Thrombosis remains a global health concern, necessitating research into its underlying mechanisms. Utilizing a high-speed bright-field microscope based on optical frequency-division multiplexing and microfluidics, we performed image-based single-cell profiling and temporal monitoring of circulating platelet aggregates that are the precursors to thrombosis. Our analysis encompassed 41 thrombosis patients, 110 COVID-19 patients, and 11 healthy individuals. By investigating the morphological changes of platelet aggregates under the influence of thrombosis, COVID-19, and COVID-19 vaccination, we observed distinct morphological alterations in platelet aggregates across different conditions, which shed light on the interplay between platelet aggregation and thrombotic events.
There is widespread concern about the safety of COVID-19 vaccinations related to platelet hyperactivity. However, their long-term influence on platelet activity remains unknown. We address this issue by applying a high-speed bright-field microscope based on optical frequency-division multiplexing and microfluidics for massive image-based analysis. We performed image-based single-cell profiling and temporal monitoring of circulating platelet aggregates in the blood samples of healthy human participants before and after they received three vaccination doses over a nearly one-year period. The results demonstrate no significant or persistent change in platelet activity after vaccine doses.
Our purpose in this study is to investigate whether a recently introduced image transform, denoted as the Radon cumulative distribution transform (R-CDT), can be used as a viable preprocessing step for augmenting the robustness of training end-to-end systems with fewer training samples. In order to assess the ability of the R-CDT to perform this aim, we identified a standard machine learning dataset, MNIST, and a preliminary dataset comprised of liver cell nuclei images derived from one of two tissue types: benign or malignant tumor lesions. We separated the data into training and testing sets with 20% of the total data used for testing across all training set size conditions. To simulate a range of limited size of training examples, we randomly generated data subsets ranging in size from 80% to 0.8% of the total dataset size to be used for training. Linear classification algorithms were implemented via logistic regression and a support vector machine model with a linear kernel on both the raw images and images transformed via the R-CDT. Additionaly, non-linear classification accuracies were assessed via comparing the R-CDT paired with a shallow CNN and using a deep CNN to classify images. Results indicate that classification in Radon cumulative distribution transform space outperforms classification in image space in conditions of limited data, as one is likely to see in medical imaging.
We propose using novel imaging biomarkers for detecting mammographically-occult (MO) cancer in women with dense breast tissue. MO cancer indicates visually occluded, or very subtle, cancer that radiologists fail to recognize as a sign of cancer. We used the Radon Cumulative Distribution Transform (RCDT) as a novel image transformation to project the difference between left and right mammograms into a space, increasing the detectability of occult cancer. We used a dataset of 617 screening full-field digital mammograms (FFDMs) of 238 women with dense breast tissue. Among 238 women, 173 were normal with 2 – 4 consecutive screening mammograms, 552 normal mammograms in total, and the remaining 65 women had an MO cancer with a negative screening mammogram. We used Principal Component Analysis (PCA) to find representative patterns in normal mammograms in the RCDT space. We projected all mammograms to the space constructed by the first 30 eigenvectors of the RCDT of normal cases. Under 10-fold crossvalidation, we conducted quantitative feature analysis to classify normal mammograms and mammograms with MO cancer. We used receiver operating characteristic (ROC) analysis to evaluate the classifier’s output using the area under the ROC curve (AUC) as the figure of merit. Four eigenvectors were selected via a feature selection method. The mean and standard deviation of the AUC of the trained classifier on the test set were 0.74 and 0.08, respectively. In conclusion, we utilized imaging biomarkers to highlight differences between left and right mammograms to detect MO cancer using novel imaging transformation.
