Automated Multi-sequence Cardiac MRI Segmentation Using Supervised Domain Adaptation

Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixel-level annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multi-sequence CMR images is tedious and subject to inter- and intra-observer variations, developing methods that can automatically adapt from one domain to the target domain is of great interest. In this paper, we propose an approach for domain adaptation in multi-sequence CMR segmentation task using transfer learning that combines multi-source image information. We first train an encoder-decoder CNN on T2-weighted and balanced-Steady State Free Precession (bSSFP) MR images with pixel-level annotation and fine-tune the same network with a limited number of Late Gadolinium Enhanced-MR (LGE-MR) subjects, to adapt the domain features. The domain-adapted network was trained with just four LGE-MR training samples and obtained an average Dice score of ∼∼85.0% on the test set comprises of 40 LGE-MR subjects. The proposed method significantly outperformed a network without adaptation trained from scratch on the same set of LGE-MR training data

Comparison between Diazepam and Phenobarbital in Prevention of Febrile Seizure: Clinical Trial

AbstractObjectiveFebrile convulsions (FC) are the most common convulsive events in childhood, occurring in 2-5% of children. About one third of these children will have  a recurrence during a subsequent febrile infection. This sudden neurologic problem is extremely frightening and emotionally traumatic for parents so some physicians try to prevent recurrence of FC by prescribing different drugs.Materials and MethodsThis is a randomized clinical trial in 85 healthy children, aged 6 months to 5 years, who were not treated before. These children received randomly either oral diazepam (0.33 mg/kg/TDS for two days during febrile illness) or continuous oral Phenobarbital (3-5mg/kg /24 h).ResultsUltimately 64 patients completed the study and were followed up for an average of 13 months (12-18 months). The rate of recurrence of febrile seizure was 18.2% in diazepam group and 32.3% in Phenobarbital group; the difference is not statistically significant (p=0.16).ConclusionThere was no significant difference between intermittent oral diazepam and continuous oral Phenobarbital for FC prevention

Comparison of the arrhythmogenicity of acepromazine, xylazine and their combination in pentobarbital-anesthetized rats

Preanesthetic medications are often used in combination with injectable anesthetics in a variety of laboratory animal species. Simultaneous administration of sedative drugs, such as alpha2-adrenergic agonists and phenothiazines, provides muscle relaxation and reduces induction doses of anesthetic agents. However, these drugs may have significant cardiovascular and arrythmogenic effects which may contribute to anesthetic morbidity and mortality (Dyson et al., 1998).Results of previous reports indicate that xylazine, an alpha2-adrenergic agonist, may sensitize the myocardium to epinephrine in dogs anesthetized with halothane (Muir et al., 1975; Tranquilli et al., 1986), isoflurane (Tranquilli et al., 1988) and ketamine (Wright et al., 1987); whereas, acepromazine, a phenothiazine tranquilizer, possessed a protective action against catecholamine-induced arrhythmia in dogs anesthetized with halothane (Muir et al., 1975; Dyson & Pettifer, 1997). The male rat has been used as an animal model to determine the arrhythmic doses of epinephrine during halothane and isoflurane anesthesia (Laster et al., 1990). Rats are commonly used for scientific research and may be anesthetized using injectable or inhalant anesthetic agents for a variety of surgical procedures (Flecknell, 2009); however, injectable anesthetics are commonly preferred in a laboratory setting.Pentobarbital, as a short acting barbiturate anesthetic, is used for short surgical procedures in rats. It is rapidly absorbed following intraperitoneal administration and provide anesthesia for up to 60 min in the rat (Flecknell, 2009).The purpose of this study was to evaluate the effects of clinical doses of acepromazine, xylazine and their combination on the occurrence of epinephrine induced arrhythmia in rats under pentobarbital anesthesia

Anesthetic management of diaphragmatic hernia repair in a dog: a case report and literature review of anesthetic techniques

Summary This case report describes the anesthetic management and ventilation technique in the surgical treatment of traumatic diaphragmatic hernia in a dog. A 5-month-old 8-kg female terrier with a history of car accident was presented for femoral fracture repair. Before anesthetic induction, marked tachypnea and dyspnea were noted. Diaphragmatic hernia was diagnosed based upon radiographic and ultrasonographic findings. Exploratory laparotomy revealed diaphragmatic rupture and herniation of spleen, omentum, parts of liver lobes and stomach into the thoracic cavity. The importance of thorough physical examination and patient assessment, anesthetic management and monitoring, provision of adequate ventilation and oxygenation during surgery using standard ventilation equipment are discussed

Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks

Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes

A 2D dilated residual U-net for multi-organ segmentation in thoracic CT

Automatic segmentation of organs-at-risk (OAR) in computed tomography (CT) is an essential part of planning effective treatment strategies to combat lung and esophageal cancer. Accurate segmentation of organs surrounding tumours helps account for the variation in position and morphology inherent across patients, thereby facilitating adaptive and computer-assisted radiotherapy. Although manual delineation of OARs is still highly prevalent, it is prone to errors due to complex variations in the shape and position of organs across patients, and low soft tissue contrast between neighbouring organs in CT images. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. In this paper, we propose a deep learning framework to segment OARs in thoracic CT images, specifically for the: heart, esophagus, trachea and aorta. Our approach employs dilated convolutions and aggregated residual connections in the bottleneck of a U-Net styled network, which incorporates global context and dense information. Our method achieved an overall Dice score of 91.57% on 20 unseen test samples from the ISBI 2019 SegTHOR challenge

Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification

Quantitative assessment of cardiac left ventricle (LV) morphology is essential to assess cardiac function and improve the diagnosis of different cardiovascular diseases. In current clinical practice, LV quantification depends on the measurement of myocardial shape indices, which is usually achieved by manual delineation. However, this process is time-consuming and subject to inter and intra-observer variability. In this paper, we propose a Spatio-temporal multi-task learning approach to obtain a complete set of measurements quantifying cardiac LV morphology, regional-wall thickness (RWT), and additionally detecting the cardiac phase cycle (systole and diastole) for a given 3D Cine-magnetic resonance (MR) image sequence. We first segment cardiac LVs using an encoder-decoder network and then introduce a multitask framework to regress 11 LV indices and classify the cardiac phase, as parallel tasks during model optimization. The proposed deep learning model is based on the 3D Spatio-temporal convolutions, which extract spatial and temporal features from MR images. We demonstrate the efficacy of the proposed method using cine-MR sequences of 145 subjects and comparing the performance with other state-of-the-art quantification methods. The proposed method achieved high prediction accuracy, with an average mean absolute error (MAE) of 129 mm2 , 1.23 mm , 1.76 mm , Pearson correlation coefficient (PCC) of 96.4%, 87.2%, and 97.5% for LV and myocardium (Myo) cavity regions, 6 RWTs, 3 LV dimensions, and an error rate of 9.0% for phase classification. The experimental results highlight the robustness of the proposed method, despite varying degrees of cardiac morphology, image appearance, and low contrast in the cardiac MR sequences

The Effect of Data Augmentation on Classification of Atrial Fibrillation in Short Single-Lead ECG Signals Using Deep Neural Networks

Cardiovascular diseases are the most common cause of mortality worldwide. Detection of atrial fibrillation (AF) in the asymptomatic stage can help prevent strokes. It also improves clinical decision making through the delivery of suitable treatment such as, anticoagulant therapy, in a timely manner. The clinical significance of such early detection of AF in electrocardiogram (ECG) signals has inspired numerous studies in recent years, of which many aim to solve this task by leveraging machine learning algorithms. ECG datasets containing AF samples, however, usually suffer from severe class imbalance, which if unaccounted for, affects the performance of classification algorithms. Data augmentation is a popular solution to tackle this problem. In this study, we investigate the impact of various data augmentation algorithms, e.g., oversampling, Gaussian Mixture Models (GMMs) and Generative Adversarial Networks (GANs), on solving the class imbalance problem. These algorithms are quantitatively and qualitatively evaluated, compared and discussed in detail. The results show that deep learning-based AF signal classification methods benefit more from data augmentation using GANs and GMMs, than oversampling. Furthermore, the GAN results in circa 3% better AF classification accuracy in average while performing comparably to the GMM in terms of f1-score

Efeitos anestésicos da administração intranasal ou intramuscular de cetamina S+ e midazolam em pomba-rola (Streptotelia sp.)

A via intranasal é uma boa alternativa por ser indolor e de fácil aplicação em aves. O objetivo deste estudo foi avaliar os efeitos anestésicos da associação de cetamina S+ e midazolam pela via intranasal (IN) em comparação com a via intramuscular (IM) em pombos. Foram utilizados 12 pombos alocados em dois grupos com 15 dias de intervalo, os quais receberam: grupo IM: 20 mg/kg de cetamina S+ associada a 3,5 mg/kg de midazolam pela via intramuscular (musculatura do peito); e grupo IN, mesmo protocolo, porém, pela via intranasal. Os parâmetros avaliados foram: período de latência, tempo de duração em decúbito dorsal, tempo total de anestesia, tempo de recuperação e efeitos adversos. Para a análise estatística, empregou-se o teste de Wilcoxon, com as diferenças consideradas significativas quando P<0,05. O período de latência obtido foi de 30 [30-47,5] e 40 [30-50] segundos para IM e IN, respectivamente. O tempo de duração de decúbito dorsal foi de 59 [53,25-65] e 63 [37-71,25] minutos para IM e IN, respectivamente, sem diferenças significativas entre os grupos. Com relação à duração total de anestesia, foi observada diferença significativa, com 88 [86,25-94,5] e 68 [53,5-93] minutos para os grupos IM e IN, respectivamente. O tempo de recuperação foi mais curto no grupo IN (15 [4,25-19,5]) comparado ao IM (32 [28,25-38,25] minutos). Dois animais de cada grupo apresentaram regurgitação na fase de recuperação. Conclui-se que a administração de cetamina S+ e midazolam pela via intranasal é um método aceitável de administração de fármacos e produz anestesia rápida e eficaz em pombos