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

Design and clinical validation of novel imaging strategies for analysis of arrhythmogenic substrate

_CURRENT CHALLENGES IN ELECTROPHYSIOLOGY_ Technical advances in cardiovascular electrophysiology have resulted in an increasing number of catheter ablation procedures reaching 200 000 in Europe for the year 2013. These advanced interventions are often complex and time consuming and may cause significant radiation exposure. Furthermore, a substantial number of ablation procedures remain associated with poor (initial) outcomes and frequently require ≥1 redo procedures. Innovations in modalities for substrate imaging could facilitate our understanding of the arrhythmogenic substrate, improve the design of patient-specific ablation strategies and improve the results of ablation procedures. _NOVEL SUBSTRATE IMAGING MODALITIES_ __Cardiac magnetic resonance__ Cardiac magnetic resonance imaging (CMR) can be considered the most comprehensive and suitable modality for the complete electrophysiology and catheter ablation workup (including patient selection, procedural guidance, and [procedural] follow-up). Utilizing inversion recovery CMR, fibrotic myocardium can be visualized and quantified 10–15 min after intravenous administration of Gadolinium contrast. This imaging technique is known as late Gadolinium enhancement (LGE) imaging. Experimental models have shown excellent agreement between size and shape in LGE CMR and areas of myocardial infarction by histopathology. Recent studies have also demonstrated how scar size, shape and location from pre-procedural LGE can be useful in guiding ventricular tachycardia’s (VT) ablation or atrial fibrillation (AF) ablation. These procedures are often time-consuming due to the preceding electrophysiological mapping study required to identify slow conduction zones involved in re-entry circuits. Post-processed LGE images provide scar maps, which could be integrated with electroanatomic mapping systems to facilitate these procedures. __Inverse potential mapping__ Through the years, various noninvasive electrocardiographic imaging techniques have emerged that estimate epicardial potentials or myocardial activation times from potentials recorded on the thorax. Utilizing an inverse procedure, the potentials on the heart surface or activation times of the myocardium are estimated with the recorded body surface potentials as source data. Although this procedure only estimates the time course of unipolar epicardial electrograms, several studies have demonstrated that the epicardial potentials and electrograms provide substantial information about intramyocardial activity and have great potential to facilitate risk-stratification and generate personalized ablation strategies. __Objectives of this thesis__ 1. To evaluate the utility of cardiac magnetic resonance derived geometrical and tissue characteristic information for patient stratification and guidance of AF ablation. 2. To design and evaluate the performance of a finite element model based inverse potential mapping in predicting the arrhythmogenic focus in idiopathic ventricular tachycardia using invasive electro-anatomical activation mapping as a reference standard

Automated Scar Segmentation from CMR-LGE Images Using a Deep Learning Approach

Aim. The presence of myocardial scar is a strong predictor of ventricular remodeling, cardiac dysfunction and mortality. Our aim was to assess quantitatively the presence of scar tissue from cardiac-magnetic-resonance (CMR) with late-Gadolinium-enhancement (LGE) images using a deep-learning (DL) approach. Methods. Scar segmentation was performed automatically with a DL approach based on ENet, a deep fully-convolutional neural network (FCNN). We investigated three different ENet configurations. The first configuration (C1) exploited ENet to retrieve directly scar segmentation from the CMR-LGE images. The second (C2) and third (C3) configurations performed scar segmentation in the myocardial region, which was previously obtained in a manual or automatic way with a state-of-the-art DL method, respectively. Results. When tested on 250 CMR-LGE images from 30 patients, the best-performing configuration (C2) achieved 97% median accuracy (inter-quartile (IQR) range = 4%) and 71% median Dice similarity coefficient (IQR = 32%). Conclusions. DL approaches using ENet are promising in automatically segmenting scars in CMR-LGE images, achieving higher performance when limiting the search area to the manually-defined myocardial region

ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans

Medical images with specific pathologies are scarce, but a large amount of data is usually required for a deep convolutional neural network (DCNN) to achieve good accuracy. We consider the problem of segmenting the left ventricular (LV) myocardium on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) scans of which only some of the scans have scar tissue. We propose ScarGAN to simulate scar tissue on healthy myocardium using chained generative adversarial networks (GAN). Our novel approach factorizes the simulation process into 3 steps: 1) a mask generator to simulate the shape of the scar tissue; 2) a domain-specific heuristic to produce the initial simulated scar tissue from the simulated shape; 3) a refining generator to add details to the simulated scar tissue. Unlike other approaches that generate samples from scratch, we simulate scar tissue on normal scans resulting in highly realistic samples. We show that experienced radiologists are unable to distinguish between real and simulated scar tissue. Training a U-Net with additional scans with scar tissue simulated by ScarGAN increases the percentage of scar pixels correctly included in LV myocardium prediction from 75.9% to 80.5%.Comment: 12 pages, 5 figures. To appear in MICCAI DLMIA 201

Artificial Intelligence Will Transform Cardiac Imaging-Opportunities and Challenges

National Institute for Health Research (NIHR) Cardiovascular Biomedical Research Center at BartsSmartHeart EPSRC programme grant (www.nihr.ac.uk; EP/P001009/1)London Medical Imaging and AI Center for Value-Based HealthcareCAP-AI programmeEuropean Union's Horizon 2020 research and innovation programme under grant agreement No 825903

Comparison of Segmentation Algorithms for Detecting Myocardial Infarction Using Late Gadolinium Enhancement Magnetic Resonance Imaging

Objective: The aim of this study was to validate the accuracy of a new automatic method for scar segmentation and compare its performance with that of two other frequently used segmentation algorithms. Methods: Twenty-six late gadolinium enhancement cardiovascular magnetic resonance images of diseased hearts were segmented by the full width at half maximum (FWHM) method, the n standard deviations ( n SD) method, and our new automatic method. The results of the three methods were compared with the consensus ground truth obtained by manual segmentation of the ventricular boundaries. Results: Our automatic method yielded the highest Dice score and the lowest volume difference compared with the consensus ground truth segmentation. The n SD method produced large variations in the Dice score and the volume difference. The FWHM method yielded the lowest Dice score and the greatest volume difference compared with the automatic, 6SD, and 8SD methods, but resulted in less variation when different observers segmented the images. Conclusion: The automatic method introduced in this study is highly reproducible and objective. Because it requires no manual intervention, it may be useful for processing large datasets produced in clinical applications

The role of artificial intelligence in paediatric cardiovascular magnetic resonance imaging

Artificial intelligence (AI) offers the potential to change many aspects of paediatric cardiac imaging. At present, there are only a few clinically validated examples of AI applications in this field. This review focuses on the use of AI in paediatric cardiovascular MRI, using examples from paediatric cardiovascular MRI, adult cardiovascular MRI and other radiologic experience

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.Methods: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) mod-els based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo-and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.Results: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets.Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker ( <1 min versus 7 +/- 3 min), and required minimal user interaction. Conclusions: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.(c) 2022 Elsevier B.V. All rights reserved

Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images

Objective: The aim of this paper is to investigate the use of fully convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images. Methods: A successful FCNN in the literature (the ENet) was modified and trained to provide scar-tissue segmentation. Two segmentation protocols (Protocol 1 and Protocol 2) were investigated, the latter limiting the scar-segmentation search area to the left ventricular myocardial tissue region. CMR-LGE from 30 patients with ischemic-heart disease were retrospectively analyzed, for a total of 250 images, presenting high variability in terms of scar dimension and location. Segmentation results were assessed against manual scar-tissue tracing using one-patient-out cross validation. Results: Protocol 2 outperformed Protocol 1 significantly (p value < 0.05), with median sensitivity and Dice similarity coefficient equal to 88.07% [inter-quartile range (IQR) 18.84%] and 71.25% (IQR 31.82%), respectively. Discussion: Both segmentation protocols were able to detect scar tissues in the CMR-LGE images but higher performance was achieved when limiting the search area to the myocardial region. The findings of this paper represent an encouraging starting point for the use of FCNNs for the segmentation of nonviable scar tissue from CMR-LGE images