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Background: Plantar fasciitis described as the inflammation of planter fascia, particularly hampering an individual’s daily living activities related to ankle and foot that impact on quality of life. The study aimed to identify the association between BMI and planter fasciitis among hospital staff in a selected hospital in Dhaka city. Methods: A descriptive cross-sectional study. Results: Mean±SD age of the respondents were 29.03±6.56 years. Among the hospital staff, the majority of the respondents were nurses 31.25%, 13.02% respondents were physician and 2.07% of respondents were physiotherapists, others were pharmacist, lab technician, administration staff, cleaner, security, information technologists and officers from the maintenance department of selected hospital. According to BMI scale the 65% were normal weight, 23% were overweight and only 12% were obese. In this study respondents who have only two family members, have strong relationships with BMI (p

The article aims to assess the governance of the social safety net programme in Bangladesh. The data were collected from the beneficiaries and stakeholders of the employment generation programme for the poorest scheme of the government of Bangladesh. A total of 150 respondents were selected randomly, and data and information were collected using a structured questionnaire for interviews and a guideline for focus group discussions. The analysis shows that beneficiaries and stakeholders of the programme are mostly satisfied with the quality and timeliness of work, beneficiaries selection and benefits distribution process, but many respondents expressed their dissatisfaction with monitoring and wage rates. Furthermore, the research reveals several factors that contribute to establishing poor governance, such as weak monitoring, absenteeism, proxy work and inappropriate project selection. The finding suggests that it is necessary to make local-level institutions fully operational, determine district-wise wage rates and enhance the monitoring process to achieve the programme objectives.

We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and much of it not being annotated. Lack of annotated data is a significant problem in machine learning, and Self-Supervised Learning (SSL) methods are typically used to deal with limited labelled data. However, there is a lack of studies that use SSL methods to exploit inter-omics relationships on unlabelled multi-omics data. In this work, we develop a novel and efficient pre-training paradigm that consists of various SSL components, including but not limited to contrastive alignment, data recovery from corrupted samples, and using one type of omics data to recover other omic types. Our pre-training paradigm improves performance on downstream tasks with limited labelled data. We show that our approach outperforms the state-of-the-art method in cancer type classification on the TCGA pan-cancer dataset in semi-supervised setting. Moreover, we show that the encoders that are pre-trained using our approach can be used as powerful feature extractors even without fine-tuning. Our ablation study shows that the method is not overly dependent on any pretext task component. The network architectures in our approach are designed to handle missing omic types and multiple datasets for pre-training and downstream training. Our pre-training paradigm can be extended to perform zero-shot classification of rare cancers., Comment: Preprint of an article published in Pacific Symposium on Biocomputing $\copyright$ 2022 World Scientific Publishing Co., Singapore, http://psb.stanford.edu/

Recent literature on Islam and the digital covers a wide range of topics and themes; however, what is yet to be developed from an Islamic perspective is a broader philosophical framework that accounts for the nature, exigencies and affordances of contemporary digital technologies. In advance of such a framework, this article is an attempt to open the way to philosophical engagement with issues of digital ethics from an Islamic perspective. After a brief review of recent literature on Islam and the digital and a significantly earlier work by Ziauddin Sardar, in which he proposed an information strategy for the Muslim world, this paper provides some background to Islamic ethics and the wide field of ethical theories in the Islamic tradition. This paper then proceeds by identifying and outlining examples of contemporary themes in Islam and the digital which conceal underlying philosophical and theological issues. Finally, this paper considers the significant scale and scope of the transformations involved in the ongoing transposition to the ‘onlife’ and highlights several areas where Islamic perspectives may be seen to converge or diverge with other strands of scholarship on digital ethics.

