Handling Label Uncertainty on the Example of Automatic Detection of Shepherd's Crook RCA in Coronary CT Angiography

Coronary artery disease (CAD) is often treated minimally invasively with a catheter being inserted into the diseased coronary vessel. If a patient exhibits a Shepherd's Crook (SC) Right Coronary Artery (RCA) - an anatomical norm variant of the coronary vasculature - the complexity of this procedure is increased. Automated reporting of this variant from coronary CT angiography screening would ease prior risk assessment. We propose a 1D convolutional neural network which leverages a sequence of residual dilated convolutions to automatically determine this norm variant from a prior extracted vessel centerline. As the SC RCA is not clearly defined with respect to concrete measurements, labeling also includes qualitative aspects. Therefore, 4.23% samples in our dataset of 519 RCA centerlines were labeled as unsure SC RCAs, with 5.97% being labeled as sure SC RCAs. We explore measures to handle this label uncertainty, namely global/model-wise random assignment, exclusion, and soft label assignment. Furthermore, we evaluate how this uncertainty can be leveraged for the determination of a rejection class. With our best configuration, we reach an area under the receiver operating characteristic curve (AUC) of 0.938 on confident labels. Moreover, we observe an increase of up to 0.020 AUC when rejecting 10% of the data and leveraging the labeling uncertainty information in the exclusion process.Comment: Accepted at ISBI 202

Пигменты для окрашивания строительных материалов

Получены керамические пигменты с использованием техногенного кремнезёмсодержащего отхода - ванадиевого катализатора. В составе пигментов наряду с преобладающей фазой муллита идентифицируется корунд. По результатам рентгенофазового анализа установлено, что оксиды хрома и железа встраиваются в структуру вплоть до концентрации 10 мас. % и не выделяются в свободном виде. В кобальтсодержащих пигментах образуется шпинель CoAl2O4. Разработанные пигменты выдерживают температуру обжига 1200 ?С, их можно рекомендовать для получения керамических красок, цветных глазурей, для окрашивания строительных материалов

The Technome - a predictive internal calibration approach for quantitative imaging biomarker research

The goal of radiomics is to convert medical images into a minable data space by extraction of quantitative imaging features for clinically relevant analyses, e.g. survival time prediction of a patient. One problem of radiomics from computed tomography is the impact of technical variation such as reconstruction kernel variation within a study. Additionally, what is often neglected is the impact of inter-patient technical variation, resulting from patient characteristics, even when scan and reconstruction parameters are constant. In our approach, measurements within 3D regions-of-interests (ROI) are calibrated by further ROIs such as air, adipose tissue, liver, etc. that are used as control regions (CR). Our goal is to derive general rules for an automated internal calibration that enhance prediction, based on the analysed features and a set of CRs. We define qualification criteria motivated by status-quo radiomics stability analysis techniques to only collect information from the CRs which is relevant given a respective task. These criteria are used in an optimisation to automatically derive a suitable internal calibration for prediction tasks based on the CRs. Our calibration enhanced the performance for centrilobular emphysema prediction in a COPD study and prediction of patients’ one-year-survival in an oncological study

Myocardial motion and deformation analysis from echocardiograms

Echocardiography is a widely used imaging technique to examine myocardial function in patients with known or suspected heart disease. The analysis of ventricular wall motion and deformation, in particular, allows to assess the extent of myocardial ischemia and infarction. In clinical practice, the analysis mainly relies on visual inspection or manual measurements by experienced cardiologists. Manual methods are tedious and time-consuming, and visual assessment leads to qualitative and subjective diagnoses that suffer from considerable inter- and intraobserver variability. Automating the analysis of echocardiographic images is therefore highly desirable but also challenging because of the low image quality and the high amount of speckle noise. In this thesis, we propose a framework for robust and quantitative analysis of echocardiographic sequences. We make the following key contributions: Motion and Deformation Analysis—We propose a novel optical-flow-based algorithm to estimate ventricular wall motion from B-mode echocardiograms. To account for typical heart motions such as contraction/expansion and shear, we use a local affine model for the velocity in space and time. An attractive feature of the affine motion model is that it gives also access to local strain rate parameters that describe local myocardial deformation such as wall thickening. The motion parameters are estimated in the least-squares sense within a sliding spatio-temporal B-spline window. The estimation of large motions is made possible through the use of a coarse-to-fine multi-scale strategy, which also adds robustness to the method. Computational Efficiency—We introduce the notion of multiresolution moment filters, a novel filtering scheme to compute local weighted geometric moments efficiently at dyadic scales by using a wavelet-like algorithm. Beyond their application in motion analysis, we demonstrate their usefulness for image denoising and feature extraction. Multi-Modality—We extend the proposed motion analysis algorithm by integrating directional, Doppler-based velocity measurements. The exploitation of two ultrasound modalities, i.e., B-mode and tissue Doppler, renders the method more accurate and robust. Visualization—We display diagnostically meaningful motion data inside a user-defined region of interest that is tracked in time. Myocardial inward and outward motion is visualized by color coding the radial motion component with respect to the ventricular center. Two-dimensional strain rate information is superimposed in the form of deforming ellipses. The display allows a more intuitive and simplified identification of regions with abnormal motion patterns. Validation—The proposed method is validated on 1) synthetic data, 2) real ultrasound phantom data, and 3) clinical echocardiograms. A large-scale validation study that includes 114 patients confirms its ability to detect and quantify wall motion abnormalities

Multigrid Image Reconstruction From Arbitrarily Spaced Samples

We propose a novel multiresolution-multigrid based signal reconstruction method from arbitrarily spaced samples. The signal is reconstructed on a uniform grid using B-splines basis functions. The computation of spline weights is formulated as a variational problem. Specifically, we minimize a cost that is a weighted sum of two terms: (i) the sum of squared errors at the specified points; (ii) a quadratic functional that penalizes the lack of smoothness. The problem is equivalent to solving a very large system of linear equations, with the dimension equal to the number of grid points. We develop a computationally efficient multiresolution-multigrid scheme for solving the system. We demonstrate the method with image reconstruction from contour points

BIMODAL MYOCARDIAL MOTION ANALYSIS FROM B-MODE AND TISSUE DOPPLER ULTRASOUND

ABSTRACT We present a new method for estimating heart motion from two-dimensional echocardiographic sequences by exploiting two ultrasound modalities: B-mode and tissue Doppler. The algorithm estimates a two-dimensional velocity field locally by using a spatial affine velocity model inside a sliding window. Within each window, we minimize a local cost function that is composed of two quadratic terms: an optical flow constraint that involves the B-mode data and a constraint that enforces the agreement of the velocity field with the directional tissue Doppler measurements. The relative influence of the two different modalities to the resulting solution is controlled by an adjustable weighting parameter. Robustness is achieved by a coarse-to-fine multi-scale approach. The method was tested on synthetic ultrasound data and validated by a rotating phantom experiment. First applications to clinical echocardiograms give promising results

MULTIRESOLUTION MOMENT FILTERS

We define multi-scale moments that are estimated locally by analyzing the image through a sliding window at multiple scales. When the analysis window satisfies a two-scale relation, we prove that these moments can be computed very efficiently using a multiresolution wavelet-like algorithm. We also show that B-spline windows are best suited for this kind of analysis because, in addition to being refinable, they are positive, symmetric and very nearly isotropic. We present two applications of our method. The first is a feature extraction method for detecting strands of DNA in noisy cryoelectron-micrographs. The second is an extension of the Lucas-Kanade optical flow algorithm that assumes a local affine model of the motion field. The results obtained in both cases are very promising. 1