ResDUnet: Residual Dilated UNet for Left Ventricle Segmentation from Echocardiographic Images

Echocardiography is the modality of choice for the assessment of left ventricle function. Left ventricle is responsible for pumping blood rich in oxygen to all body parts. Segmentation of this chamber from echocardiographic images is a challenging task, due to the ambiguous boundary and inhomogeneous intensity distribution. In this paper we propose a novel deep learning model named ResDUnet. The model is based on U-net incorporated with dilated convolution, where residual blocks are employed instead of the basic U-net units to ease the training process. Each block is enriched with squeeze and excitation unit for channel-wise attention and adaptive feature re-calibration. To tackle the problem of left ventricle shape and size variability, we chose to enrich the process of feature concatenation in U-net by integrating feature maps generated by cascaded dilation. Cascaded dilation broadens the receptive field size in comparison with traditional convolution, which allows the generation of multi-scale information which in turn results in a more robust segmentation. Performance measures were evaluated on a publicly available dataset of 500 patients with large variability in terms of quality and patients pathology. The proposed model shows a dice similarity increase of 8.4% when compared to deeplabv3 and 1.2% when compared to the basic U-net architecture. Experimental results demonstrate the potential use in clinical domain

ResDUnet: A Deep Learning based Left Ventricle Segmentation Method for Echocardiography

Segmentation of echocardiographic images is an essential step for assessing the cardiac functionality and providing indicative clinical measures, and all further heart analysis relies on the accuracy of this process. However, the fuzzy nature of echocardiographic images degraded by distortion and speckle noise poses some challenges on the manual segmentation task. In this paper, we propose a fully automated left ventricle segmentation method that can overcome those challenges. Our method performs accurate delineation for the ventricle boundaries despite the ill-defined borders and shape variability of the left ventricle. The well-known deep learning segmentation model, known as the U-net, has addressed some of these challenges with outstanding performance. However, it still ignores the contribution of all semantic information through the segmentation process. Here we propose a novel deep learning segmentation method based on U-net, named ResDUnet. It incorporates feature extraction at different scales through the integration of cascaded dilated convolution. To ease the training process, residual blocks are deployed instead of the basic U-net blocks. Each residual block is enriched with a squeeze and excitation unit for channel-wise attention and adaptive feature re-calibration. The performance of the method is evaluated on a dataset of 2000 images acquired from 500 patients with large variability in quality and patient pathology. ResDUnet outperforms state-of-the-art methods with a Dice similarity increase of 8.4% and 1.2% compared to deeplabv3 and U-net, respectively. Furthermore, to demonstrate the impact of each proposed sub-module, several experiments have been carried out with different designs and variations of the integrated sub-modules. We also describe and discuss all technical elements of a deep-learning model via a step-by-step explanation of parameters and methods, while using our left ventricle segmentation as a case study, to explain the application of AI to echocardiographic imaging

MDA-Unet: A Multi-Scale Dilated Attention U-Net For Medical Image Segmentation

The advanced development of deep learning methods has recently made significant improvements in medical image segmentation. Encoder–decoder networks, such as U-Net, have addressed some of the challenges in medical image segmentation with an outstanding performance, which has promoted them to be the most dominating deep learning architecture in this domain. Despite their outstanding performance, we argue that they still lack some aspects. First, there is incompatibility in U-Net’s skip connection between the encoder and decoder features due to the semantic gap between low-processed encoder features and highly processed decoder features, which adversely affects the final prediction. Second, it lacks capturing multi-scale context information and ignores the contribution of all semantic information through the segmentation process. Therefore, we propose a model named MDA-Unet, a novel multi-scale deep learning segmentation model. MDA-Unet improves upon U-Net and enhances its performance in segmenting medical images with variability in the shape and size of the region of interest. The model is integrated with a multi-scale spatial attention module, where spatial attention maps are derived from a hybrid hierarchical dilated convolution module that captures multi-scale context information. To ease the training process and reduce the gradient vanishing problem, residual blocks are deployed instead of the basic U-net blocks. Through a channel attention mechanism, the high-level decoder features are used to guide the low-level encoder features to promote the selection of meaningful context information, thus ensuring effective fusion. We evaluated our model on 2 different datasets: a lung dataset of 2628 axial CT images and an echocardiographic dataset of 2000 images, each with its own challenges. Our model has achieved a significant gain in performance with a slight increase in the number of trainable parameters in comparison with the basic U-Net model, providing a dice score of 98.3% on the lung dataset and 96.7% on the echocardiographic dataset, where the basic U-Net has achieved 94.2% on the lung dataset and 93.9% on the echocardiographic dataset

MCA-Unet: A multiscale context aggregation U-Net for the segmentation of COVID-19 lesions from CT images

The pandemic of coronavirus disease (COVID-19) caused the world to face an existential health crisis. COVID-19 lesions segmentation from CT images is nowadays an essential step to assess the severity of the disease and the amount of damage to the lungs. Deep learning has brought about a breakthrough in medical image segmentation where U-Net is the most prominent deep network. However, in this study, we argue that its architecture still lacks in certain aspects. First, there is an incompatibility in the U-Net skip connection between the encoder and decoder features which adversely affects the final prediction. Second, it lacks capturing multiscale context information and ignores the contribution of all semantic information through the segmentation process. Therefore, we propose a model named MCA-Unet, a novel multiscale deep learning segmentation model, which proposes some modifications to improve upon the U-Net model. MCA-Unet is integrated with a multiscale context aggregation module which is constituted of two blocks; a context embedding block (CEB) and a cascaded dilated convolution block (CDCB). The CEB aims at reducing the semantic gap between the concatenated features along the U-Net skip connections, it enriches the low-level encoder features with rich semantics inherited from the subsequent higher-level features, to reduce the semantic gap between the low-processed encoder features and the highly-processed decoder features, thus ensuring effectual concatenation. The CDCB is integrated to address the variability in shape and size of the COVID-19 lesions, it captures global context information by gradually expanding the receptive field, then operates reversely to capture the small fine details that might be scattered by enlarging the receptive field. To validate the robustness of our model, we tested it on a publicly available dataset of 1705 axial CT images with different types of COVID-19 infection. Experimental results show that MCA-Unet has attained a remarkable gain in performance in comparison with the basic U-Net and its variant. It achieved high performance using different evaluation metrics showing 88.6% Dice similarity coefficient, 85.4% Jaccard index, and 93.5% F-score measure. This outperformance shows great potential to help physicians during their examination and improve the clinical workflow