Nuclei segmentation and classification is the first and most crucial step that is utilized for many different microscopy medical analysis applications. However, it suffers from many issues such as the segmentation of small objects, imbalance, and fine-grained differences between types of nuclei. In this paper, multiple different contributions were done tackling these problems present. Firstly, the recently released "ConvNeXt" was used as the encoder for HoVer-Net model since it leverages the key components of transformers that make them perform well. Secondly, to enhance the visual differences between nuclei, a multi-channel color space-based approach is used to aid the model in extracting distinguishing features. Thirdly, Unified Focal loss (UFL) was used to tackle the background-foreground imbalance. Finally, Sharpness-Aware Minimization (SAM) was used to ensure generalizability of the model. Overall, we were able to outperform the current state-of-the-art (SOTA), HoVer-Net, on the preliminary test set of the CoNiC Challenge 2022 by 12.489% mPQ+., Comment: 3 pages, 1 figure, and 1 table

The aims of this work were to create a robust automatic software tool for measurement of the levator hiatal area on transperineal ultrasound (TPUS) volumes and to measure the potential reduction in variability and time taken for analysis in a clinical setting. The proposed tool automatically detects the C-plane (i.e., the plane of minimal hiatal dimensions) from a 3-D TPUS volume and subsequently uses the extracted plane to automatically segment the levator hiatus, using a convolutional neural network. The automatic pipeline was tested using 73 representative TPUS volumes. Reference hiatal outlines were obtained manually by two experts and compared with the pipeline's automated outlines. The Hausdorff distance, area, a clinical quality score, C-plane angle and C-plane Euclidean distance were used to evaluate C-plane detection and quantify levator hiatus segmentation accuracy. A visual Turing test was created to compare the performance of the software with that of the expert, based on the visual assessment of C-plane and hiatal segmentation quality. The overall time taken to extract the hiatal area with both measurement methods (i.e., manual and automatic) was measured. Each metric was calculated both for computer-observer differences and for inter-and intra-observer differences. The automatic method gave results similar to those of the expert when determining the hiatal outline from a TPUS volume. Indeed, the hiatal area measured by the algorithm and by an expert were within the intra-observer variability. Similarly, the method identified the C-plane with an accuracy of 5.76 ± 5.06° and 6.46 ± 5.18 mm in comparison to the inter-observer variability of 9.39 ± 6.21° and 8.48 ± 6.62 mm. The visual Turing test suggested that the automatic method identified the C-plane position within the TPUS volume visually as well as the expert. The average time taken to identify the C-plane and segment the hiatal area manually was 2 min and 35 ± 17 s, compared with 35 ± 4 s for the automatic result. This study presents a method for automatically measuring the levator hiatal area using artificial intelligence-based methodologies whereby the C-plane within a TPUS volume is detected and subsequently traced for the levator hiatal outline. The proposed solution was determined to be accurate, relatively quick, robust and reliable and, importantly, to reduce time and expertise required for pelvic floor disorder assessment. ispartof: ULTRASOUND IN MEDICINE AND BIOLOGY vol:47 issue:12 pages:3470-3479 ispartof: location:England status: published

Within clinical practise, a shift in the distribution of data collected over time is commonly observed. This occurs generally due to deliberate changes in acquisition hardware but also through natural, unforeseen shifts in the hardware’s physical properties like scanner SNR and gradient non-linearities. These domain shifts thus may not be known a-priori, but may cause significant degradation in the diagnostic performance of machine learning models. A deployed diagnostic system must therefore be robust to such unpredictable and continuous domain shifts. However, given the infrastructure and resource constraints pervasive in clinical settings in developing countries, such robustness must be achieved under finite memory and data privacy constraints. In this work, we propose a domain-incremental learning approach that leverages vector quantization to efficiently store and replay hidden representations under limited memory constraints. Our proposed approach validated on well known large-scale public Chest X-ray datasets achieves significant reduction in catastrophic forgetting over previous approaches in medical imaging, while requiring no prior knowledge of domain shift boundaries and a constrained memory. Finally, we also formulate a more natural continual learning setting for medical imaging using a tapered uniform distribution schedule with gradual interleaved domain shifts.

