Simplification of neural networks for skin lesion image segmentation using color channel pruning.

Automatic analysis of skin abnormality is an effective way for medical experts to facilitate diagnosis procedures and improve their capabilities. Efficient and accurate methods for analysis of the skin abnormalities such as convolutional neural networks (CNNs) are typically complex. Hence, the implementation of such complex structures in portable medical instruments is not feasible due to power and resource limitations. CNNs can extract features from the skin abnormality images automatically. To reduce the burden of the network for feature extraction, which can lead to the network simplicity, proper input color channels could be selected. In this paper, a pruning framework is proposed to simplify these complex structures through the selection of most informative color channels and simplification of the network. Moreover, hardware requirements of different network structures are identified to analyze the complexity of different networks. Experimental results are conducted for segmentation of images from two publicly available datasets of both dermoscopy and non-dermoscopy images. Simulation results show that using the proposed color channel selection method, simple and efficient neural network structures can be applied for segmentation of skin abnormalities.

COVID-19 and intracerebral haemorrhage: causative or coincidental?

Pneumonia appears to be the most common manifestation of coronavirus disease 2019 (COVID-19), but some extrapulmonary involvement, such as gastrointestinal, cardiac and renal, has been reported. The limited clinical data about the virus's behavior to date, especially extrapulmonary symptoms, suggest that we should be aware of the possibility of initial cerebrovascular manifestations of COVID-19.

Effect of 6% hydroxyethyl starch 130/0.4 on kidney and haemostatic function in cardiac surgical patients: a randomised controlled trial.

Whether third-generation hydroxyethyl starch solutions provoke kidney injury or haemostatic abnormalities in patients having cardiac surgery remains unclear. We tested the hypotheses that intra-operative administration of a third-generation starch does not worsen postoperative kidney function or haemostasis in cardiac surgical patients compared with human albumin 5%. This triple-blind, non-inferiority, clinical trial randomly allocated patients aged 40-85 who underwent elective aortic valve replacement, with or without coronary artery bypass grafting, to plasma volume replacement with 6% starch 130/0.4 vs. 5% human albumin. Our primary outcome was postoperative urinary neutrophil gelatinase-associated lipocalin concentrations, a sensitive and early marker of postoperative kidney injury. Secondarily, we evaluated urinary interleukin-18; acute kidney injury using creatinine RIFLE criteria, coagulation measures, platelet count and function. Non-inferiority (delta 15%) was assessed with correction for multiple comparisons. We enrolled 141 patients (69 starch, 72 albumin) as planned. Results of the primary analysis demonstrated that postoperative urine neutrophil gelatinase-associated lipocalin (median (IQR [range])) was slightly lower with hydroxyethyl starch (5 (1-68 [0-996]) ng.ml-1 ) vs. albumin (5 (2-74 [0-1604]) ng.ml-1 ), although not non-inferior [ratio of geometric means (95%CI) 0.91 (0.57, 1.44); p = 0.15] due to higher than expected variability. Urine interleukin-18 concentrations were reduced, but interleukin-18 and kidney injury were again not non-inferior. Of 11 individual coagulation measures, platelet count and function, nine were non-inferior to albumin. Two remaining measures, thromboelastographic R value and arachidonic acid-induced platelet aggregation, were clinically similar but with wide confidence intervals. Starch administration during cardiac surgery produced similar observed effects on postoperative kidney function, coagulation, platelet count and platelet function compared with albumin, though greater than expected variability and wide confidence intervals precluded the conclusion of non-inferiority. Long-term mortality and kidney function appeared similar between starch and albumin.

Measurability of D-concurrence.

An effective approach to quantify entanglement of any bipartite systems is D-concurrence, which is important in quantum information science. In this paper, we present a direct method for experimental determination of the D-concurrence of an arbitrary bipartite pure state. To do this, we show that measurement of the D-concurrence of bipartite pure state can be conversed into the measurement performed on some observables so called generalized Gell-Mann operators. We first introduce the concept of D-concurrence for a bipartite system. Then we explain the method of measuring this entanglement measure for the pure state. Finally, for clarify of the subject, we give an example consisting of two parties A and B with dimensions 3.

Electrophoretic deposition of chitosan reinforced graphene oxide-hydroxyapatite on the anodized titanium to improve biological and electrochemical characteristics.

