Lung infection segmentation for COVID-19 pneumonia based on a cascade convolutional network from CT images

The COVID-19 pandemic is a global, national, and local public health which causing a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose the patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To overcome the mentioned problems, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into normal and infected tissue. For improving the classification accuracy, we used two different strategies including Fuzzy c-mean clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find a more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved Precision 96%, Recall 97%, F-score, average surface distance (ASD) of 2.8\pm0.3\ mm and volume overlap error (VOE) of 5.6\pm1.2%

Suivi de la sécheresse par l’Indice de Précipitation Standardisé dans le bassin versant du Chéliff. Conférence Internationale sur l’hydrologie des grands bassins Fluviaux d’Afrique

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Investigation of effectiveness of shuffled frog-leaping optimizer in training a convolution neural network

One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine learning and computer vision fields. CNN is composed of several filters accompanied by nonlinear functions and pooling layers. It enforces limitations on the weights and interconnections of the neural network to create a good structure for processing spatial and temporal distributed data. A CNN can restrain the numbering of free parameters of the network through its weight-sharing property. However, the training of CNNs is a challenging approach. Some optimization techniques have been recently employed to optimize CNN's weight and biases such as Ant Colony Optimization, Genetic, Harmony Search, and Simulated Annealing. This paper employs the well-known nature-inspired algorithm called Shuffled Frog-Leaping Algorithm (SFLA) for training a classical CNN structure (LeNet-5), which has not been experienced before. The training method is investigated by employing four different datasets. To verify the study, the results are compared with some of the most famous evolutionary trainers: Whale Optimization Algorithm (WO), Bacteria Swarm Foraging Optimization (BFSO), and Ant Colony Optimization (ACO). The outcomes demonstrate that the SFL technique considerably improves the performance of the original LeNet-5 although using this algorithm slightly increases the training computation time. The results also demonstrate that the suggested algorithm presents high accuracy in classification and approximation in its mechanism

Compound heterozygous ASPM mutations associated with microcephaly and simplified cortical gyration in a consanguineous Algerian family

International audienceHomozygous mutations in the ASPM gene are a major cause of autosomal recessive primary microcephaly (MCPH). Here we report on a consanguineous Algerian family in which three out of five children presented with severe microcephaly, simplified cortical gyration, mild to severe mental retardation and low to low-normal birth weight. Given the parental consanguinity with the unaffected parents being third cousins once removed, the most probable pattern of inheritance was autosomal recessive. Linkage and mutational analyses identified compound heterozygous truncating mutations within the ASPM gene segregating with MCPH (c.2389C>T [p.Arg797X] and c.7781_7782delAG [p.Gln2594fsX6]). These results highlight some of the pitfalls of genetic analysis in consanguineous families. They also suggest that low birth weight may be a feature of MCPH, a finding that needs confirmation, and confirm that ASPM mutations are associated with simplified cortical gyration

Osteolysis: a literature review of basic science and potential computer-based Image processing detection methods

Osteolysis is one of the most prominent reasons of revision surgeries in total joint arthroplasty. is biological phenomenon is induced by wear particles and corrosion products that stimulate inflammatory biological response of surrounding tissues. e eventual responses of osteolysis are the activation of macrophages leading to bone resorption and prosthesis failure. Various factors are involved in the initiation of osteolysis from biological issues, design, material specifications, and model of the prosthesis to the health condition of the patient. Nevertheless, the factors leading to osteolysis are sometimes preventable. Changes in implant design and polyethylene manufacturing are striving to improve overall wear. Osteolysisis clinically asymptomatic and can be diagnosed and analyzed during follow-up sessions through various imaging modalities and methods, such as serial radiographic, CT scan, MRI, and image processing-based methods, especially with the use of artificial neural network algorithms. Deep learning algorithms with a variety of neural network structures such as CNN, U-Net, and Seg-UNet have proved to be efficient algorithms for medical image processing specifically in the field of orthopedics for the detection and segmentation of tumors. ese deep learning algorithms can effectively detect and analyze osteolytic lesions well in advance during follow-up sessions in order to administer proper treatments before reaching a critical point. Osteolysis can be treated surgically or nonsurgically with medications. However, revision surgeries are the only solution for the progressive osteolysis. In this literature review, the underlying causes, mechanisms, and treatments of osteolysis are discussed with the main focus on the possible computer-based methods and algorithms that can be effectively employed for the detection of osteolysis