Automatic Gestational Age (GA) estimation based on the Crown Rump Length (CRL) measurement is the preferred solution to overcome the challenges while using the last menstrual period (LMP) to date pregnancies. However, GA estimation based on CRL requires accurate placement of calipers on the fetal crown and rump which is not always a straightforward task, especially for an inexperienced sonographer. This paper proposes an accurate GA estimation method from fetal CRL images during the first trimester scan. The method addresses this problem by segmenting the fetus using a binary and multi-class U-Net. The fetal segmentation is used to compute the CRL. This is then followed by an estimation of GA from the automatic CRL measurement based of clinical information. The results from the multi-class segmentation achieves a more accurate precision, recall, Dice, and Jaccard. This has also led to a more accurate CRL measurement and hence more robust GA estimation.

Purpose: We present an original method for simulating realistic fetal neurosonography images specifically generating third-trimester pregnancy ultrasound images from second-trimester images. Our method was developed using unpaired data, as pairwise data were not available. We also report original insights on the general appearance differences between second- and third-trimester fetal head transventricular (TV) plane images. Approach: We design a cycle-consistent adversarial network (Cycle-GAN) to simulate visually realistic third-trimester images from unpaired second- and third-trimester ultrasound images. Simulation realism is evaluated qualitatively by experienced sonographers who blindly graded real and simulated images. A quantitative evaluation is also performed whereby a validated deep-learning-based image recognition algorithm (ScanNav®) acts as the expert reference to allow hundreds of real and simulated images to be automatically analyzed and compared efficiently. Results: Qualitative evaluation shows that the human expert cannot tell the difference between real and simulated third-trimester scan images. 84.2% of the simulated third-trimester images could not be distinguished from the real third-trimester images. As a quantitative baseline, on 3000 images, the visibility drop of the choroid, CSP, and mid-line falx between real second- and real third-trimester scans was computed by ScanNav® and found to be 72.5%, 61.5%, and 67%, respectively. The visibility drop of the same structures between real second-trimester and simulated third-trimester was found to be 77.5%, 57.7%, and 56.2%, respectively. Therefore, the real and simulated third-trimester images were consider to be visually similar to each other. Our evaluation also shows that the third-trimester simulation of a conventional GAN is much easier to distinguish, and the visibility drop of the structures is smaller than our proposed method. Conclusions: The results confirm that it is possible to simulate realistic third-trimester images from second-trimester images using a modified Cycle-GAN, which may be useful for deep learning researchers with a restricted availability of third-trimester scans but with access to ample second trimester images. We also show convincing simulation improvements, both qualitatively and quantitatively, using the Cycle-GAN method compared with a conventional GAN. Finally, the use of a machine learning-based reference (in the case ScanNav®) for large-scale quantitative image analysis evaluation is also a first to our knowledge.

Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81\(^{\circ }\) and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment.

Flower classification is a challenging task due to the wide range of flower species, which have a similar shape, appearance or surrounding objects such as leaves and grass. In this study, the authors propose a novel two-step deep learning classifier to distinguish flowers of a wide range of species. First, the flower region is automatically segmented to allow localisation of the minimum bounding box around it. The proposed flower segmentation approach is modelled as a binary classifier in a fully convolutional network framework. Second, they build a robust convolutional neural network classifier to distinguish the different flower types. They propose novel steps during the training stage to ensure robust, accurate and real-time classification. They evaluate their method on three well known flower datasets. Their classification results exceed 97% on all datasets, which are better than the state-of-the-art in this domain.

During routine ultrasound assessment of the fetal brain for biometry estimation and detection of fetal abnormalities, accurate imaging planes must be found by sonologists following a well-defined imaging protocol or clinical standard, which can be difficult for non-experts to do well. This assessment helps provide accurate biometry estimation and the detection of possible brain abnormalities. We describe a machine-learning method to assess automatically that transventricular ultrasound images of the fetal brain have been correctly acquired and meet the required clinical standard. We propose a deep learning solution, which breaks the problem down into three stages: (i) accurate localization of the fetal brain, (ii) detection of regions that contain structures of interest and (iii) learning the acoustic patterns in the regions that enable plane verification. We evaluate the developed methodology on a large real-world clinical data set of 2-D mid-gestation fetal images. We show that the automatic verification method approaches human expert assessment.