Modern advancements in imaging devices have enabled us to explore the subcellular structure of living organisms and extract vast amounts of information. However, interpreting the biological information mined in the captured images is not a trivial task. Utilizing predetermined numerical features is usually the only hope for quantifying this information. Nonetheless, direct visual or biological interpretation of results obtained from these selected features is non-intuitive and difficult. In this paper, we describe an automatic method for modeling visual variations in a set of images, which allows for direct visual interpretation of the most significant differences, without the need for predefined features. The method is based on a linearized version of the continuous optimal transport (OT) metric, which provides a natural linear embedding for the image data set, in which linear combination of images leads to a visually meaningful image. This enables us to apply linear geometric data analysis techniques such as principal component analysis and linear discriminant analysis in the linearly embedded space and visualize the most prominent modes, as well as the most discriminant modes of variations, in the dataset. Using the continuous OT framework, we are able to analyze variations in shape and texture in a set of images utilizing each image at full resolution, that otherwise cannot be done by existing methods. The proposed method is applied to a set of nuclei images segmented from Feulgen stained liver tissues in order to investigate the major visual differences in chromatin distribution of Fetal-Type Hepatoblastoma (FHB) cells compared to the normal cells.
diagnostic standard is a pleural biopsy with subsequent histologic examination of the tissue demonstrating invasion by
the tumor. The diagnostic tissue is obtained through thoracoscopy or open thoracotomy, both being highly invasive
procedures. Thoracocenthesis, or removal of effusion fluid from the pleural space, is a far less invasive procedure that
can provide material for cytological examination. However, it is insufficient to definitively confirm or exclude the
diagnosis of malignant mesothelioma, since tissue invasion cannot be determined. In this study, we present a
computerized method to detect and classify malignant mesothelioma based on the nuclear chromatin distribution from
digital images of mesothelial cells in effusion cytology specimens. Our method aims at determining whether a set of
nuclei belonging to a patient, obtained from effusion fluid images using image segmentation, is benign or malignant, and
has a potential to eliminate the need for tissue biopsy. This method is performed by quantifying chromatin morphology
of cells using the optimal transportation (Kantorovich–Wasserstein) metric in combination with the modified Fisher
discriminant analysis, a k-nearest neighborhood classification, and a simple voting strategy. Our results show that we can
classify the data of 10 different human cases with 100% accuracy after blind cross validation. We conclude that nuclear
structure alone contains enough information to classify the malignant mesothelioma. We also conclude that the
distribution of chromatin seems to be a discriminating feature between nuclei of benign and malignant mesothelioma
cells.
One method of modelling respiratory motion of the abdomen is to acquire CT images at different points in the respiratory cycle and develop a deformation model that gives a mapping between corresponding anatomical points in the images. In this work, we use such a method, and the target application is radiosurgery, particularly radiosurgical treatment of lesions that move during respiration, for example those in the liver, lung, or pancreas. In order to accurately calculate the treatment dose, it is necessary to have a good deformation map both globally and locally (in the vicinity of the treatment target). We use a dual-resolution method in order to allow a more accurate deformation model to be computed in the region of interest. We also introduce a tissue stiffness constraint, along with an application of matrix algebra that allows this constraint to be applied in an effective way with respect to the control point values.
KEYWORDS: Signal to noise ratio, Magnetic resonance imaging, Interference (communication), Image segmentation, Magnetism, Diffusion, Data acquisition, Error analysis, Monte Carlo methods, Data analysis
Signal intensity in magnetic resonance images (MRIs) is affected by random noise. Assessing noise-induced signal variance is important for controlling image quality. Knowledge of signal variance is required for correctly computing the chi-square value, a measure of goodness of fit, when fitting signal data to estimate quantitative parameters such as T1 and T2 relaxation times or diffusion tensor elements. Signal variance can be estimated from measurements of the noise variance in an object- and ghost-free region of the image background. However, identifying a large homogeneous region automatically is problematic. In this paper, a novel, fully automated approach for estimating the noise-induced signal variance in magnitude-reconstructed MRIs is proposed. This approach is based on the histogram analysis of the image signal intensity, explicitly by extracting the peak of the underlining Rayleigh distribution that would characterize the distribution of the background noise. The peak is extracted using a nonparametric univariate density estimation like the Parzen window density estimation; the corresponding peak position is shown here to be the expected signal variance in the object. The proposed method does not depend on prior foreground segmentation, and only one image with a small amount of background is required when the signal-to-noise ratio (SNR) is greater than three. This method is applicable to magnitude-reconstructed MRIs, though diffusion tensor (DT)-MRI is used here to demonstrate the approach.
