Virus detections among patients with severe acute respiratory illness, Northern Vietnam.

Severe acute respiratory illness (SARI) is a major cause of death and morbidity in low- and middle-income countries, however, the etiologic agents are often undetermined due to the lack of molecular diagnostics in hospitals and clinics. To examine evidence for select viral infections among patients with SARI in northern Vietnam, we studied 348 nasopharyngeal samples from military and civilian patients admitted to 4 hospitals in the greater Hanoi area from 2017-2019. Initial screening for human respiratory viral pathogens was performed in Hanoi, Vietnam at the National Institute of Hygiene and Epidemiology (NIHE) or the Military Institute of Preventative Medicine (MIPM), and an aliquot was shipped to Duke-NUS Medical School in Singapore for validation. Patient demographics were recorded and used to epidemiologically describe the infections. Among military and civilian cases of SARI, 184 (52.9%) tested positive for one or more respiratory viruses. Influenza A virus was the most prevalent virus detected (64.7%), followed by influenza B virus (29.3%), enterovirus (3.8%), adenovirus (1.1%), and coronavirus (1.1%). Risk factor analyses demonstrated an increased risk of influenza A virus detection among military hospital patients (adjusted OR, 2.0; 95% CI, 1.2-3.2), and an increased risk of influenza B virus detection among patients enrolled in year 2017 (adjusted OR, 7.9; 95% CI, 2.7-22.9). As influenza A and B viruses were commonly associated with SARI and are treatable, SARI patients entering these hospitals would benefit if the hospitals were able to adapt onsite molecular diagnostics.

PDM-ENLOR for segmentation of mouse brain gene expression images.

Statistical shape models, such as Active Shape Models (ASMs), suffer from their inability to represent a large range of variations of a complex shape and to account for the large errors in detection of (point) landmarks. We propose a method, PDM-ENLOR (Point Distribution Model-based ENsemble of LOcal Regressors), that overcomes these limitations by locating each landmark individually using an ensemble of local regression models and appearance cues from selected landmarks. We first detect a set of reference landmarks which were selected based on their saliency during training. For each landmark, an ensemble of regressors is built. From the locations of the detected reference landmarks, each regressor infers a candidate location for that landmark using local geometric constraints, encoded by a point distribution model (PDM). The final location of that point is determined as a weighted linear combination, whose coefficients are learned from the training data, of candidates proposed by its ensemble's component regressors. We use multiple subsets of reference landmarks as explanatory variables for the component regressors to provide varying degrees of locality for the models in each ensemble. This helps our ensemble model to capture a larger range of shape variations as compared to a single PDM. We demonstrate the advantages of our method on the challenging problem of segmenting gene expression images of mouse brain. The overall mean and standard deviation of the Dice coefficient overlap over all 14 anatomical regions and all 100 test images were (88.1 ± 9.5)%.

3D dense local point descriptors for mouse brain gene expression images.

Anatomical landmarks play an important role in many biomedical image analysis applications (e.g., registration and segmentation). Landmark detection can be computationally very expensive, especially in 3D images, because every single voxel in a region of interest may need to be evaluated. In this paper, we introduce two 3D local image descriptors which can be computed simultaneously for every voxel in a volume. Both our proposed descriptors are extensions of the DAISY descriptor, a popular descriptor that is based on the histograms of oriented gradients and was named after its daisy-flower-like configuration. Our experiments on mouse brain gene expression images indicate that our descriptors are discriminative and are able to reduce the detection errors of landmark points more than 30% when compared with SIFT-3D, an extension in 3D of SIFT (scale-invariant feature transform). We also demonstrate that our descriptors are more computationally efficient than SIFT-3D and n-SIFT (an extension SIFT in n-dimensions) for densely sampled points. Therefore, our descriptors can be used in applications that require computation of the descriptors at densely sampled points (e.g., landmark point detection or feature-based registration).

Similarity-based appearance-prior for fitting a subdivision mesh in gene expression images.