The first trimester fetal ultrasound scan is important to confirm fetal viability, to estimate the gestational age of the fetus, and to detect fetal anomalies early in pregnancy. First trimester ultrasound images have a different appearance than for the second trimester scan, reflecting the different stage of fetal development. There is limited literature on automation of image-based assessment for this earlier trimester, and most of the literature is focused on one specific fetal anatomy. In this paper, we consider automation to support first trimester fetal assessment of multiple fetal anatomies including both visualization and the measurements from a single 3D ultrasound scan. We present a deep learning and image processing solution (i) to perform semantic segmentation of the whole fetus, (ii) to estimate plane orientation for standard biometry views, (iii) to localize and automatically estimate biometry, and (iv) to detect fetal limbs from a 3D first trimester volume. Computational analysis methods were built using a real-world dataset (n = 44 volumes). An evaluation on a further independent clinical dataset (n = 21 volumes) showed that the automated methods approached human expert assessment of a 3D volume.

Type 2 diabetes mellitus (T2DM) is a polygenic metabolic disease described by hyperglycemia, which is caused by insulin resistance or reduced insulin secretion. Interaction between various genetic variants and environmental factors triggers T2DM. The main aim of this study was to find the risk associated with genetic variant (rs5210) of KCNJ11gene in the development of T2D in Indian Population. A total number of 300 cases of T2D and 100 control samples were studied to find the polymorphism in KCNJ11 through PCR-RFLP. The genotype and allele frequencies in T2DM cases were significantly different from the control population. We found a significant association of KCNJ11 (rs5210) gene polymorphism with T2DM in North Indian patients indicating the role of this variant in developing risk for T2DM.

Objective To create prescriptive growth standards for five fetal brain structures, measured using ultrasound, in healthy, well-nourished women at low risk of impaired fetal growth and poor perinatal outcome, taking part in the Fetal Growth Longitudinal Study (FGLS) of the INTERGROWTH-21st Project. Methods This was a complementary analysis of a large, population-based, multicenter, longitudinal study. The sample analyzed was selected randomly from the overall FGLS population, ensuring an equal distribution among the eight diverse participating sites and of three-dimensional (3D) ultrasound volumes across pregnancy (range: 15-36 weeks' gestation). We measured, in planes reconstructed from 3D ultrasound volumes of the fetal head at different timepoints in pregnancy, the size of the parieto-occipital fissure (POF), Sylvian fissure (SF), anterior horn of the lateral ventricle, atrium of the posterior horn of the lateral ventricle (PV) and cisterna magna (CM). Fractional polynomials were used to construct the standards. Growth and development of the infants were assessed at 1 and 2 years of age to confirm their adequacy for constructing international standards. Results From the entire FGLS cohort of 4321 women, 451 (10.4%) were selected at random. After exclusions, 3D ultrasound volumes from 442 fetuses born without a congenital malformation were used to create the charts. The fetal brain structures of interest were identified in 90% of cases. All structures, except the PV, showed increasing size with gestational age, and the size of the POF, SF, PV and CM showed increasing variability. The 3rd , 5th , 50th , 95th and 97th smoothed centiles are presented. The 5th centiles for the POF and SF were 3.1 mm and 4.7 mm at 22 weeks' gestation and 4.6 mm and 9.9 mm at 32 weeks, respectively. The 95th centiles for the PV and CM were 8.5 mm and 7.5 mm at 22 weeks and 8.6 mm and 9.5 mm at 32 weeks, respectively. Conclusions We have produced prescriptive size standards for fetal brain structures based on prospectively enrolled pregnancies at low risk of abnormal outcome. We recommend these as international standards for the assessment of measurements obtained using ultrasound from fetal brain structures. © 2020 Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Giant kelp (Macrocystis pyrifera) is the most widely distributed kelp species on the planet, constituting one of the richest and most productive ecosystems on Earth, but detailed information on its distribution is entirely missing in some marine ecoregions, especially in the high latitudes of the Southern Hemisphere. Here, we present an algorithm based on a series of filter thresholds to detect giant kelp employing Sentinel-2 imagery. Given the overlap between the reflectances of giant kelp and intertidal green algae (Ulvophyceae), the latter are also detected on shallow rocky intertidal areas. The kelp filter algorithm was applied separately to vegetation indices, the Floating Algae Index (FAI), the Normalised Difference Vegetation Index (NDVI), and a novel formula (the Kelp Difference, KD). Training data from previously surveyed kelp forests and other coastal and ocean features were used to identify reflectance threshold values. This procedure was validated with independent field data collected with UAV imagery at a high spatial resolution and point-georeferenced sites at a low spatial resolution. When comparing UAV with Sentinel data (high-resolution validation), an average overall accuracy ≥ 0.88 and Cohen’s kappa ≥ 0.64 coefficients were found in all three indices for canopies reaching the surface with extensions greater than 1 hectare, with the KD showing the highest average kappa score (0.66). Measurements between previously surveyed georeferenced points and remotely-sensed kelp grid cells (low-resolution validation) showed that 66% of the georeferenced points had grid cells indicating kelp presence within a linear distance of 300 m. We employed the KD in our kelp filter algorithm to estimate the global extent of giant kelp and intertidal green algae per marine ecoregion and province, producing a high-resolution global map of giant kelp and intertidal green algae, powered by Google Earth Engine.