Image registration refers to the process of finding the spatial correspondence between two or more images. This is usually done by applying a spatial transformation, computed automatic or manually, to a given image using a continuous image model computed either with interpolation or approximation methods. We show that noise induced signal variance in interpolated images differs significantly from the signal variance of the original images in native space. We describe a simple approach to compute the signal variance in registered images based on the signal variance and covariance of the original images, the spatial transformations computed by the registration procedure, and the interpolation or approximation kernel chosen. Our approach is applied to diffusion tensor (DT) MRI data. We show that incorrect noise variance estimates in registered diffusion weighted images can affect the estimated DT parameters, their estimated uncertainty, as well as indices of goodness of fit such as chi-square maps. In addition to DT-MRI, we believe that this methodology would be useful any time parameter extraction methods are applied to registered or interpolated data.
Measuring the similarity between discretely sampled intensity values of different images as a function of geometric transformations is necessary for performing automatic image registration. Arbitrary spatial transformations require a continuous model for the intensity values of the discrete images. Because of computation cost most researchers choose to use low order basis functions, such as the linear hat function or low order B-splines, to model the discrete images. Using the theory of random processes we show that low order interpolators cause undesirable local optima artifacts in similarity measures based on the L2 norm, linear correlation coefficient, and mutual information. We show how these artifacts can be significantly reduced, and at times completely eliminated, by using sinc approximating kernels.
Nonrigid registration of medical images is an important procedure in many aspects of current biomedical and bioengineering research. For example, it is a necessary step for studying the variation of biological tissue properties, such as shape or diffusion properties across population, compute population averages, or atlas-based segmentation. Recently we have introduced the Adaptive Bases registration algorithm as a general method for performing nonrigid registration of medical images and we have shown it to be faster and more accurate than existing algorithms of the same class. The overall properties of the Adaptive Bases algorithm are reviewed here and the method is validated on applications that include the computation of average images, atlas based segmentation, and motion correction of video images. Results show the Adaptive Bases algorithm to be capable of producing high quality nonrigid matches for the applications above mentioned.
KEYWORDS: Image registration, Detection and tracking algorithms, Optimization (mathematics), Medical imaging, Image resolution, 3D acquisition, Magnetic resonance imaging, 3D image processing, Image fusion, Image segmentation
A number of methods have been proposed recently to solve nonrigid registration problems. One of these involves optimizing a Mutual Information (MI) based objective function over a regularly spaced grid of basis functions. This approach has produced good results but its computational complexity is inversely proportional to the compliance of the transformation. Transformations able to register two high resolution images on a very local scale need a large number of degrees of freedom. Finding an optimum in such a search space is lengthy and prone to convergence to local maxima. In this paper, we propose a modification to this class of algorithms that reduces their computational complexity and improves their convergence properties. The approach we propose adapts the compliance of the transformation locally. Registration is achieved iteratively, from a coarse to a fine scale. At each level, the gradient of the cost function with respect to the coefficients of a set of compactly supported radial basis functions spread over a regular grid is used to estimate a local adaptation of the grid. Optimization is then conducted over the estimated irregular grid one region at a time. Results show the advantage of the approach we propose over a method without local grid adaptation.
When performing registrations, it is often crucial to maintain certain structure of the template data T - the data being deformed into the subject data S - as well as to keep the deformation field smooth. Current approaches to registration often impose smoothness through heuristic means, but building it into the model has proven to be more difficult due mainly to computational constraints.
High-resolution optical mapping is an emerging technique to record the activation and propagation of transmembrane potential on the surface of cardiac tissues. Important electrodynamic information previously not available from extracellular electric recording could be extracted from these detailed optical recordings. The noise contamination in the images is a major obstacle that prohibits higher level of information extraction. Because the patterns of interest contain sharp wavefronts and structures that we wish to detect and track in a series of frames, we seek to perform denoising based on wavelet decomposition approaches. Among the wavelet denoise methods that were tested in this preliminary study, the wavelet packet produced the best results that could be extended to denoise the entire image sequence for multi-dimensional information processing.
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