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Observing the dynamic characteristics of proteins by multi-channel fluorescence microscopy allows studying the interactions between different subcellular structures. We introduce a probabilistic approach for tracking and colocalization analysis of viral proteins in two-channel microscopy image sequences. Our approach is based on particle filters and the Kalman filter, and performs tracking and colocalization...
Automatic analysis of fetal echocardiography screening images could aid in the identification of congenital heart diseases. The first step towards automatic fetal echocardiography analysis is locating the fetal heart in an image and identifying the viewing (imaging) plane. This is highly challenging since the fetal heart is small with relatively indistinct anatomical structural appearance. This is...
As medical imaging datasets grow, we are approaching the era of big data for radiologic decision support systems. This requires renewed efforts in dataset curation and labeling. We propose a methodology for weak labeling of medical images for attributes such as anatomy and disease that relies on image to sentence transformation. The methodology consists of three models, a convolutional neural network...
This study aims to investigate the apparent backscatter of cancellous bone from the second ultrasonic harmonics using an imaging system. A cylinder model was used as the bone trabecular model and a positive correlation was found between the power spectrum of the second harmonics and trabecular thickness. The result of the in vitro experiment using ultrasound imaging system shows that the apparent...
Automatic identification of side branch and main vascular measurements in IVOCT images take critical roles in pre-interventional decision making for coronary artery disease treatment. Very little works have been presented on these tasks. In this paper, we proposed a novel side branch identification algorithm which utilizes a newly defined global curvature feature to identify the ostium of side branch...
Histopathology image classification can provide automated support towards cancer diagnosis. In this paper, we present a transfer learning-based approach for histopathology image classification. We first represent the image feature by Fisher Vector (FV) encoding of local features that are extracted using the Convolutional Neural Network (CNN) model pretrained on ImageNet. Next, to better transfer the...
1p/19q co-deletion is an important prognostic factor in low grade gliomas. However, determination of the 1p/19q status currently requires a biopsy. To overcome this, we investigate a radiogenomic classification using support vector machines to non-invasively predict the 1p/19q status from multimodal MRI data. Different approaches of predicting this status were compared: a direct approach which predicts...
Automatic recognition of surgical workflow is an unresolved problem among the community of computer-assisted interventions. Among all the features used for surgical workflow recognition, one important feature is the presence of the surgical tools. Extracting this feature leads to the surgical tool presence detection problem to detect what tools are used at each time in surgery. This paper proposes...
Hyperechogenicity of the substantia nigra (SN) in the “butterfly shaped” midbrain is a widely recognized diagnostic marker to differentiate between the early stages of Parkinsons Disease (PD) and other diseases which cause parkinsonian symptoms. While clinical differentiation of these diseases can be difficult, hyperechogenicity of the SN is only common in PD patients. Transcranial B-mode Ultrasound...
In this study, we deal with the problem of image reconstruction from compressive measurements of multi-contrast magnetic resonance imaging (MRI). We propose a synthesis based approach for image reconstruction to better exploit mutual information across contrasts, while retaining individual features of each contrast image. For fast recovery, we propose an augmented Lagrangian based algorithm, using...
Retinal Neovascularization (NV) is a critical stage of Diabetic Retinopathy (DR) and its detection is important to prevent blindness. Existing fully supervised frameworks typically take a patch-based approach and report good results only on limited number of images due to sparsity of annotated data. We propose a patch-based semi-supervised framework which paves the way for including unlabeled data...
In this paper, we propose a novel segmentation method for cells in histopathologic images based on a sparse shape prior guided variational level set framework. We automate the cell contour initialization by detecting seeds and deform contours by minimizing a new energy functional that incorporates a shape term involving sparse shape priors, an adaptive contour occlusion penalty term, and a boundary...
Fluorescence microscopy has emerged as a powerful tool for studying cell biology because it enables the acquisition of 3D image volumes deeper into tissue and the imaging of complex subcellular structures. Quantitative analysis of these structures, which is needed to characterize the structure and constitution of tissue volumes, is facilitated by nuclei segmentation. However, manual segmentation is...
Image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy...
We introduce a novel model-based generator that produces biologically grounded synthetic volumes of the cerebrovasculature. Our models are synthesized stochastically, according to the biological characteristics of venule arborescence in the human collateral sulcus. Each synthetic volume produced is individually unique, yet representative of this cerebral region. As the locations and characteristics...
Estimating the main magnetic field inhomogeneity is important for many magnetic resonance imaging (MRI) techniques. Regularized estimation methods can provide accurate estimates that intrinsically avoid phase wrapping, account for the chemical shift due to fat, and reduce noise. However, these methods require minimizing nonconvex cost functions and existing algorithms are undesirably slow or do not...
Retinal nerve fiber layer defect (RNFLD) is the earliest objective evidence of glaucoma in fundus images. Glaucoma is an optic neuropathy which causes irreversible vision impairment. Early glaucoma detection and its prevention are the only way to prevent further damage to human vision. In this paper, we propose a new automated method for RNFLD detection in fundus images through patch features driven...
Brain connectivity is increasingly being studied using connectomes. Typical structural connectome definitions do not directly take white matter pathology into account. Presumably, pathology impedes signal transmission along fibres, leading to a reduction in function. In order to directly study disconnection and localize pathology within the connectome, we present the disconnectome, which only considers...
Osteoarthritis is a common cartilage disease, particularly in societies with aging population. Over 80% of the people over 75 years are affected in the USA. MRI and X-ray can be used to image cartilage, but both approaches suffer from specific drawbacks. X-ray Talbot-Lau interferometers (TLI) have the potential to unite benefits from both modalities. However, TLI setups require to be carefully designed...
Quantitative imaging biomarkers identification has become a powerful tool for predictive diagnosis given increasingly available clinical imaging data. In parallel, molecular profiles have been well documented in non-small cell lung cancers (NSCLCs). However, there has been limited studies on leveraging the two major sources for improving lung cancer computer-aided diagnosis. In this paper, we investigate...
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