324 research outputs found
DisQ: Disentangling Quantitative MRI Mapping of the Heart
No full textQuantitative MRI (qMRI) of the heart has become an important clinical tool for examining myocardial tissue properties. Because heart is a moving object, it is usually imaged with electrocardiogram and respiratory gating during acquisition, to “freeze” its motion. In reality, gating is more-often-than-not imperfect given the heart rate variability and nonideal breath-hold. qMRI of the heart, consequently, is characteristic of varying image contrast as well as residual motion, the latter compromising the quality of quantitative mapping. Motion correction is an important step prior to parametric mapping, however, a long-standing difficulty for registering the dynamic sequence is that the contrast across frames varies wildly: depending on the acquisition scheme some frames can have extremely poor contrast, which fails both traditional optimization-based and modern learning-based registration methods. In this work, we propose a novel framework named DisQ, which Disentangles Quantitative mapping sequences into the latent space of contrast and anatomy, fully unsupervised. The disentangled latent spaces serve for the purpose of generating a series of images with identical contrast, which enables easy and accurate registration of all frames. We applied our DisQ method to the modified Look-Locker inversion recovery (MOLLI) sequence, and demonstrated improved performance of T1 mapping. In addition, we showed the possibility of generating a dynamic series of baseline images with exactly the same shape, strictly registered and perfectly “frozen". Our proposed DisQ methodology readily extends to other types of cardiac qMRI such as T2 mapping and perfusion.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Medical Imagin
A study on Chinese linguistic landscapes from the perspective of positioning theory
No full textPositioning theory is an analytic framework to explore the identity-forming of interlocutors in communications. In this study the author adopts positioning theory to analyze the Chinese linguistic landscapes in the city of Winnipeg in order to explore the positional identities of the Chinese population in western societies. The results show that Chinese Winnipeggers: a) assign high values to the English language but probably not equally to the French language; b) tend to accept western cultures and learn the English language; c) like to express their ethnic identities and inherit and carry the traditional Chinese cultures forward; d) attach importance to the marketing value of English and some Asian languages (like Korean and Vietnamese); e) are supported by some local businesses regarding languages; f) gradually replace the “hostile relationships” between old and new Chinese immigrants with “friendships and partnerships”; and g) identify Chinese newcomers as investors who need and are eager to buy educational products. The writer of this study exposes the power dynamics between the Chinese immigrants and mainstream society in Winnipeg, reveals the relationships between various Chinese sub-groups created during differing waves of immigration, and builds connections among time, space, and people. The functions and roles of linguistic landscapes in language education are discussed in the study.October 202
Electrocardiographic Imaging
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Electrocardiographic Imaging
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Efficient Bayesian Uncertainty Estimation for nnU-Net
No full textThe self-configuring nnU-Net has achieved leading performance in a large
range of medical image segmentation challenges. It is widely considered as the
model of choice and a strong baseline for medical image segmentation. However,
despite its extraordinary performance, nnU-Net does not supply a measure of
uncertainty to indicate its possible failure. This can be problematic for
large-scale image segmentation applications, where data are heterogeneous and
nnU-Net may fail without notice. In this work, we introduce a novel method to
estimate nnU-Net uncertainty for medical image segmentation. We propose a
highly effective scheme for posterior sampling of weight space for Bayesian
uncertainty estimation. Different from previous baseline methods such as Monte
Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does
not require a variational architecture and keeps the original nnU-Net
architecture intact, thereby preserving its excellent performance and ease of
use. Additionally, we boost the segmentation performance over the original
nnU-Net via marginalizing multi-modal posterior models. We applied our method
on the public ACDC and M&M datasets of cardiac MRI and demonstrated improved
uncertainty estimation over a range of baseline methods. The proposed method
further strengthens nnU-Net for medical image segmentation in terms of both
segmentation accuracy and quality control
Stochastic optimization with decisions truncated by random variables and its applications in operations
No full textWe study stochastic optimization problems with decisions truncated by random variables and its applications in operations management. The technical difficulty of these problems is that the optimization problem is not convex due to the truncation. We develop a transformation technique to convert the original non-convex optimization problems to convex ones while preservation some desired structural properties, which are useful for characterizing optimal decision policies and conducting comparative statics. Our transformation technique provides a unified approach to analyze a broad class of models in inventory control and revenue management. In additional, we develop efficient algorithms to solve the transformed stochastic optimization problem.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-08-01The student, Xiangyu Gao, accepted the attached license on 2017-06-23 at 09:21.The student, Xiangyu Gao, submitted this Dissertation for approval on 2017-06-23 at 09:23.This Dissertation was approved for publication on 2017-06-26 at 13:18.DSpace SAF Submission Ingestion Package generated from Vireo submission #11220 on 2017-09-29 at 11:13:44Made available in DSpace on 2017-09-29T16:39:09Z (GMT). No. of bitstreams: 3
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Previous issue date: 2017-06-26Embargo set by: Colleen Fallaw for item 103381
Lift date: 2019-09-29T16:39:52Z
Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemEmbargo set by: Colleen Fallaw for item 103381
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Reason: Author requested closed access (OA after 2yrs) in Vireo ETD systemLimited Restriction Lifted for Item 103381 on 2019-09-30T09:15:26Z
Decoupling Predictions in Distributed Learning for Multi-Center Left Atrial MRI Segmentation
No full textDistributed learning has shown great potential in medical image analysis. It
allows to use multi-center training data with privacy protection. However, data
distributions in local centers can vary from each other due to different
imaging vendors, and annotation protocols. Such variation degrades the
performance of learning-based methods. To mitigate the influence, two groups of
methods have been proposed for different aims, i.e., the global methods and the
personalized methods. The former are aimed to improve the performance of a
single global model for all test data from unseen centers (known as generic
data); while the latter target multiple models for each center (denoted as
local data). However, little has been researched to achieve both goals
simultaneously. In this work, we propose a new framework of distributed
learning that bridges the gap between two groups, and improves the performance
for both generic and local data. Specifically, our method decouples the
predictions for generic data and local data, via distribution-conditioned
adaptation matrices. Results on multi-center left atrial (LA) MRI segmentation
showed that our method demonstrated superior performance over existing methods
on both generic and local data. Our code is available at
https://github.com/key1589745/decouple_predictComment: Accepted by MICCAI 202
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