Obstetric ultrasound is a fundamental ingredient of modern prenatal care with many applications including accurate dating of a pregnancy, identifying pregnancy-related complications, and diagnosis of fetal abnormalities. However, despite its many benefits, two factors currently prevent wide-scale uptake of this technology for point-of-care clinical decision-making in low- and middle-income country (LMIC) settings. First, there is a steep learning curve for scan proficiency, and second, there has been a lack of easy-to-use, affordable, and portable ultrasound devices. We introduce a framework toward addressing these barriers, enabled by recent advances in machine learning applied to medical imaging. The framework is designed to be realizable as a point-of-care ultrasound (POCUS) solution with an affordable wireless ultrasound probe, a smartphone or tablet, and automated machine-learning-based image processing. Specifically, we propose a machine-learning-based algorithm pipeline designed to automatically estimate the gestational age of a fetus from a short fetal ultrasound scan. We present proof-of-concept evaluation of accuracy of the key image analysis algorithms for automatic head transcerebellar plane detection, automatic transcerebellar diameter measurement, and estimation of gestational age on conventional ultrasound data simulating the POCUS task and discuss next steps toward translation via a first application on clinical ultrasound video from a low-cost ultrasound probe.

Sudoku is one of the most popular puzzle games of all time. Today, Sudoku is based on simple rules of placing the numbers from 1 to 9 in the empty cells of the Sudoku board. After solving the Sudoku, each row, column, and mini grid should have only one occurrence of each digit between 1 and 9. There are various approaches introduced to solve the Sudoku game till now. The solutions in the state of art are computationally complex as the algorithmic complexity for the solution falls under the class of NP-complete. The paper discusses various solutions for the problem along with their pros and cons. The paper also discusses the performance of a serial version solution with respect to execution time taken to solve Sudoku. With certain modifications and assumptions to the serial version code, we have evaluated the possible solution and bottleneck in the parallel approach. The results have been evaluated for the parallelized algorithm on a normal user machine first, and then on a supercomputing solution box PARAMSHAVAK. Finally, the paper is concluded with the analysis of all the three solution aspects proposed.

This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.

