568 research outputs found
Automatic Segmentation of Left Atrial Scar from Delayed-Enhancement Magnetic Resonance Imaging
Abstract. Delayed-enhancement magnetic resonance imaging is an effective technique for imaging left atrial (LA) scars both pre-and post-radio-frequency ablation for the treatment of atrial fibrillation. Existing techniques for LA scar segmentation require expert manual interaction making them tedious and prone to high observer variability. In this paper, we propose a novel automatic segmentation algorithm for segmenting LA scar based on a probabilistic tissue intensity model. This is implemented as a Markov random field-based energy formulation and solved using graph-cuts. It was evaluated against an existing semi-automatic approach and expert manual segmentations using 9 patient data sets. Surface representations were used to compare the methods. The segmented LA scar was expressed as a percentage of the total LA surface. Statistical analysis showed that the novel algorithm was not significantly different to the manual method and that it compared more favorably with this than the semi-automatic approach
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
Rapid automatic segmentation of abnormal tissue in late gadolinium enhancement cardiovascular magnetic resonance images for improved management of long-standing persistent atrial fibrillation
Background: Atrial fibrillation (AF) is the most common heart rhythm disorder. In order for late Gd enhancement cardiovascular magnetic resonance (LGE CMR) to ameliorate the AF management, the ready availability of the accurate enhancement segmentation is required. However, the computer-aided segmentation of enhancement in LGE CMR of AF is still an open question. Additionally, the number of centres that have reported successful application of LGE CMR to guide clinical AF strategies remains low, while the debate on LGE CMR’s diagnostic ability for AF still holds. The aim of this study is to propose a method that reliably distinguishes enhanced (abnormal) from non-enhanced (healthy) tissue within the left atrial wall of (pre-ablation and 3 months post-ablation) LGE CMR data-sets from long-standing persistent AF patients studied at our centre.
Methods: Enhancement segmentation was achieved by employing thresholds benchmarked against the statistics of the whole left atrial blood-pool (LABP). The test-set cross-validation mechanism was applied to determine the input feature representation and algorithm that best predict enhancement threshold levels.
Results: Global normalized intensity threshold levels T PRE = 1 1/4 and T POST = 1 5/8 were found to segment enhancement in data-sets acquired pre-ablation and at 3 months post-ablation, respectively. The segmentation results were corroborated by using visual inspection of LGE CMR brightness levels and one endocardial bipolar voltage map. The measured extent of pre-ablation fibrosis fell within the normal range for the specific arrhythmia phenotype. 3D volume renderings of segmented post-ablation enhancement emulated the expected ablation lesion patterns. By comparing our technique with other related approaches that proposed different threshold levels (although they also relied on reference regions from within the LABP) for segmenting enhancement in LGE CMR data-sets of AF patients, we illustrated that the cut-off levels employed by other centres may not be usable for clinical studies performed in our centre.
Conclusions: The proposed technique has great potential for successful employment in the AF management within our centre. It provides a highly desirable validation of the LGE CMR technique for AF studies. Inter-centre differences in the CMR acquisition protocol and image analysis strategy inevitably impede the selection of a universally optimal algorithm for segmentation of enhancement in AF studies
Automated Method for the Volumetric Evaluation of Myocardial Scar from Cardiac Magnetic Resonance Images
In most western countries cardiovascular diseases are the leading cause of death, and for the survivors of ischemic attack an accurate quantification of the extent of the damage is required to correctly assess its impact and for
risk stratification, and to select the best treatment for the patient. Moreover, a fast and reliable tool for the assessment of the cardiac function and the measurement of clinical indexes is highly desirable. The aim of this thesis is to provide computational approaches to better detect and assess the presence of myocardial fibrosis in the heart, particularly but not only in the left ventricle, by performing a fusion of the information from different magnetic resonance imaging sequences. We also developed and provided a semiautomatic tool useful for the fast evaluation and quantification of clinical indexes derived from heart chambers volumes. The thesis is composed by five chapters. The first chapter introduces the most common cardiac diseases such as ischemic cardiomyopathy and describes in detail the cellular and structural remodelling phenomena stemming from heart failure. The second chapter regards the detection of the left ventricle through the development of a semi-automated approach for both endocardial and epicardial surfaces, and myocardial mask extraction. In the third chapter the workflow for scar assessment is presented, in which the previously described approach is used to obtain the 3D left ventricle patient-specific geometry; a registration algorithm is then used to superimpose the fibrosis information derived from the late gadolinium enhancement magnetic resonance imaging to obtain a patientspecific 3D map of fibrosis extension and location on the left ventricle myocardium. Focus of the fourth chapter is on the left atrium, and fibrotic tissue detection for gaining insight on atrial fibrillation. In the fifth chapter some conclusive remarks are presented with possible future developments of the presented work
Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images
Studies have demonstrated the feasibility of late Gadolinium enhancement (LGE) cardiovascular magnetic
resonance (CMR) imaging for guiding the management of patients with sequelae to myocardial infarction,
such as ventricular tachycardia and heart failure. Clinical implementation of these developments necessitates
a reproducible and reliable segmentation of the infarcted regions. It is challenging to compare
new algorithms for infarct segmentation in the left ventricle (LV) with existing algorithms. Benchmarking
datasets with evaluation strategies are much needed to facilitate comparison. This manuscript presents
a benchmarking evaluation framework for future algorithms that segment infarct from LGE CMR of the
LV. The image database consists of 30 LGE CMR images of both humans and pigs that were acquired
from two separate imaging centres. A consensus ground truth was obtained for all data using maximum
likelihood estimation.