Chitosan reinforced hydroxyapatite-graphene oxide (CS-GO-HA) nanocomposite coatings were developed using electrophoretic deposition process in order to improve the biological and electrochemical properties of Ti surface. Moreover, the role of anodized layer on the physical and electrochemical properties of the CS-GO-HA nanocomposite coating was evaluated. After synthesize of HA-GO nanopowder using a sol-gel process, nanocomposite coatings with various concentrations of chitosan (0.5, 1 and 1.5 mg/ml) were produced. Increasing the chitosan content lowered the deposition rate of HA-GO nanoparticles, reduced the coating thickness and diminished apatite-formation ability and biocompatibility. Noticeably, MG63 cell viability significantly reduced form 119.3 ±â€¯5.1 (% control) to 51.9 ±â€¯14.8 (% control), when the chitosan concentration increased from 0.5 to 1.5 mg/ml. In addition, the CS-GO-HA coating containing 0.5 mg/ml chitosan revealed the best barrier property owing to the less crack formation. Furthermore, anodizing of titanium substrate and formation of TiO2 nanotube (TiNT) resulted in the formation of crack-free and homogeneous CS-GO-HA coatings without any observable defect. Moreover, the TiNT formation noticeably improved barrier resistance of the coating (6.7 times) due to better adhesion governed between coating and substrate. Our results confirmed that the surface modification using both anodizing of Ti substrate and electrophoretic deposition of ternary CS-GO-HA nanocomposite coating with 0.5 mg/ml chitosan successfully improves electrochemical properties, bioactivity and cell function, which makes it promising for bone implant applications.

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

Vessel extraction in X-ray angiograms using deep learning.

Coronary artery disease (CAD) is the most common type of heart disease which is the leading cause of death all over the world. X-ray angiography is currently the gold standard imaging technique for CAD diagnosis. These images usually suffer from low quality and presence of noise. Therefore, vessel enhancement and vessel segmentation play important roles in CAD diagnosis. In this paper a deep learning approach using convolutional neural networks (CNN) is proposed for detecting vessel regions in angiography images. Initially, an input angiogram is preprocessed to enhance its contrast. Afterward, the image is evaluated using patches of pixels and the network determines the vessel and background regions. A set of 1,040,000 patches is used in order to train the deep CNN. Experimental results on angiography images of a dataset show that our proposed method has a superior performance in extraction of vessel regions.

Robust watermarking in non-ROI of medical images based on DCT-DWT.

Increasing demand and utilization of telemedicine require transmission of medical information and images over internet. Since authenticity of received images is crucial and patient's information should be included with minimum changes in images, robust watermarking schemes are needed. In this paper, we propose a robust watermark method that embeds patient's information outside the region of interest (ROI) in medical image. In order to find appropriate regions for embedding, we use saliency as a means of measuring importance of regions and find blocks having minimum overlap with the ROI. The algorithm employs wavelet transform and also discrete cosine transform (DCT) domains in the embedding stage and redundantly embeds watermark to increase robustness against possible alterations. Moreover, voting is utilized in the extraction phase. Experimental results show the efficiency of the proposed method and better results are obtained as compared to comparable methods with same size of the watermarked data.

Automatic detection of melanoma using broad extraction of features from digital images.

Automatic and reliable diagnosis of skin cancer, as a smartphone application, is of great interest. Among different types of skin cancers, melanoma is the most dangerous one which causes most deaths. Meanwhile, melanoma is curable if it were diagnosed in its early stages. In this paper we propose an efficient system for prescreening of pigmented skin lesions for malignancy using general-purpose digital cameras. These images can be captured by a smartphone or a digital camera. This could be beneficial in different applications, such as computer aided diagnosis and telemedicine applications. It could assist dermatologists, or smartphone users, evaluate risk of suspicious moles. The proposed method enhances borders and extracts a broad set of dermatologically important features. These discriminative features allow classification of lesions into two groups of melanoma and benign. This method is computationally appropriate as a smartphone application. Experimental results show that our proposed method is superior in diagnosis accuracy compared to state-of-the-art methods.

Melanoma detection by analysis of clinical images using convolutional neural network.

Melanoma, most threatening type of skin cancer, is on the rise. In this paper an implementation of a deep-learning system on a computer server, equipped with graphic processing unit (GPU), is proposed for detection of melanoma lesions. Clinical (non-dermoscopic) images are used in the proposed system, which could assist a dermatologist in early diagnosis of this type of skin cancer. In the proposed system, input clinical images, which could contain illumination and noise effects, are preprocessed in order to reduce such artifacts. Afterward, the enhanced images are fed to a pre-trained convolutional neural network (CNN) which is a member of deep learning models. The CNN classifier, which is trained by large number of training samples, distinguishes between melanoma and benign cases. Experimental results show that the proposed method is superior in terms of diagnostic accuracy in comparison with the state-of-the-art methods.

