25 research outputs found
AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs
Left atrial (LA) segmentation from late gadolinium enhanced magnetic
resonance imaging (LGE MRI) is a crucial step needed for planning the treatment
of atrial fibrillation. However, automatic LA segmentation from LGE MRI is
still challenging, due to the poor image quality, high variability in LA
shapes, and unclear LA boundary. Though deep learning-based methods can provide
promising LA segmentation results, they often generalize poorly to unseen
domains, such as data from different scanners and/or sites. In this work, we
collect 210 LGE MRIs from different centers with different levels of image
quality. To evaluate the domain generalization ability of models on the LA
segmentation task, we employ four commonly used semantic segmentation networks
for the LA segmentation from multi-center LGE MRIs. Besides, we investigate
three domain generalization strategies, i.e., histogram matching, mutual
information based disentangled representation, and random style transfer, where
a simple histogram matching is proved to be most effective.Comment: 10 pages, 4 figures, MICCAI202
Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly
used to visualize and quantify left atrial (LA) scars. The position and extent
of scars provide important information of the pathophysiology and progression
of atrial fibrillation (AF). Hence, LA scar segmentation and quantification
from LGE MRI can be useful in computer-assisted diagnosis and treatment
stratification of AF patients. Since manual delineation can be time-consuming
and subject to intra- and inter-expert variability, automating this computing
is highly desired, which nevertheless is still challenging and
under-researched.
This paper aims to provide a systematic review on computing methods for LA
cavity, wall, scar and ablation gap segmentation and quantification from LGE
MRI, and the related literature for AF studies. Specifically, we first
summarize AF-related imaging techniques, particularly LGE MRI. Then, we review
the methodologies of the four computing tasks in detail, and summarize the
validation strategies applied in each task. Finally, the possible future
developments are outlined, with a brief survey on the potential clinical
applications of the aforementioned methods. The review shows that the research
into this topic is still in early stages. Although several methods have been
proposed, especially for LA segmentation, there is still large scope for
further algorithmic developments due to performance issues related to the high
variability of enhancement appearance and differences in image acquisition.Comment: 23 page
MAD: Modality Agnostic Distance Measure for Image Registration
Multi-modal image registration is a crucial pre-processing step in many
medical applications. However, it is a challenging task due to the complex
intensity relationships between different imaging modalities, which can result
in large discrepancy in image appearance. The success of multi-modal image
registration, whether it is conventional or learning based, is predicated upon
the choice of an appropriate distance (or similarity) measure. Particularly,
deep learning registration algorithms lack in accuracy or even fail completely
when attempting to register data from an "unseen" modality. In this work, we
present Modality Agnostic Distance (MAD), a deep image distance}] measure that
utilises random convolutions to learn the inherent geometry of the images while
being robust to large appearance changes. Random convolutions are
geometry-preserving modules which we use to simulate an infinite number of
synthetic modalities alleviating the need for aligned paired data during
training. We can therefore train MAD on a mono-modal dataset and successfully
apply it to a multi-modal dataset. We demonstrate that not only can MAD
affinely register multi-modal images successfully, but it has also a larger
capture range than traditional measures such as Mutual Information and
Normalised Gradient Fields
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information
Deep learning (DL)-based models have demonstrated good performance in medical
image segmentation. However, the models trained on a known dataset often fail
when performed on an unseen dataset collected from different centers, vendors
and disease populations. In this work, we present a random style transfer
network to tackle the domain generalization problem for multi-vendor and center
cardiac image segmentation. Style transfer is used to generate training data
with a wider distribution/ heterogeneity, namely domain augmentation. As the
target domain could be unknown, we randomly generate a modality vector for the
target modality in the style transfer stage, to simulate the domain shift for
unknown domains. The model can be trained in a semi-supervised manner by
simultaneously optimizing a supervised segmentation and an unsupervised style
translation objective. Besides, the framework incorporates the spatial
information and shape prior of the target by introducing two regularization
terms. We evaluated the proposed framework on 40 subjects from the M\&Ms
challenge2020, and obtained promising performance in the segmentation for data
from unknown vendors and centers.Comment: 11 page
ICoNIK: Generating Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit Representations in k-Space
Motion-resolved reconstruction for abdominal magnetic resonance imaging (MRI)
remains a challenge due to the trade-off between residual motion blurring
caused by discretized motion states and undersampling artefacts. In this work,
we propose to generate blurring-free motion-resolved abdominal reconstructions
by learning a neural implicit representation directly in k-space (NIK). Using
measured sampling points and a data-derived respiratory navigator signal, we
train a network to generate continuous signal values. To aid the regularization
of sparsely sampled regions, we introduce an additional informed correction
layer (ICo), which leverages information from neighboring regions to correct
NIK's prediction. Our proposed generative reconstruction methods, NIK and
ICoNIK, outperform standard motion-resolved reconstruction techniques and
provide a promising solution to address motion artefacts in abdominal MRI
EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging without External Trackers
Ultrasound (US) is the most widely used fetal imaging technique. However, US
images have limited capture range, and suffer from view dependent artefacts
such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a
high-resolution volume can extend the field of view and remove image artefacts,
which is useful for retrospective analysis including population based studies.
