital appointments. Segmentation is a key image analysis step, after which attributes of the wound segment (e.g. wound area and tissue composition) can be analyzed. The Associated Hierarchical Random Field (AHRF) formulates the image segmentation problem as a graph optimization problem. Handcrafted features are extracted, which are then classified using machine learning classifiers. More recently deep learning approaches have emerged and demonstrated superior performance for a wide range of image analysis tasks. FCN, U-Net and DeepLabV3 are Convolutional Neural Networks used for semantic segmentation. While in separate experiments each of these methods have shown promising results, no prior work has comprehensively and systematically compared the approaches on the same large wound image dataset, or more generally compared deep learning vs non-deep learning wound image segmentation approaches. In this paper, we compare the segmentation performance of AHRF and CNN approaches (FCN, U-Net, DeepLabV3) using various metrics including segmentation accuracy (dice score), inference time, amount of training data required and performance on diverse wound sizes and tissue types. Improvements possible using various image pre- and post-processing techniques are also explored. As access to adequate medical images/data is a common constraint, we explore the sensitivity of the approaches to the size of the wound dataset. We found that for small datasets ( 300 images), AHRF saturates quickly, and all CNN approaches (FCN, U-Net and DeepLabV3) are significantly more accurate than AHRF.Background After a slow start due to an effective lockdown, the coronavirus disease 2019 (COVID-19) pandemic in India has been raging at a rapid pace, posing a formidable challenge to the healthcare system in the country. The personal protective equipment (PPE) undoubtedly provides a shield of protection for the healthcare workers (HCWs) fighting the disease as a valuable asset to the nation. However, there have been various problems associated with the PPE, ranging from its shortage to problems arising from heat, dehydration, etc while wearing them. There is a need to assess these problems faced by HCWs both qualitatively and quantitatively for their timely and effective redressal. Methods An electronic questionnaire survey was conducted among a cohort of HCWs who had performed COVID-19 duties and used PPE kits. The cohort consisted of different categories of doctors, nursing personnel, and other paramedical staff. Results The most common problems associated with using PPE kits was excessive sweating (100%), fogging of goggles, spectacles, or face shields (88%), suffocation (83%), breathlessness (61%), fatigue (75%), headache due to prolonged use (28%), and pressure marks on the skin at one or more areas on repeated use (19%). Occasional problems reported were skin allergy/dermatitis caused by the synthetic material of the PPE kit, face shield impinging onto the neck during intubation, and nasal pain, pain at the root of the pinna, and slipperiness of shoe covers. Various ways and means have been employed by the HCWs to actively address and solve these problems. Selleck JAK inhibitor Conclusion These plausible solutions will definitely help the HCWs to deal with and solve the problems arising out of the PPE use.Background Intranasal corticosteroids (INCSs) are the first-line treatment for patients with moderate to severe conditions of allergic rhinitis (AR) as per current guidelines. However, patients' knowledge and practice towards the safety of such medications remains ambiguous. Therefore, this study was undertaken to identify the awareness of and knowledge about the safety of nasal corticosteroid usage in patients with allergic rhinitis as well as their adherence to taking the medication. Materials and methods We conducted a cross-sectional study from June to September 2020 at Imam Mohammad Ibn Saud Islamic University Medical Center, Riyadh, Kingdom of Saudi Arabia. Data were collected through questionnaire-based surveys, and a total of 375 patients were enrolled in the study. The eligibility criteria included all adult patients diagnosed with allergic rhinitis. Results Most of the patients had used intranasal corticosteroids. However, only two-fifths of patients stated these medications were effective and only and adherence to the treatment plan. Corrective measures are needed to ensure better health outcomes.Introduction Multiligament knee injuries are uncommon but serious injuries. There is ongoing debate on the optimal treatment of these injuries. We designed a study to establish the effects of repair or reconstruction on proprioceptive outcomes following multiligament injury to the knee. Materials and Methods A total of 34 patients were analysed by independent researchers who had no conflict of interest in the cases (23 in the repair group and 11 in the reconstruction group). Proprioception of the knee was measured using a previously validated tool to assess the reproduction of passive positioning. Functional outcome was measured using the Lysholm score. Sub-group analysis was performed. The mean time from injury to review was 83 months (range 25-193 months). Results There were no significant differences in proprioceptive acuity between the injured (5.9±4.2°; range 1.0-18.3°) and uninjured contralateral (control) knees (5.2±3.8°; range 1.0-15.0°; p=0.35). Similarly, there was no significant difference in proprioceptive acuity identified between the injured knees that underwent repair (6.