Hardware image assessment for wireless endoscopy capsules.

Wireless capsule endoscopy is a new technology in the realm of telemedicine that has many advantages over the traditional endoscopy systems. Transmitted images should help diagnosis of diseases of the gastrointestinal tract. Two important technical challenges for the manufacturers of these capsules are power consumption and size of the circuitry. Also, the system must be fast enough for real-time processing of image or video data. To solve this problem, many hardware designs have been proposed for implementation of the image processing unit. In this paper we propose an architecture that could be used for the assessment of endoscopy images. The assessment allows avoidance of transmission of medically useless images. Hence, volume of data is reduced for more efficient transmission of images by the endoscopy capsule. This is done by color space conversion and moment calculation of images captured by the capsule. The inputs of the proposed architecture are RGB image frames and the outputs are images with converted colors and calculated image moments. Experimental results indicate that the proposed architecture has low complexity and is appropriate for a real-time application.

Cooccurrence of Multiple Sclerosis and Idiopathic Basal Ganglia Calcification.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and neurodegenerative disease of central nervous system that affects both white and gray matter. Idiopathic calcification of the basal ganglia is a rare neurodegenerative disorder of unknown cause that is characterized by sporadic or familial brain calcification. Concurrence of multiple sclerosis (MS) and idiopathic basal ganglia calcification (Fahr's disease) is very rare event. In this study, we describe a cooccurrence of idiopathic basal ganglia calcification with multiple sclerosis. The association between this disease and MS is unclear and also maybe probably coincidental.

Mutation analysis of the CYP21A2 gene in congenital adrenal hyperplasia.

Congenital adrenal hyperplasia (CAH) is an inherited autosomal recessive enzymatic disorder involving the synthesis of adrenal corticosteroids. 21-Hydroxylase deficiency (21-OHD) is the most common form of the disease which is observed in more than 90% of patients with CAH. Early identification of mutations in the genes involved in this disease is critical. A marker of the disease, errors in the CYP21A2 gene, is thought to be part of the pathophysiology of CAH. Therefore, the identification of gene mutations would be very beneficial in the early detection of CAH. This research was a descriptive epidemiological study conducted on individuals elected by the inclusion criteria whom were referred to the Genetic Diagnosis Center of Tabriz during 2012 to 2013. After sampling and DNA extraction, PCR for the detection of mutations in the CYP21A2 gene was performed followed by sequencing. For data analysis, the results of sequencing were compared with the reference gene by blast, Gene Runner and MEGA-5 software. Obtained changes were compared with NCBI databases. The analysis of the sequencing determined the mutations located in Exons 6, 7, 8 and 10. The most frequent findings were Q318X (53%) and R356W (28%). Exon 6 cluster (7%), E431k (4%), V237E (2%), V281L (2%), E351K (2%), R426C (2%) were also frequent in our patients. The most frequent genotype was compound heterozygote, Q318X/R356W. Three rare mutations in our study were E431K, E351K and R426C. Observed mutation frequencies in this study were much higher than those reported in previous studies in Iranian populations. Thus, it seems that it is necessary to follow-up screening programs and use sequencing methods to better identify mutations in the development of the disease.

Region of interest extraction for lossless compression of bone X-ray images.

For few decades digital X-ray imaging has been one of the most important tools for medical diagnosis. With the advent of distance medicine and the use of big data in this respect, the need for efficient storage and online transmission of these images is becoming an essential feature. Limited storage space and limited transmission bandwidth are the main challenges. Efficient image compression methods are lossy while the information of medical images should be preserved with no change. Hence, lossless compression methods are necessary for this purpose. In this paper, a novel method has been proposed to eliminate the non-ROI data from bone X-ray images. Background pixels do not contain any valuable medical information. The proposed method is based on the histogram dispersion method. ROI is separated from the background and it is compressed with a lossless compression method to preserve medical information of the image. Compression ratios of the implemented results show that the proposed algorithm is capable of effective reduction of the statistical and spatial redundancies.

Adaptive stereo medical image watermarking using non-corresponding blocks.

Today with the advent of technology in different medical imaging fields, the use of stereoscopic images has increased. Furthermore, with the rapid growth in telemedicine for remote diagnosis, treatment, and surgery, there is a need for watermarking. This is for copyright protection and tracking of digital media. Also, the efficient use of bandwidth for transmission of such data is another concern. In this paper an adaptive watermarking scheme is proposed that considers human visual system in depth perception. Our proposed scheme modifies maximum singular values of wavelet coefficients of stereo pair for embedding watermark bits. Experimental results show high 3D visual quality of watermarked video frames. Moreover, comparison with a compatible state of the art method shows that the proposed method is highly robust against attacks such as AWGN, salt and pepper noise, and JPEG compression.