However, such volume reconstructions require information about relative
transformations between probe positions from which the individual volumes were
acquired. In prenatal US scans, the fetus can move independently from the
mother, making external trackers such as electromagnetic or optical tracking
unable to track the motion between probe position and the moving fetus. We
provide a novel methodology for image-based tracking and volume reconstruction
by combining recent advances in deep learning and simultaneous localisation and
mapping (SLAM). Tracking semantics are established through the use of a
Residual 3D U-Net and the output is fed to the SLAM algorithm. As a proof of
concept, experiments are conducted on US volumes taken from a whole body fetal
phantom, and from the heads of real fetuses. For the fetal head segmentation,
we also introduce a novel weak annotation approach to minimise the required
manual effort for ground truth annotation. We evaluate our method
qualitatively, and quantitatively with respect to tissue discrimination
accuracy and tracking robustness.Comment: MICCAI Workshop on Perinatal, Preterm and Paediatric Image analysis
(PIPPI), 201
A skeletonization algorithm for gradient-based optimization
The skeleton of a digital image is a compact representation of its topology,
geometry, and scale. It has utility in many computer vision applications, such
as image description, segmentation, and registration. However, skeletonization
has only seen limited use in contemporary deep learning solutions. Most
existing skeletonization algorithms are not differentiable, making it
impossible to integrate them with gradient-based optimization. Compatible
algorithms based on morphological operations and neural networks have been
proposed, but their results often deviate from the geometry and topology of the
true medial axis. This work introduces the first three-dimensional
skeletonization algorithm that is both compatible with gradient-based
optimization and preserves an object's topology. Our method is exclusively
based on matrix additions and multiplications, convolutional operations, basic
non-linear functions, and sampling from a uniform probability distribution,
allowing it to be easily implemented in any major deep learning library. In
benchmarking experiments, we prove the advantages of our skeletonization
algorithm compared to non-differentiable, morphological, and
neural-network-based baselines. Finally, we demonstrate the utility of our
algorithm by integrating it with two medical image processing applications that
use gradient-based optimization: deep-learning-based blood vessel segmentation,
and multimodal registration of the mandible in computed tomography and magnetic
resonance images.Comment: Accepted at ICCV 202
A STUDY OF THE ANTI-INFLAMMATORY EFFECTS OF THE ETHYL ACETATE FRACTION OF THE METHANOL EXTRACT OF FORSYTHIAE FRUCTUS
Background: The dried fruit of Forsythia suspensa (Thunb.) Vahl. (Oleaceae) are better known by their herbal name Forsythiae Fructus, and have a bitter taste, slightly pungent smell, and cold habit. FF has been widely used to treat symptoms associated with the lung, heart, and small intestine. Recently, bioactive compounds isolated from hydrophobic solvent fractions of FF have been reported to have anti-oxidant, anti-bacterial, and anti-cancer effects. Traditionally, almost all herbal medicines are water extracts, and thus, extraction methods should be developed to optimize the practical efficacies of herbal medicines. Materials and Methods: In this study, the anti-inflammatory effects of the ethyl acetate fraction of the methanol extract of FF (FFE) were assessed by measuring NO and PGE2 production byand intracellular ROS and protein levels of iNOS and COX-2in RAW 264.7 cells. Results: FFE inhibited COX-2 expression in LPS-stimulated RAW 264.7 cells. Conclusion: In summary, FFE effectively reduced intracellular ROS and NO levels and inhibited PGE2 production by down- regulating COX-2 levels