Six widely-used fixed-thresholding methods and five recently developed algorithms are tested on the
benchmarking framework. Results demonstrate that the algorithms have better overlap with the consensus
ground truth than most of the n-SD fixed-thresholding methods, with the exception of the FullWidth-at-Half-Maximum
(FWHM) fixed-thresholding method. Some of the pitfalls of fixed thresholding
methods are demonstrated in this work. The benchmarking evaluation framework, which is a contribution
of this work, can be used to test and benchmark future algorithms that detect and quantify infarct
in LGE CMR images of the LV. The datasets, ground truth and evaluation code have been made publicly
available through the website: https://www.cardiacatlas.org/web/guest/challenges
Mind the gap: quantification of incomplete ablation patterns after pulmonary vein isolation using minimum path search
Pulmonary vein isolation (PVI) is a common procedure for the treatment of
atrial fibrillation (AF). A successful isolation produces a continuous lesion
(scar) completely encircling the veins that stops activation waves from
propagating to the atrial body. Unfortunately, the encircling lesion is often
incomplete, becoming a combination of scar and gaps of healthy tissue. These
gaps are potential causes of AF recurrence, which requires a redo of the
isolation procedure. Late-gadolinium enhanced cardiac magnetic resonance
(LGE-CMR) is a non-invasive method that may also be used to detect gaps, but it
is currently a time-consuming process, prone to high inter-observer
variability. In this paper, we present a method to semi-automatically identify
and quantify ablation gaps. Gap quantification is performed through minimum
path search in a graph where every node is a scar patch and the edges are the
geodesic distances between patches. We propose the Relative Gap Measure (RGM)
to estimate the percentage of gap around a vein, which is defined as the ratio
of the overall gap length and the total length of the path that encircles the
vein. Additionally, an advanced version of the RGM has been developed to
integrate gap quantification estimates from different scar segmentation
techniques into a single figure-of-merit. Population-based statistical and
regional analysis of gap distribution was performed using a standardised
parcellation of the left atrium. We have evaluated our method on synthetic and
clinical data from 50 AF patients who underwent PVI with radiofrequency
ablation. The population-based analysis concluded that the left superior PV is
more prone to lesion gaps while the left inferior PV tends to have less gaps
(p<0.05 in both cases), in the processed data. This type of information can be
very useful for the optimization and objective assessment of PVI interventions
Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images
Multi-sequence of cardiac magnetic resonance (CMR) images can provide
complementary information for myocardial pathology (scar and edema). However,
it is still challenging to fuse these underlying information for pathology
segmentation effectively. This work presents an automatic cascade pathology
segmentation framework based on multi-modality CMR images. It mainly consists
of two neural networks: an anatomical structure segmentation network (ASSN) and
a pathological region segmentation network (PRSN). Specifically, the ASSN aims
to segment the anatomical structure where the pathology may exist, and it can
provide a spatial prior for the pathological region segmentation. In addition,
we integrate a denoising auto-encoder (DAE) into the ASSN to generate
segmentation results with plausible shapes. The PRSN is designed to segment
pathological region based on the result of ASSN, in which a fusion block based
on channel attention is proposed to better aggregate multi-modality information
from multi-modality CMR images. Experiments from the MyoPS2020 challenge
dataset show that our framework can achieve promising performance for
myocardial scar and edema segmentation.Comment: 12 pages,MyoPS 202
- …