Robust catheter identification and tracking in X-ray angiographic sequences.

Coronary artery disease (CAD) is one of the major causes of death worldwide. Today X-ray angiography is a standard method for CAD diagnosis. Usually, the quality of these images is not good enough. Noise, camera and heart motions, non-uniform illumination and even the presence of catheter are sources of quality degradation. The existence of catheter can produce difficulties in vessel extraction methods because catheter is structurally similar to arteries. In this paper we propose a fully automatic method for catheter detection and tracking during the whole angiography sequence. In this method with a vesselness map, we smooth each frame using guided filter. The catheter is detected in the first frame using Hough transform. We then fit a second order polynomial on the catheter and accurately track it throughout the sequence. Our method is tested on 25 X-ray angiography sequences where a precision of 0.9597 is achieved.

Effects of hydrostatic pressure on mouse sperm.

OBJECTIVES: The objective of this study was to investigate the abnormalities in sperm after exposure to hydrostatic pressure. BACKGROUND: Hydrostatic pressure acting on the cells is one of the fundamental environmental mechanical forces. Disorders of relationship between the cells and this mechanical force, such as when pressure varies beyond physiological limits, can lead to disease or pathological states. Sperm exposed to different range of hydrostatic pressure within male reproductive system and after entering the female reproductive system. METHODS: Sexually mature male NMRI mice, 8-12 weeks-old were sperm donors. Sperms were separated from the caudal epididymis and maintained in Ham's F-10 culture medium supplemented with 10 % FBS and divided into control and treatments. Sperm suspensions in the treatments were placed within pressure chamber and were subjected to increased hydrostatic pressure of 25, 50 and 100 mmHg (treatment I, II and III) above atmospheric pressure for 2 and 4 h. Sperm viability, motility, morphology, DNA integrity and fertilizing ability were assessed and compared with control. RESULTS: Results showed that hydrostatic pressure dependent on ranges and time manner reduced sperm quality due to adverse effect on viability, motility , morphology, DNA integrity and fertilizing ability in all of treatments, especially after 4h (p<0.05). CONCLUSION: Our data revealed hydrostatic pressure reduces sperm quality as a consequence of adverse effects on sperm parameters and may cause male infertility or subfertility (Tab. 5, Ref. 5).

Talking about STD/HIV prevention: a look at communication online.

Little is known about the quality and usability of online health information. This analysis evaluated STD prevention websites' content quality and usability. Thirty-six sites were analyzed to determine their adherence to established sexual health guidelines and their accessibility, usability, credibility and currency. The objective of this study was to determine what communication skills are available to teenagers through STD prevention websites. Only two (6%) addressed safe sex negotiation; seven (19%) addressed basic communication skills; and slightly more than half (53%) addressed at least one sexual decision-making message. Most sites displayed consistent template design and two-thirds had working hyperlinks. But few (19%) of the sites offered a site map, while only one-third (36%) provided an internal search engine. These results indicate that health educators need to include tips on how to negotiate safe sex and improve interactive design features when creating STD websites for teenagers.

Vitrification enhancement by synthetic ice blocking agents.

Small concentrations of the synthetic polymer polyvinyl alcohol (PVA) were found to inhibit formation of ice in water/cryoprotectant solutions. Ice inhibition improved with decreasing molecular weight. A PVA copolymer of molecular weight 2 kDa consisting of 20% vinyl acetate was found to be particularly effective. PVA copolymer concentrations of 0.001, 0.01, 0.1, and 1% w/w decreased the concentration of glycerol required to vitrify in a 10-ml volume by 1, 3, 4, and 5% w/w, respectively. Dimethyl sulfoxide concentrations required for vitrification were also reduced by 1, 2, 2, and 3% w/w, respectively. Crystallization of ice on borosilicate glass in contact with cryoprotectant solutions was inhibited by only 1 ppm of PVA copolymer. Devitrification of ethylene glycol solutions was also strongly inhibited by PVA copolymer. Visual observation and differential scanning calorimeter data suggest that PVA blocks ice primarily by inhibition of heterogeneous nucleation. PVA thus appears to preferentially bind and inactivate heterogeneous nucleators and/or nascent ice crystals in a manner similar to that of natural antifreeze proteins found in cold-hardy fish and insects. Synthetic PVA-derived ice blocking agents can be produced much less expensively than antifreeze proteins, offering new opportunities for improving cryopreservation by vitrification.