79 research outputs found
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An Alternative Approach to Spectrum-Based Atherosclerotic Plaque Characterization Techniques Using Intravascular Ultrasound (IVUS) Backscattered Signals
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Texture-Driven Coronary Artery Plaque Characterization Using Wavelet Packet Signatures
High-frequency ultrasound transducers are being widely used to generate high resolution, real time, cross-sectional images of the coronary arteries. In this paper, we present a robust unsupervised texture-derived technique based on multi-channel wavelet frames to delineate atherosclerotic plaque compositions. The intravascular ultrasound (IVUS) signals were acquired from coronary arteries dissected from 32 diseased cadaver hearts employing 40 MHz mechanically rotating, single-element transducers. The wavelet packet representations were classified using a K- means clustering algorithm to generate IVUS-histology color maps (IV-HCMs) and categorize tissues in lipidic, fibrotic and calcified. Finally, two independent observers evaluated the results contrasting the histology images corresponding to the IV-HCMs. Our results show that the proposed algorithm may have great potential as an alternative to existing spectrum-based classification techniques
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Automatic detection of blood versus non-blood regions on intravascular ultrasound (IVUS) images using wavelet packet signatures
Intravascular ultrasound (IVUS) has been proven a reliable imaging modality that is widely employed in cardiac interventional procedures. It can provide morphologic as well as pathologic information on the occluded plaques in the coronary arteries. In this paper, we present a new technique using wavelet packet analysis that differentiates between blood and non-blood regions on the IVUS images. We utilized the multi-channel texture segmentation algorithm based on the discrete wavelet packet frames (DWPF). A k-mean clustering algorithm was deployed to partition the extracted textural features into blood and non-blood in an unsupervised fashion. Finally, the geometric and statistical information of the segmented regions was used to estimate the closest set of pixels to the lumen border and a spline curve was fitted to the set. The presented algorithm may be helpful in delineating the lumen border automatically and more reliably prior to the process of plaque characterization, especially with 40 MHz transducers, where appearance of the red blood cells renders the border detection more challenging, even manually. Experimental results are shown and they are quantitatively compared with manually traced borders by an expert. It is concluded that our two dimensional (2-D) algorithm, which is independent of the cardiac and catheter motions performs well in both in-vivo and in-vitro cases
Search for atoxic cereals: a single blind, cross-over study on the safety of a single dose of Triticum monococcum, in patients with celiac disease
Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data
Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe
Classification of Blood Regions in IVUS Images Using Three Dimensional Brushlet Expansions
The presence of non-coherent blood speckle patterns makes the assessment of lumen size in intravascular ultrasound (IVUS) images a challenging problem, especially for images acquired with recent high frequency transducers. In this paper, we present a robust three-dimensional (3D) feature extraction algorithm based on the expansion of IVUS cross-sectional images and pullback directions onto an orthonormal complex brushlet basis. Several features are selected from the projections of low-frequency 3D brushlet coefficients. These representations are used as inputs to a neural network that is trained to classify blood maps on IVUS images. We evaluated the algorithm performance using repeated randomized experiments on sub-samples to validate the quantification of the blood maps when compared to expert manual tracings of 258 frames collected from three patients. Our results demonstrate that the proposed features extracted in the brushlet domain capture well the non-coherent structures of blood speckle, enabling identification of blood pools and enhancement of the lumen area
A state of the art review on segmentation algorithms in intravascular ultrasound (IVUS) images
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