Backgroud: Hypocalcemia can be defined as a state when serum calcium level is less than 7 mg/dl in preterm neonates and less than 8 mg/dl in term neonates and less than 8.5 mg/dl in older children. Nutritional rickets is commonly associated with rickets in children. Objective: To determine the common etiological factors of hypocalcemia and nutritional rickets in children. Study design: Cross sectional analytical study. Place and duration of study: This study was conducted in the department of Pediatrics, King Edward Medical University/Mayo hospital Lahore from March 1998 to January 2001. Patients and methods: 120 patients (2 months to 60 months) who presented with tetany, clinical and skeletal manifestations of rickets and seizures were included in the study. Biochemical profile (serum calcium, phosphate and alkaline phosphatase) and radiographs of wrist joint were done. Each child was accessed for age, sex, feeding pattern, exposure to sunlight, number of children in family, maternal age, educational sta tus of mothers, presenting features of the disease, total calcium, phosphate and alkaline phosphatase level in the serum and X-ray wrist. Results:: The study group comprised of 120 children (72 boys & 48 girls) ranging from 2 months to 3.5 years. 65% of the children had weight below 5th centile on National Center for Health Statistics charts. 32% of children got sunlight exposure for less than 30 minutes/week and 16% got it for 30-120 minutes/week. Out of total 120, 110 were below 2years among these 110, 9% were exclusive breast fed, 35% got diluted fresh milk, 1% got formula milk and 13% got both breast and bottle feeding. In 45% children weaning was not started. 47.5% couples had 2 or more children. 72% mothers were below 30 years of age, at the time of interview. 31% of the mothers were uneducated. 85% children had seizures at the time of presentation. Out of these 24.51% had upper respiratory tract infection and 51.96% had lower respiratory tract infection 79% had clinical signs of florid rickets. 67.5% of patients were had serum calcium between 6 & 7mg/dl, 69% were had serum phosphate level < 4 mg/dl and 76% had alkaline phosphatase level >1000IU/L. Conclusion: Hypocalcemia represents a major health problem in Pakistani children. Infants under 2 years of age are liable to have vitamin D deficiency rickets particularly if they are exclusively breastfed or received fresh milk with reduced exposure to sunlight. The higher the level of education of mothers, the less likely is the chance that their children become rachitic. Hypocalcemia can present with a wide variety of symptoms, the most important of which are seizures and recurrent chest infections. Rickets must be looked for as an underlying cause of these complaints.

In this paper, we present a fully automated machine-learning based solution to localize the fetus and extract the best fetal biometry planes for the head and abdomen from 11–13+6days week 3D fetal ultrasound (US) images. Our method to localize the whole fetus in the sagittal plane utilizes Structured Random Forests (SRFs) and classical Random Forests (RFs). A transfer learning Convolutional Neural Network (CNNs) is then applied to axial images to localize one of three classes (head, body and non-fetal). Finally, the best fetal head and abdomen planes are automatically extracted based on clinical knowledge of the position of the fetal biometry planes within the head and body. Our hybrid method achieves promising localization of the best biometry fetal planes with 1.6 mm and 3.4 mm for head and abdomen plane localization respectively compared to the best manually chosen biometry planes.

In this work we investigate the charge transport, injection and optical properties of several series of fluorene based copolymers. These properties are key areas for polymer semiconductor research studies that are intended to address the challenges of achieving electrically pumped polymer lasing. In general, high charge carrier mobility and luminescence efficiency have been found to be mutually exclusive, however, optimising both together, as well as demonstrating high optical gain will be a significant step to offset losses associated with the mechanisms for lasing. A further requirement is that it is necessary to achieve efficient charge injection in the particular materials that are found to have both of these properties optimised. We concentrate our investigation on fluorene-based copolymers, which have been amongst the earliest classes of materials of interest for polymer lasers (including studies in optically pumped polymer lasers), and since it has previously been shown that high hole mobility and luminescence efficiency may be achieved in fluorene based copolymers with simple alterations to side chain configurations. However, ohmic hole injection into poly(9,9-dioctylfluorene) (PFO) has been difficult to achieve because of its deep-lying highest occupied molecular orbital (HOMO) level of ~5.8eV below the vacuum level. Therefore, in our first experimental study, we revisit the charge transport properties of PFO based on recent developments in the use of transition metal oxides as hole injecting electrodes. With the ability to achieve ohmic injection in PFO, we are able to use Dark Injection Space Charge Limited Current (DI-SCLC) and field effect transistor (FET) techniques, both of which require ohmic injecton for charge transport measurements, and make comparisons with measurements from Time-of-Flight (ToF) photocurrent experiments. Together, these techniques span several orders of magnitude in charge carrier density, which provides an additional perspective on charge transport and the presence of charge traps. The subsequent chapters introduce three series of fluorene-based copolymers. The first series of blue emitting polymers has a resemblance to familiar fluorene based polymers with hexyloxyphenyl substituents instead of linear alkyl side chains and the series contains copolymers with a phenoxazine (BPPX) unit derived from more familiar triarylamine structures. Optical measurements show these polymers are free of morphologies such as the beta-phase and we obtain a hole mobility of ~10-3 cm2/Vs, photoluminescence quantum efficiency (PLQE) of 65.7% and optical gain of 41cm-1 for poly(9,9-di(4-hexyloxyphenyl)fluorene). Transient DI-SCLC measurements are used to show differences in charge trapping behaviour in the series. The final chapter of experimental results is based on novel poly(indenofluorene-fluorene) and poly(indenopyrazine-fluorene) copolymers. In the poly(indenofluorene-fluorene) series we consider the role of increased backbone rigidity compared to polyfluorene and the variation of side chain length and structure on charge transport and optical properties. Copolymers in this series yield a time-of-flight hole mobility of ~10-2 cm2/Vs, PLQE of 61.5% and optical gain of 43cm-1. Finally, we show that optical gain is possible in the indenopyrazine-fluorene series, which has been designed with the aim of achieving balanced electron and hole mobility. Open Access