Miltefosine Lipid Nanocapsules for Single Dose Oral Treatment of Schistosomiasis Mansoni: A Preclinical Study.

Miltefosine (MFS) is an alkylphosphocholine used for the local treatment of cutaneous metastases of breast cancer and oral therapy of visceral leishmaniasis. Recently, the drug was reported in in vitro and preclinical studies to exert significant activity against different developmental stages of schistosomiasis mansoni, a widespread chronic neglected tropical disease (NTD). This justified MFS repurposing as a potential antischistosomal drug. However, five consecutive daily 20 mg/kg doses were needed for the treatment of schistosomiasis mansoni in mice. The present study aims at enhancing MFS efficacy to allow for a single 20mg/kg oral dose therapy using a nanotechnological approach based on lipid nanocapsules (LNCs) as oral nanovectors. MFS was incorporated in LNCs both as membrane-active structural alkylphospholipid component and active antischistosomal agent. MFS-LNC formulations showed high entrapment efficiency (EE%), good colloidal properties, sustained release pattern and physical stability. Further, LNCs generally decreased MFS-induced erythrocyte hemolytic activity used as surrogate indicator of membrane activity. While MFS-free LNCs exerted no antischistosomal effect, statistically significant enhancement was observed with all MFS-LNC formulations. A maximum effect was achieved with MFS-LNCs incorporating CTAB as positive charge imparting agent or oleic acid as membrane permeabilizer. Reduction of worm load, ameliorated liver pathology and extensive damage of the worm tegument provided evidence for formulation-related efficacy enhancement. Non-compartmental analysis of pharmacokinetic data obtained in rats indicated independence of antischistosomal activity on systemic drug exposure, suggesting possible gut uptake of the stable LNCs and targeting of the fluke tegument which was verified by SEM. The study findings put forward MFS-LNCs as unique oral nanovectors combining the bioactivity of MFS and biopharmaceutical advantages of LNCs, allowing targeting via the oral route. From a clinical point of view, data suggest MFS-LNCs as a potential single dose oral nanomedicine for enhanced therapy of schistosomiasis mansoni and possibly other diseases

Occurrence of Multidrug-Resistant Strains of <i>Acinetobacter</i> spp.: An Emerging Threat for Nosocomial-Borne Infection in Najran Region, KSA

Multidrug-resistant strains are frequent causes of nosocomial infections. The majority of nosocomial infections, particularly in critical care units (ICU), have been linked to A. baumannii, which has major clinical significance. The current paper attempts to identify the potential risk and prognosis factors for acquiring an infection due to A. baumannii compared to that of other nosocomial bacteria. In our study, we employed antibiotics generally prescribed for the initial course of treatment such as colistin, meropenem, amikacin, trimethoprime-sulfamethoxazole, levofloxacin, gentamicin, ciprofloxacin, and piperacillin-tazobactam. We found that the isolated A. baumannii were resistant at a high rate to meropenem, piperacillin–tazobactam, amikacin, levofloxacin, and ciprofloxacin, while they were partially susceptible to trimethoprim-sulfamethoxazole. Our study revealed that A. baumannii was most susceptible to gentamicin and colistin at 85.8% and 92.9%, respectively, whereas the combination of colistin and trimethoprim/sulfamethoxazole was 100% active. The patients were the primary source of infection with A. baumannii, followed by inanimate objects present in the ICU and hospital premises, and then the hospital staff who were taking care of the ICU patients. Gentamicin and colistin were the most sensitive antibiotics; of the 13 tested in total, the rate of drug resistance was above 50%. The very high rate of antibiotic resistance is alarming

Effect of miltefosine lipid nanocapsule formulations (MFS-LNCs) on <i>S</i>. <i>mansoni</i> worm burden as compared with control groups(n = 6).

<p>* F test (ANOVA). % R₁: % reduction in each of the study groups relative to infected control. %R₂:% reduction in each of the study groups relative to miltefosine solution control</p><p>^a: significant with infected untreated control</p><p>^b: significant with MFS solution control</p><p>^c: significant with group I</p><p>^d: significant with group II</p><p>^e: significant with group III</p><p>^f: significant with group IV</p><p>^g: significant with group V</p><p>F* = 78.369 (p<0.001).</p

Scanning Electron Microscopy (SEM) of a male <i>Schistosoma mansoni</i> worm from a mouse treated with MFS-LNC-OA showing.

<p>(a) Marked tegumental irregularity and disfigurement (X 3,500); (b) Tegumental surface blebbing (X 7,500); (c) Edema, flattening and sloughing of the whole tubercles with partial to complete loss of the spines (X 5,000); (d) and (e) nano-objects of similar size to lipid nanocapsules in between spines and on damaged schistosomal surface, respectively (X35,000). SEM of a normal male worm showing: (f) and (g) normal dorsal tegumental surface and papilla (X5,000 and 35,000 respectively)</p

Hypothetical diagram of lipid nanocapsules incorporating miltefosine.

<p>Hypothetical diagram of lipid nanocapsules incorporating miltefosine.</p