Mcmillanlamb1276

A cyclic dynamic model with time-varying angular frequency is introduced in UKF to characterize the variations in cardiac motion during image scanning. The proposed approach was trained and evaluated separately with varying amount of training data with images acquired from 20, 40, 60 and 80 patients. Evaluations over 1515 images with equal number of images from each chamber group acquired from an additional 20 patients demonstrated that the proposed model outperformed state-of-the-art and yielded a mean Dice coefficient value of 94.1%, 93.7% and 90.1% for 2, 3 and 4-chamber sequences, respectively, when trained with datasets from 80 patients.The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2148 COVID-19 cases and 1182 CAP cases from five hospitals, are conducted to evaluate the prediction accuracy of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.The efficient diagnosis of COVID-19 plays a key role in preventing the spread of this disease. The computer-aided diagnosis with deep learning methods can perform automatic detection of COVID-19 using CT scans. However, large scale annotation of CT scans is impossible because of limited time and heavy burden on the healthcare system. To meet the challenge, we propose a weakly-supervised deep active learning framework called COVID-AL to diagnose COVID-19 with CT scans and patient-level labels. The COVID-AL consists of the lung region segmentation with a 2D U-Net and the diagnosis of COVID-19 with a novel hybrid active learning strategy, which simultaneously considers sample diversity and predicted loss. With a tailor-designed 3D residual network, the proposed COVID-AL can diagnose COVID-19 efficiently and it is validated on a large CT scan dataset collected from the CC-CCII. The experimental results demonstrate that the proposed COVID-AL outperforms the state-of-the-art active learning approaches in the diagnosis of COVID-19. With only 30% of the labeled data, the COVID-AL achieves over 95% accuracy of the deep learning method using the whole dataset. The qualitative and quantitative analysis proves the effectiveness and efficiency of the proposed COVID-AL framework.Accurately counting the number of cells in microscopy images is required in many medical diagnosis and biological studies. This task is tedious, time-consuming, and prone to subjective errors. However, designing automatic counting methods remains challenging due to low image contrast, complex background, large variance in cell shapes and counts, and significant cell occlusions in two-dimensional microscopy images. In this study, we proposed a new density regression-based method for automatically counting cells in microscopy images. The proposed method processes two innovations compared to other state-of-the-art density regression-based methods. First, the density regression model (DRM) is designed as a concatenated fully convolutional regression network (C-FCRN) to employ multi-scale image features for the estimation of cell density maps from given images. Second, auxiliary convolutional neural networks (AuxCNNs) are employed to assist in the training of intermediate layers of the designed C-FCRN to improve the DRM performance on unseen datasets. Experimental studies evaluated on four datasets demonstrate the superior performance of the proposed method.Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of body regions. Modeling such information into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. However, existing deep learning based approaches neglect motion information during the reconstruction procedure, while traditional motion-guided methods are hindered by heuristic parameter tuning and long inference time. We propose a novel dynamic MRI reconstruction approach called MODRN and an end-to-end improved version called MODRN(e2e), both of which enhance the reconstruction quality by infusing motion information into the modeling process with deep neural networks. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components Dynamic Reconstruction Network, Motion Estimation and Motion Compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches.Hepatocellular carcinoma (HCC), as the most common type of primary malignant liver cancer, has become a leading cause of cancer deaths in recent years. Accurate segmentation of HCC lesions is critical for tumor load assessment, surgery planning, and postoperative examination. As the appearance of HCC lesions varies greatly across patients, traditional manual segmentation is a very tedious and time-consuming process, the accuracy of which is also difficult to ensure. Therefore, a fully automated and reliable HCC segmentation system is in high demand. In this work, we present a novel hybrid neural network based on multi-task learning and ensemble learning techniques for accurate HCC segmentation of hematoxylin and eosin (H&E)-stained whole slide images (WSIs). First, three task-specific branches are integrated to enlarge the feature space, based on which the network is able to learn more general features and thus reduce the risk of overfitting. Second, an ensemble learning scheme is leveraged to perform feature aggregation, in which selective kernel modules (SKMs) and spatial and channel-wise squeeze-and-excitation modules (scSEMs) are adopted for capturing the features from different spaces and scales. Our proposed method achieves state-of-the-art performance on three publicly available datasets, with segmentation accuracies of 0.797, 0.923, and 0.765 in the PAIP, CRAG, and UHCMC&CWRU datasets, respectively, which demonstrates its effectiveness in addressing the HCC segmentation problem. To the best of our knowledge, this is also the first work on the pixel-wise HCC segmentation of H&E-stained WSIs.8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG), a major product of DNA oxidation, is a pre-mutagenic lesion which is prone to mispair, if left unrepaired, with 2'-deoxyadenosine during DNA replication. While unrepaired or incompletely repaired 8-oxodG has classically been associated with genome instability and cancer, it has recently been reported to have a role in the epigenetic regulation of gene expression. Despite the growing collection of genome-wide 8-oxodG mapping studies that have been used to provide new insight on the functional nature of 8-oxodG within the genome, a comprehensive view that brings together the epigenetic and the mutagenic nature of the 8-oxodG is still lacking. To help address this gap, this review aims to provide (i) a description of the state-of-the-art knowledge on both the mutagenic and epigenetic roles of 8-oxodG; (ii) putative molecular models through which the 8-oxodG can cause genome instability; (iii) a possible molecular model on how 8-oxodG, acting as an epigenetic signal, could cause the translocations and deletions which are associated with cancer.Homologous recombination (HR), considered the highest fidelity DNA double-strand break (DSB) repair pathway that a cell possesses, is capable of repairing multiple DSBs without altering genetic information. However, in "last resort" scenarios, HR can be directed to low fidelity subpathways which often use non-allelic donor templates. Such repair mechanisms are often highly mutagenic and can also yield chromosomal rearrangements and/or deletions. While the choice between HR and its less precise counterpart, non-homologous end joining (NHEJ), has received much attention, less is known about how cells manage and prioritize HR subpathways. In this review, we describe work focused on how chromatin and nuclear architecture orchestrate subpathway choice and repair template usage to maintain genome integrity without sacrificing cell survival. Understanding the relationships between nuclear architecture and recombination mechanics will be critical to understand these cellular repair decisions.The purpose of this study was to examine how camera resolution and suspect-camera distance affect the accuracy and precision of suspect height estimations using PhotoModeler software. Sixteen individuals were measured and recorded standing at 15 pre-set distances on 7 security cameras, each with a different resolution setting. A height scale was used to measure each individual's height prior to recording and was also used as a reference height. Height estimates were taken in PhotoModeler by extracting video frames that were calibrated using 3D point model data obtained from a laser scan of the test site. Errors were calculated for the measurements and compared using the Kruskal-Wallis H-test, which indicated significant differences for errors among different resolutions and distances (p less then 0.01). Interaction plots, however, demonstrated little difference in errors for most resolutions and positions. The accuracy and precision of height estimates began to decrease with resolutions under 960H and distances over 36.5 m.Three-dimensional facial images are becoming more and more widespread. As such images provide more information about facial morphology than 2D imagery, they show great promise for use in future forensic applications, including age estimation and verification. This paper proposes an approach using random forests, a machine learning method, to develop and test models for classification of legal age thresholds (15 years and 18 years) using 3D facial landmarks. Our approach was developed on a set of 3D facial scans from 394 Czech individuals (194 males and 200 females) aged between 10 and 25 years. The dataset was retrieved from a sizable database of Central European faces - The FIDENTIS 3D Face Database. Three main types of input variables were processed using random forests I) shape (size-invariant) coordinates of 3D landmarks, II) size and shape coordinates of 3D landmarks, and III) inter-landmark distances, angles and indices. The performance rates for the combinations of variables and age threshold were expressed in terms of sensitivity and specificity.