673 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
sj-docx-1-chl-10.1177_17475198231180835 – Supplemental material for Synthesis and anti-leukemia activity of phorbol 13,20-diesters and phorbol 12,13,20-triesters
No full textSupplemental material, sj-docx-1-chl-10.1177_17475198231180835 for Synthesis and anti-leukemia activity of phorbol 13,20-diesters and phorbol 12,13,20-triesters by Yan Wang, Yu Shan, Rui Feng, Siyu Wang, Linwei Li, Shu Xu, Yu Chen, Xu Feng, Jinyue Luo and Fei Liu in Journal of Chemical Research</p
An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles
No full textThis paper develops an event-related potential (ERP)-based adaptive model for the control of humanoid robot movements in an environment cluttered with obstacles based on live video feedback. This model adaptively determines the repetition number according to an individual's mental state to speed up the robot control cycle. N200 and P300 potential features increase in the frontal and occipital areas when using robot images as visual stimuli, so it is able to effectively recognize target visual stimuli by processing Fisher's linear discriminant analysis (FLDA) and to identify a subject's intention by using support vector machine (SVM), in parallel. The offline evaluations show that, compared with a nonadaptive model, the adaptive model increases the accuracy rate from 88.8% to 92.9%, a change of 4.1%, and the information transfer rate (ITR) from 41.3 to 46.3 bits/min, a change of 5.0 bits/min. Eight subjects participated in telepresence controlling a NAO humanoid robot to move in an office environment cluttered with obstacles. The successful maneuvers demonstrate that the brain-controlled humanoid robot can be applied for surveillance and exploration in unknown environments based on live video feedback, which are evaluated by using new metrics for the performance of the brain-robot interaction (BRI) system
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
Thirty-six stratagems and game theory
No full textThis Final Year Project Report serves as a documentation of the knowledge and tasks carried out by the author for his MP4079 Final Year Project in his fourth year in Nanyang Technological University.
Discovered decades ago, the Thirty-six Stratagems was employed heavily during the ancient war times in China. Today, it is widely applied, to the business world, politics, etc. Just like “The Art of War” by Sun Tzu, a lot of research has been carried out to seek further understanding of the Thirty-six Stratagems. Chapter 2 of this report will focus on and explain each of the thirty-six stratagems.
Over the years, the western theorists have developed Game Theory to model human behaviour and decision-making processes, seeking for optimal solutions, so as to attain the highest possible returns or payoffs. Chapter 3 of this report will provide an enhanced understand of Game Theory.
This report aims at merging the Chinese’s Thirty-six Stratagems with the Western’s Game Theory, with the intention of gaining an upper hand over other players, thereby increasing the chance of winning. Chapter 4 of this report will attempt to do so, and provide explanations for the connection.
Another major section of this report is to look at the different types of human personalities. Chapter 5 of this report will discuss about the 16 different personality types, highlighting each personality type’s strength and weakness.
Chapter 6 of this report will focus on the implementation of the Thirty-six Stratagems on the 16 Personality Types, providing an analysis of the different personality types that are suitable to have each of the stratagems implemented.
Chapter 7 will cover some of the possible future recommendations, applications and research to be done following this project.Bachelor of Engineering (Mechanical Engineering
DeStripe: A Self2Self Spatio-Spectral Graph Neural Network with Unfolded Hessian for Stripe Artifact Removal in Light-Sheet Microscopy
No full textEnd-to-End Learning for Image-Based Detection of Molecular Alterations in Digital Pathology
No full textIdentification of carbon responsibility factors based on energy consumption from 2005 to 2020 in China
No full textDecoupling 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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