11,955 research outputs found
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images
is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We
propose an algorithm that extracts coronary artery centerlines in CCTA using a
convolutional neural network (CNN).
A 3D dilated CNN is trained to predict the most likely direction and radius
of an artery at any given point in a CCTA image based on a local image patch.
Starting from a single seed point placed manually or automatically anywhere in
a coronary artery, a tracker follows the vessel centerline in two directions
using the predictions of the CNN. Tracking is terminated when no direction can
be identified with high certainty.
The CNN was trained using 32 manually annotated centerlines in a training set
consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery
Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08
challenge showed that extracted centerlines had an average overlap of 93.7%
with 96 manually annotated reference centerlines. Extracted centerline points
were highly accurate, with an average distance of 0.21 mm to reference
centerline points. In a second test set consisting of 50 CCTA scans, 5,448
markers in the coronary arteries were used as seed points to extract single
centerlines. This showed strong correspondence between extracted centerlines
and manually placed markers. In a third test set containing 36 CCTA scans,
fully automatic seeding and centerline extraction led to extraction of on
average 92% of clinically relevant coronary artery segments.
The proposed method is able to accurately and efficiently determine the
direction and radius of coronary arteries. The method can be trained with
limited training data, and once trained allows fast automatic or interactive
extraction of coronary artery trees from CCTA images.Comment: Accepted in Medical Image Analysi
Contextual Out-of-Domain Utterance Handling With Counterfeit Data Augmentation
Neural dialog models often lack robustness to anomalous user input and
produce inappropriate responses which leads to frustrating user experience.
Although there are a set of prior approaches to out-of-domain (OOD) utterance
detection, they share a few restrictions: they rely on OOD data or multiple
sub-domains, and their OOD detection is context-independent which leads to
suboptimal performance in a dialog. The goal of this paper is to propose a
novel OOD detection method that does not require OOD data by utilizing
counterfeit OOD turns in the context of a dialog. For the sake of fostering
further research, we also release new dialog datasets which are 3 publicly
available dialog corpora augmented with OOD turns in a controllable way. Our
method outperforms state-of-the-art dialog models equipped with a conventional
OOD detection mechanism by a large margin in the presence of OOD utterances.Comment: ICASSP 201
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