In this paper, we propose a novel machine learning based method to categorize unlabeled fetal ultrasound images. The proposed method guides the learning of a Random Forests classifier to extract features from regions inside the images where meaningful structures exist. The new method utilizes a translation and orientation invariant feature which captures the appearance of a region at multiple spatial resolutions. Evaluated on a large real world clinical dataset (~30K images from a hospital database), our method showed very promising categorization accuracy (accuracytop1 is 75% while accuracytop2 is 91%).

BACKGROUND: Three-dimensional fusion echocardiography (3DFE) is a novel postprocessing approach that utilizes imaging data acquired from multiple 3D acquisitions. We assessed image quality, endocardial border definition, and cardiac wall motion in patients using 3DFE compared to standard 3D images (3D) and results obtained with contrast echocardiography (2DC). METHODS: Twenty-four patients (mean age 66.9 ± 13 years, 17 males, 7 females) undergoing 2DC had three, noncontrast, 3D apical volumes acquired at rest. Images were fused using an automated image fusion approach. Quality of the 3DFE was compared to both 3D and 2DC based on contrast-to-noise ratio (CNR) and endocardial border definition. We then compared clinical wall-motion score index (WMSI) calculated from 3DFE and 3D to those obtained from 2DC images. RESULTS: Fused 3D volumes had significantly improved CNR (8.92 ± 1.35 vs. 6.59 ± 1.19, P

This paper develops a novel approach to find the plane in a 3D fetal ultrasound scan which corresponds to the 2D diagnostic plane used in cranial ultrasound of a neonate to allow image-based biomarkers to be tracked from pre-birth through the first weeks of post-birth life. We propose a method based on regression forests (RF) with important algorithm design considerations taken into account to provide an accurate plane-finding solution. Specifically, the new method constrains the RF method by 1) using informative voxels and voxel informative strength as a weighting within the training stage objective function u, and 2) introducing regularization of the RF by proposing a geometrical feature within the training stage. Results on clinical data indicate that the new automated method is more reproducible than manual plane finding.

Objective: To combine multiple 3D volumes of the same fetal femur into one composite image data set using image registration and wavelet-based fusion. Fused and single data sets were compared in terms of image quality and femur volume (FV) measurement repeatability. Method: In healthy pregnant volunteers, six volumes of the same femur were acquired and fused into a composite data set. Image quality scores were given to the fused and single data sets by an independent assessor in a blinded fashion; repeatability of FV measurement was assessed using coefficients of variation (CV), intraclass correlation coefficients (ICC) and Bland-Altman plots. Results: Fusion was successful in 24 out of 25 cases. Median image quality score was 7/10 in fused data sets, compared to 6/10 in single data sets (p = 0.096). Repeatability of FV measurement was better in fused data sets (intraobserver CV 4.6% and ICC 0.987; interobserver CV 4.9%, ICC 0.985) compared to single ones (intraobserver CV 5.8%, ICC 0.977; interobserver CV 10.0%, ICC 0.931). The measured FV was significantly higher in fused data sets (mean FV 1.7 vs. 1.3 ml, p < 0.001). Conclusion: Image registration and wavelet-based fusion can improve image quality and FV repeatability; it also results in an increased FV measurement.

Neurosonography is the most widely used imaging technique for assessing neuro-development of the growing fetus in clinical practice. 3D neurosonography has an advantage of quick acquisition but is yet to demonstrate improvements in clinical workflow. In this paper we propose an automatic technique to segment four important fetal brain structures in 3D ultrasound. The technique is built within a Random Decision Forests framework. Our solution includes novel pre-processing and new features. The pre-processing step makes sure that all volumes are in the same coordinate. The new features constrain the appearance framework by adding a novel distance feature. Validation on 51 3D fetal neurosonography images shows that the proposed technique is capable of segmenting fetal brain structures and providing promising qualitative and quantitative results.

This work addresses the challenging problem of segmenting the myocardium in 3D LV echocardiograms by Random Forests (RF). While the RF framework has proven to be a good discriminative classifier for segmentation of 3D echocardiography [1], our hypothesis is that richer features than those traditionally used (Haar etc) need to be employed for accurate segmentation to tackle artifacts in ultrasound images such as missing anatomical boundaries. To address this, we propose two new context rich and shape invariant features, called LoCo and GloCo. The new features impose a local and global constraint on the coupled endocardial and epicardial shape of the left ventricle and use barycentric coordinates to uniquely identify the position of a voxel with respect to a number of landmarks on the epicardial and endocardial border. The landmarks are found using a new measure (COFA) to separate the two boundaries. Experimental results show that the new features provide a smoother segmentation and improve the accuracy compared with a classic RF implementation.

Objective Cranial sutures and fontanelles can be reliably demonstrated using three-dimensional (3D) ultrasound with rendering. Our objective was to assess the repeatability and validity of fontanelle surface area measurement on rendered 3D images. Methods This was an in-vitro phantom validation study. Four holes, representing fontanelles, were cut on a flat vinyl tile. The phantom was scanned in a test-tank by two sonographers, at four different depths and using two different 3D sweep directions. The surface areas were measured on scan images and also directly from the phantom for comparison. Coefficients of variation (CVs), intraclass correlation coefficients (ICCs) and Bland–Altman plots were used for repeatability analysis. Validity was expressed as the percentage difference of the measured area from the true surface area. Results Validity of measurement was satisfactory with a mean percentage difference of − 5.9% (median = − 3.5%). The 95% limits of agreement were − 23.9 to 12.1%, suggesting that random error is introduced during image generation and measurement. Repeatability of caliper placement on the same image was higher (intraobserver CV = 1.6%, ICC = 0.999) than for measurement of a newly generated scan image (intraobserver CV = 5.5%, ICC = 0.992). Reduced accuracy was noted for the smallest shape tested. Conclusion Surface area measurements on rendered 3D ultrasound images are accurate and reproducible in vitro. Copyright © 2011 ISUOG. Published by John Wiley & Sons, Ltd.

Arthritis is one of the major problems in older age. There are about as many herbal remedies for arthritis as there are arthritis sufferers. Most work by improving the circulation, and encourage fluid elimination. Inflammation due arthritis is mediated through many primary and secondary mediators. Interleukins and TNF-α are one of these mediators. Anti-inflammatory agents are used in arthritis widely. A large flora of herbal medicines is available in traditional medicine and basic scientific research has provided an understanding of the efficacy of these remedies in treating arthritis. In recent years, there has been boom in research and industries focusing on herbal remedies to treat a variety of diseases including arthritis. Several developments in clinical studies in carefully designed clinical trials have made the way easier to include herbs as arthritis therapy. This review is focused on the potential role of herbs in treating arthritis and prospects for the future.