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

Responsive glyco-poly(2-oxazoline)s: synthesis, cloud point tuning, and lectin binding

A new sugar-substituted 2-oxazoline monomer was prepared using the copper-catalyzed alkyne-azide cycloaddition (CuAAC) reaction. Its copolymerization with 2-ethyl-2-oxazoline as well as 2-(dec-9-enyl)-2-oxazoline, yielding well-defined copolymers with the possibility to tune the properties by thiol-ene "click" reactions, is described. Extensive solubility studies on the corresponding glycocopolymers demonstrated that the lower critical solution temperature behavior and pH-responsiveness of these copolymers can be adjusted in water and phosphate-buffered saline (PBS) depending on the choice of the thiol. By conjugation of 2,3,4,6-tetra-O-acetyl-1-thio-beta-D-glucopyranose and subsequent deprotection of the sugar moieties, the hydrophilicity of the copolymer could be increased significantly, allowing a cloud-point tuning in the physiological range. Furthermore, the binding capability of the glycosylated copoly(2-oxazoline) to concanavalin A was investigated

How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography

AbstractRecently, algorithms capable of assessing the severity of Coronary Artery Disease (CAD) in form of the Coronary Artery Disease-Reporting and Data System (CAD-RADS) grade from Coronary Computed Tomography Angiography (CCTA) scans using Deep Learning (DL) were proposed. Before considering to apply these algorithms in clinical practice, their robustness regarding different commonly used Computed Tomography (CT)-specific image formation parameters—including denoising strength, slab combination, and reconstruction kernel—needs to be evaluated. For this study, we reconstructed a data set of 500 patient CCTA scans under seven image formation parameter configurations. We select one default configuration and evaluate how varying individual parameters impacts the performance and stability of a typical algorithm for automated CAD assessment from CCTA. This algorithm consists of multiple preprocessing and a DL prediction step. We evaluate the influence of the parameter changes on the entire pipeline and additionally on only the DL step by propagating the centerline extraction results of the default configuration to all others. We consider the standard deviation of the CAD severity prediction grade difference between the default and variation configurations to assess the stability w.r.t. parameter changes. For the full pipeline we observe slight instability (± 0.226 CAD-RADS) for all variations. Predictions are more stable with centerlines propagated from the default to the variation configurations (± 0.122 CAD-RADS), especially for differing denoising strengths (± 0.046 CAD-RADS). However, stacking slabs with sharp boundaries instead of mixing slabs in overlapping regions (called true stack ± 0.313 CAD-RADS) and increasing the sharpness of the reconstruction kernel (± 0.150 CAD-RADS) leads to unstable predictions. Regarding the clinically relevant tasks of excluding CAD (called rule-out; AUC default 0.957, min 0.937) and excluding obstructive CAD (called hold-out; AUC default 0.971, min 0.964) the performance remains on a high level for all variations. Concluding, an influence of reconstruction parameters on the predictions is observed. Especially, scans reconstructed with the true stack parameter need to be treated with caution when using a DL-based method. Also, reconstruction kernels which are underrepresented in the training data increase the prediction uncertainty.</jats:p

Unfallgeschehen innerhalb bebauter Gebiete in Abhaengigkeit von Strassen- und Verkehrsbedingungen

SIGLETIB Hannover: RA 2038(34) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman