52,518 research outputs found
2015 Update on Acute Adverse Reactions to Gadolinium based Contrast Agents in Cardiovascular MR. Large Multi-National and Multi-Ethnical Population Experience With 37788 Patients From the EuroCMR Registry
Objectives: Specifically we aim to demonstrate that the results of our earlier safety data hold true in this much larger multi-national and multi-ethnical population. Background: We sought to re-evaluate the frequency, manifestations, and severity of acute adverse reactions associated with administration of several gadolinium- based contrast agents during routine CMR on a European level. Methods: Multi-centre, multi-national, and multi-ethnical registry with consecutive enrolment of patients in 57 European centres. Results: During the current observation 37788 doses of Gadolinium based contrast agent were administered to 37788 patients. The mean dose was 24.7Â ml (range 5â80Â ml), which is equivalent to 0.123Â mmol/kg (range 0.01 - 0.3Â mmol/kg). Forty-five acute adverse reactions due to contrast administration occurred (0.12Â %). Most reactions were classified as mild (43 of 45) according to the American College of Radiology definition. The most frequent complaints following contrast administration were rashes and hives (15 of 45), followed by nausea (10 of 45) and flushes (10 of 45). The event rate ranged from 0.05Â % (linear non-ionic agent gadodiamide) to 0.42Â % (linear ionic agent gadobenate dimeglumine). Interestingly, we also found different event rates between the three main indications for CMR ranging from 0.05Â % (risk stratification in suspected CAD) to 0.22Â % (viability in known CAD). Conclusions: The current data indicate that the results of the earlier safety data hold true in this much larger multi-national and multi-ethnical population. Thus, the âoff-labelâ use of Gadolinium based contrast in cardiovascular MR should be regarded as safe concerning the frequency, manifestation and severity of acute events
Stratified decision forests for accurate anatomical landmark localization in cardiac images
Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy
Improving the performance of object detection by preserving label distribution
Object detection is a task that performs position identification and label
classification of objects in images or videos. The information obtained through
this process plays an essential role in various tasks in the field of computer
vision. In object detection, the data utilized for training and validation
typically originate from public datasets that are well-balanced in terms of the
number of objects ascribed to each class in an image. However, in real-world
scenarios, handling datasets with much greater class imbalance, i.e., very
different numbers of objects for each class , is much more common, and this
imbalance may reduce the performance of object detection when predicting unseen
test images. In our study, thus, we propose a method that evenly distributes
the classes in an image for training and validation, solving the class
imbalance problem in object detection. Our proposed method aims to maintain a
uniform class distribution through multi-label stratification. We tested our
proposed method not only on public datasets that typically exhibit balanced
class distribution but also on custom datasets that may have imbalanced class
distribution. We found that our proposed method was more effective on datasets
containing severe imbalance and less data. Our findings indicate that the
proposed method can be effectively used on datasets with substantially
imbalanced class distribution.Comment: Code is available at
https://github.com/leeheewon-01/YOLOstratifiedKFold/tree/mai
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images
A method for automatically quantifying emphysema regions using
High-Resolution Computed Tomography (HRCT) scans of patients with chronic
obstructive pulmonary disease (COPD) that does not require manually annotated
scans for training is presented. HRCT scans of controls and of COPD patients
with diverse disease severity are acquired at two different centers. Textural
features from co-occurrence matrices and Gaussian filter banks are used to
characterize the lung parenchyma in the scans. Two robust versions of multiple
instance learning (MIL) classifiers, miSVM and MILES, are investigated. The
classifiers are trained with the weak labels extracted from the forced
expiratory volume in one minute (FEV) and diffusing capacity of the lungs
for carbon monoxide (DLCO). At test time, the classifiers output a patient
label indicating overall COPD diagnosis and local labels indicating the
presence of emphysema. The classifier performance is compared with manual
annotations by two radiologists, a classical density based method, and
pulmonary function tests (PFTs). The miSVM classifier performed better than
MILES on both patient and emphysema classification. The classifier has a
stronger correlation with PFT than the density based method, the percentage of
emphysema in the intersection of annotations from both radiologists, and the
percentage of emphysema annotated by one of the radiologists. The correlation
between the classifier and the PFT is only outperformed by the second
radiologist. The method is therefore promising for facilitating assessment of
emphysema and reducing inter-observer variability.Comment: Accepted at PLoS ON
Initial Draft of a Possible Declarative Semantics for the Language
This article introduces a preliminary declarative semantics for a subset of the language Xcerpt (so-called
grouping-stratifiable programs) in form of a classical (Tarski style) model theory, adapted to the specific
requirements of Xcerptâs constructs (e.g. the various aspects of incompleteness in query terms, grouping
constructs in rule heads, etc.). Most importantly, the model theory uses term simulation as a replacement
for term equality to handle incomplete term specifications, and an extended notion of substitutions in
order to properly convey the semantics of grouping constructs. Based upon this model theory, a fixpoint
semantics is also described, leading to a first notion of forward chaining evaluation of Xcerpt program
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