Automated segmentation of multi-part anatomical objects in images is a challenging task. In this paper, we propose a similarity-based appearance-prior to fit a compartmental geometric atlas of the mouse brain in gene expression images. A subdivision mesh which is used to model the geometry is deformed using a Markov random field (MRF) framework. The proposed appearance-prior is computed as a function of the similarity between local patches at corresponding atlas locations from two images. In addition, we introduce a similarity-saliency score to select the mesh points that are relevant for the computation of the proposed prior. Our method significantly improves the accuracy of the atlas fitting, especially in the regions that are influenced by the selected similarity-salient points, and outperforms the previous subdivision mesh fitting methods for gene expression images.

Markov Random Field-based Fitting of a Subdivision-based Geometric Atlas.

An accurate labeling of a multi-part, complex anatomical structure (e.g., brain) is required in order to compare data across images for spatial analysis. It can be achieved by fitting an object-specific geometric atlas that is constructed using a partitioned, high-resolution deformable mesh and tagging each of its polygons with a region label. Subdivision meshes have been used to construct such an atlas because they can provide a compact representation of a partitioned, multi-resolution, object-specific mesh structure using only a few control points. However, automated fitting of a subdivision mesh-based geometric atlas to an anatomical structure in an image is a difficult problem and has not been sufficiently addressed. In this paper, we propose a novel Markov Random Field-based method for fitting a planar, multi-part subdivision mesh to anatomical data. The optimal fitting of the atlas is obtained by determining the optimal locations of the control points. We also tackle the problem of landmark matching in tandem with atlas fitting by constructing a single graphical model to impose pose-invariant, landmark-based geometric constraints on atlas deformation. The atlas deformation is also governed by additional constraints imposed by the mesh's geometric properties and the object boundary. We demonstrate the potential of the proposed method on the difficult problem of segmenting a mouse brain and its interior regions in gene expression images which exhibit large intensity and shape variability. We obtain promising results when compared with manual annotations and prior methods.

Landmark/Image-based Deformable Registration of Gene Expression Data.

Analysis of gene expression patterns in brain images obtained from high-throughput in situ hybridization requires accurate and consistent annotations of anatomical regions/subregions. Such annotations are obtained by mapping an anatomical atlas onto the gene expression images through intensity- and/or landmark-based registration methods or deformable model-based segmentation methods. Due to the complex appearance of the gene expression images, these approaches require a pre-processing step to determine landmark correspondences in order to incorporate landmark-based geometric constraints. In this paper, we propose a novel method for landmark-constrained, intensity-based registration without determining landmark correspondences a priori. The proposed method performs dense image registration and identifies the landmark correspondences, simultaneously, using a single higher-order Markov Random Field model. In addition, a machine learning technique is used to improve the discriminating properties of local descriptors for landmark matching by projecting them in a Hamming space of lower dimension. We qualitatively show that our method achieves promising results and also compares well, quantitatively, with the expert's annotations, outperforming previous methods.

A supervised classification-based method for coronary calcium detection in non-contrast CT.

Accurate quantification of coronary artery calcium provides an opportunity to assess the extent of atherosclerosis disease. Coronary calcification burden has been reported to be associated with cardiovascular risk. Currently, an observer has to identify the coronary calcifications among a set of candidate regions, obtained by thresholding and connected component labeling, by clicking on them. To relieve the observer of such a labor-intensive task, an automated tool is needed that can detect and quantify the coronary calcifications. However, the diverse and heterogeneous nature of the candidate regions poses a significant challenge. In this paper, we investigate a supervised classification-based approach to distinguish the coronary calcifications from all the candidate regions and propose a two-stage, hierarchical classifier for automated coronary calcium detection. At each stage, we learn an ensemble of classifiers where each classifier is a cost-sensitive learner trained on a distinct asymmetrically sampled data subset. We compute the relative location of the calcifications with respect to a heart-centered coordinate system, and also use the neighboring regions of the calcifications to better characterize their properties for discrimination. Our method detected coronary calcifications with an accuracy, sensitivity and specificity of 98.27, 92.07 and 98.62%, respectively, for a testing dataset of non-contrast computed tomography scans from 105 subjects.