Refining the phenotype associated with CASC5 mutation

Autosomal recessive primary microcephaly is a neurodevelopmental disorder characterized by congenitally reduced head circumference by at least two standard deviations (SD) below the mean for age and gender. It is associated with nonprogressive mental retardation of variable degree, minimal neurological deficit with no evidence of architectural anomalies of the brain. So far, 12 genetic loci (MCPH1-12) and corresponding genes have been identified. Most of these encode centrosomal proteins. CASC5 is one the most recently unravelled genes responsible for MCPH with mutations reported in three consanguineous families of Moroccan origin, all of whom harboured the same CASC5 homozygous mutation (c.6125G>A; p.Met2041Ile). Here, we report the identification, by whole exome sequencing, of the same missense mutation in a consanguineous Algerian family. All patients exhibited a similar clinical phenotype, including congenital microcephaly with head circumferences ranging from −3 to −4 standard deviations (SD) after age 5 years, moderate to severe cognitive impairment, short stature (adult height −3 SD), dysmorphic features included a sloping forehead, thick eyebrows, synophris and a low columella. Severe vermis hypoplasia and a large cyst of the posterior fossa were observed in one patient. Close microsatellite markers showed identical alleles in the Algerian the previously and Moroccan patients. This study confirms the involvement of CASC5 in autosomal recessive microcephaly and supports the hypothesis of a founder effect of the c.6125G>A mutation. In addition, this report refines the phenotype of this newly recognized form of primary microcephaly.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Mutation in TTI2 Reveals a Role for Triple T Complex in Human Brain Development

International audienc

Improved cardiac outcomes by early treatment with angiotensin-converting enzyme inhibitors in Becker muscular dystrophy

International audienceBackground: The latest practice guidelines from the American College of Cardiology/American Heart Association recommend the prescription of an ACE-i for patients presenting with non-ischemic cardiomyopathy when left ventricular ejection fraction (LVEF) falls below 40%. Objective: To determine if the initiation of treatment with an angiotensin-converting enzyme inhibitor (ACE-i) earlier than recommended by practice guidelines issued by professional societies improves the long-term cardiac outcomes of patients presenting with Becker muscular dystrophy (MD) cardiomyopathy. Methods: From a multicenter registry of Becker MD, we selected retrospectively patients presenting between January 1990 and April 2019 with a LVEF ≥40 and ≤49%. We used a propensity score analysis to compare the risk of a) hospitalization for management of heart failure (HF), and b) a decrease in LVEF to <35% in patients who received an ACE-i when LVEF fell below 40% (conventional treatment), versus below 50% (early treatment). Results: From the 183 patients entered in our registry, we identified 85 whose LVEF was between 40 and 49%, 51 of whom received early and 34 received conventional ACE-i treatment. Among patients with early versus conventional treatments, 2 (3.9%) versus 4 (11.8%) were hospitalized for management of HF [hazard ratio (HR) 0.151; 95% confidence interval (CI) 0.028 to 0.822; p = 0.029], and 9 (17.6%) versus 10 (29.4%) had a decrease in LVEF below 35% (HR 0.290; 95% CI 0.121 to 0.694; p = 0.005).Conclusions: The long-term cardiac outcome of patients presenting with Becker MD was significantly better when treatment with ACE-i was introduced after a decrease in LVEF below 50%, instead of below 40% as recommended in the current practice guidelines issued by professional societies

A recurrent homozygous LMNA missense variant p.Thr528Met causes atypical progeroid syndrome characterized by mandibuloacral dysostosis, severe muscular dystrophy, and skeletal deformities

Atypical progeroid syndromes (APS) are premature aging syndromes caused by pathogenicLMNA missense variants, associated with unaltered expression levels of lamins Aand C, without accumulation of wild-type or deleted prelamin A isoforms, as observed inHutchinson-Gilford progeria syndrome (HGPS) or HGPS-like syndromes. A specific LMNAmissense variant, (p.Thr528Met), was previously identified in a compound heterozygousstate in patients affected by APS and severe familial partial lipodystrophy, whereas heterozygositywas recently identified in patients affected by Type 2 familial partial lipodystrophy.Here, we report four unrelated boys harboring homozygosity for thep.Thr528Met, variant who presented with strikingly homogeneous APS clinical features,including osteolysis of mandibles, distal clavicles and phalanges, congenital muscular dystrophy with elevated creatine kinase levels, and major skeletal deformities. Immunofluorescenceanalyses of patient-derived primary fibroblasts showed a high percentage ofdysmorphic nuclei with nuclear blebs and typical honeycomb patterns devoid of laminB1. Interestingly, in some protrusions emerin or LAP2α formed aberrant aggregates, suggestingpathophysiology-associated clues. These four cases further confirm that a specificLMNA variant can lead to the development of strikingly homogeneous clinical phenotypes,in these particular cases a premature aging phenotype with major musculoskeletalinvolvement linked to the homozygous p.Thr528Met variant