Convolutional neural network-based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images

The beating heart: artificial intelligence for cardiovascular application in the clinic

Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

Deep learning for automatic volumetric segmentation of left ventricular myocardium and ischaemic scar from multi-slice late gadolinium enhancement cardiovascular magnetic resonance

AIMS

This study details application of deep learning for automatic volumetric segmentation of left ventricular (LV) myocardium and scar and automated quantification of myocardial ischaemic scar burden from late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR).

METHODS AND RESULTS

We included 501 images and manual segmentations of short-axis LGE-CMR from over 20 multinational sites, from which 377 studies were used for training and 124 studies from unique participants for internal validation. A third test set of 52 images was used for external evaluation. Three models, U-Net, Cascaded U-Net, and U-Net++, were trained with a novel adaptive weighted categorical cross-entropy loss function. Model performance was evaluated using concordance correlation coefficients (CCCs) for LV mass and per cent myocardial scar burden. Cascaded U-Net was found to be the best model for the quantification of LV mass and scar percentage. The model exhibited a mean difference of -5 ± 23 g for LV mass, -0.4 ± 11.2 g for scar mass, and -0.8 ± 7% for per cent scar. CCC were 0.87, 0.77, and 0.78 for LV mass, scar mass, and per cent scar burden, respectively, in the internal validation set and 0.75, 0.71, and 0.69, respectively, in the external test set. For segmental scar mass, CCC was 0.74 for apical scar, 0.91 for mid-ventricular scar, and 0.73 for basal scar, demonstrating moderate to strong agreement.

CONCLUSION

We successfully trained a convolutional neural network for volumetric segmentation and analysis of LV scar burden from LGE-CMR images in a large, multinational cohort of participants with ischaemic scar.

Artificial Intelligence in Heart Failure: Friend or Foe?

In recent times, there have been notable changes in cardiovascular medicine, propelled by the swift advancements in artificial intelligence (AI). The present work provides an overview of the current applications and challenges of AI in the field of heart failure. It emphasizes the "garbage in, garbage out" issue, where AI systems can produce inaccurate results with skewed data. The discussion covers issues in heart failure diagnostic algorithms, particularly discrepancies between existing models. Concerns about the reliance on the left ventricular ejection fraction (LVEF) for classification and treatment are highlighted, showcasing differences in current scientific perceptions. This review also delves into challenges in implementing AI, including variable considerations and biases in training data. It underscores the limitations of current AI models in real-world scenarios and the difficulty in interpreting their predictions, contributing to limited physician trust in AI-based models. The overarching suggestion is that AI can be a valuable tool in clinicians' hands for treating heart failure patients, as far as existing medical inaccuracies have been addressed before integrating AI into these frameworks.

Present and Future Innovations in AI and Cardiac MRI

Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.

Cardiac Magnetic Resonance as Risk Stratification Tool in Non-Ischemic Dilated Cardiomyopathy Referred for Implantable Cardioverter Defibrillator Therapy-State of Art and Perspectives

Non-ischemic dilated cardiomyopathy (DCM) is a disease characterized by left ventricular dilation and systolic dysfunction. Patients with DCM are at higher risk for ventricular arrhythmias and sudden cardiac death (SCD). According to current international guidelines, left ventricular ejection fraction (LVEF) ≤ 35% represents the main indication for prophylactic implantable cardioverter defibrillator (ICD) implantation in patients with DCM. However, LVEF lacks sensitivity and specificity as a risk marker for SCD. It has been seen that the majority of patients with DCM do not actually benefit from the ICD implantation and, on the contrary, that many patients at risk of SCD are not identified as they have preserved or mildly depressed LVEF. Therefore, the use of LVEF as unique decision parameter does not maximize the benefit of ICD therapy. Multiple risk factors used in combination could likely predict SCD risk better than any single risk parameter. Several predictors have been proposed including genetic variants, electric indexes, and volumetric parameters of LV. Cardiac magnetic resonance (CMR) can improve risk stratification thanks to tissue characterization sequences such as LGE sequence, parametric mapping, and feature tracking. This review evaluates the role of CMR as a risk stratification tool in DCM patients referred for ICD.

Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation

Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable information. For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively. Existing methods usually fuse anatomical and pathological information from different CMR sequences for MyoPS, but assume that these images have been spatially aligned. However, MS-CMR images are usually unaligned due to the respiratory motions in clinical practices, which poses additional challenges for MyoPS. This work presents an automatic MyoPS framework for unaligned MS-CMR images. Specifically, we design a combined computing model for simultaneous image registration and information fusion, which aggregates multi-sequence features into a common space to extract anatomical structures (i.e., myocardium). Consequently, we can highlight the informative regions in the common space via the extracted myocardium to improve MyoPS performance, considering the spatial relationship between myocardial pathologies and myocardium. Experiments on a private MS-CMR dataset and a public dataset from the MYOPS2020 challenge show that our framework could achieve promising performance for fully automatic MyoPS.

Artificial intelligence in cardiovascular imaging: enhancing image analysis and risk stratification

In this review, we summarize state-of-the-art artificial intelligence applications for non-invasive cardiovascular imaging modalities including CT, MRI, echocardiography, and nuclear myocardial perfusion imaging.

MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. Note that MyoPS refers to both myocardial pathology segmentation and the challenge in this paper. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore the potential of solutions, as well as to provide a benchmark for future research. The average Dice scores of submitted algorithms were 0.614±0.231 and 0.644±0.153 for myocardial scars and edema, respectively. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

Application of AI in cardiovascular multimodality imaging

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.

Classification of myocardial fibrosis in DE-MRI based on semi-supervised semantic segmentation and dual attention mechanism

OBJECTIVE

It is essential to utilize cardiac delayed-enhanced magnetic resonance imaging (DE-MRI) to diagnose cardiovascular disease. By segmenting myocardium DE-MRI images, it provides critical information for the evaluation and treatment of myocardial infarction. As a consequence, it is vital to investigate the segmentation and classification technique of myocardial DE-MRI.

METHODS

Firstly, an end-to-end minimally supervised and semi-supervised semantic DE-MRI myocardial fibrosis segmentation framework is proposed, which combines image classification and semantic segmentation branches based on the self-attention mechanism. Following that, a residual hole network fused with the dual attention mechanism was built, and a double attention metabolic pathway classification method for cardiac fibrosis in DE-MRI images was developed.

RESULTS

By adding pixel-level labels to an extra 40 training images, the segmentation model may enhance semantic segmentation performance by 2.6 percent (from 61.2 percent to 63.8 percent). When the number of pixel-level labels is increased to 80, semi-supervised feature extraction increases by 4.7 percent when compared to weakly guided semantic segmentation. Adding an attention mechanism to the critical network DRN (Deep Residual Network) can increase the classifier's performance by a small amount. Experiments revealed that the models worked effectively.

CONCLUSION

This paper investigates the segmentation and classification of cardiac fibrosis in DE-MRI data using a semi-supervised semantic segmentation and dual attention mechanism, dealing with the issue that existing segmentation algorithms have difficulty segmenting myocardial fibrosis tissue. In the future, we can consider optimizing the design of the attention module to reduce the module computation.

The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance-A Comprehensive Review

Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.

HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images

Automatic liver and tumor segmentation are essential steps to take decisive action in hepatic disease detection, deciding therapeutic planning, and post-treatment assessment. The computed tomography (CT) scan has become the choice of medical experts to diagnose hepatic anomalies. However, due to advancements in CT image acquisition protocol, CT scan data is growing and manual delineation of the liver and tumor from the CT volume becomes cumbersome and tedious for medical experts. Thus, the outcome becomes highly reliant on the operator's proficiency. Further, automatic liver and tumor segmentation from CT images is challenging due to complicated parenchyma, highly variable shape, and fewer voxel intensity variation among the liver, tumor, neighbouring organs, and discontinuity in liver boundaries. Recently deep learning (DL) exhibited extraordinary potential in medical image interpretation. Because of its effectiveness in performance advancement, the DL-based convolutional neural networks (CNN) gained significant interest in the medical realm. The proposed HFRU-Net is derived from the UNet architecture by modifying the skip pathways using local feature reconstruction and feature fusion mechanism that represents the detailed contextual information in the high-level features. Further, the fused features are adaptively recalibrated by learning the channel-wise interdependencies to acquire the prominent details of the modified high-level features using the squeeze-and-Excitation network (SENet). Also, in the bottleneck layer, we employed the atrous spatial pyramid pooling (ASPP) module to represent the multiscale features with dissimilar receptive fields to represent the rich spatial information in the low-level features. These amendments uplift the segmentation performance and reduce the computational complexity of the model than outperforming methods. The efficacy of the proposed model is proved by widespread experimentation on two datasets available publicly (LiTS and 3DIrcadb). The experimental result analysis illustrates that the proposed model has attained a dice similarity coefficient of 0.966 and 0.972 for liver segmentation and 0.771 and 0.776 for liver tumor segmentation on LiTS and the 3DIRCADb dataset. Further, the robustness of the HFRU-Net is confirmed on the independent LiTS challenge test dataset. The proposed model attained the global dice of 95.0% for liver segmentation and 61.4% for tumor segmentation which is comparable with the state-of-the-art methods.

Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review

The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.

Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging-A systematic review

Background

Accurate, rapid quantification of ventricular scar using cardiac magnetic resonance imaging (CMR) carries importance in arrhythmia management and patient prognosis. Artificial intelligence (AI) has been applied to other radiological challenges with success.

Objective

We aimed to assess AI methodologies used for left ventricular scar identification in CMR, imaging sequences used for training, and its diagnostic evaluation.

Methods

Following PRISMA recommendations, a systematic search of PubMed, Embase, Web of Science, CINAHL, OpenDissertations, arXiv, and IEEE Xplore was undertaken to June 2021 for full-text publications assessing left ventricular scar identification algorithms. No pre-registration was undertaken. Random-effect meta-analysis was performed to assess Dice Coefficient (DSC) overlap of learning vs predefined thresholding methods.

Results

Thirty-five articles were included for final review. Supervised and unsupervised learning models had similar DSC compared to predefined threshold models (0.616 vs 0.633, P = .14) but had higher sensitivity, specificity, and accuracy. Meta-analysis of 4 studies revealed standardized mean difference of 1.11; 95% confidence interval -0.16 to 2.38, P = .09, I² = 98% favoring learning methods.

Conclusion

Feasibility of applying AI to the task of scar detection in CMR has been demonstrated, but model evaluation remains heterogenous. Progression toward clinical application requires detailed, transparent, standardized model comparison and increased model generalizability.

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.

The evolving role of artificial intelligence in cardiac image analysis

Research in artificial intelligence (AI) have progressed over the last decade. The field of cardiac imaging has seen significant developments using newly developed deep learning methods for automated image analysis and AI tools for disease detection and prognostication. This review article is aimed at those without special background in AI. We review AI concepts and we survey the growing contemporary applications of AI for image analysis in echocardiography, nuclear cardiology, cardiac computed tomography, cardiac magnetic resonance, and invasive angiography.

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.

Myocardial Infarction Quantification from Late Gadolinium Enhancement MRI Using Top-Hat Transforms and Neural Networks

Late gadolinium enhancement (LGE) MRI is the gold standard technique for myocardial viability assessment. Although the technique accurately reflects the damaged tissue, there is no clinical standard to quantify myocardial infarction (MI). Moreover, commercial software used in clinical practice are mostly semi-automatic, and hence require direct intervention of experts. In this work, a new automatic method for MI quantification from LGE-MRI is proposed. Our novel segmentation approach is devised for accurately detecting not only hyper-enhanced lesions, but also microvascular obstruction areas. Moreover, it includes a myocardial disease detection step which extends the algorithm for working under healthy scans. The method is based on a cascade approach where firstly, diseased slices are identified by a convolutional neural network (CNN). Secondly, by means of morphological operations a fast coarse scar segmentation is obtained. Thirdly, the segmentation is refined by a boundary-voxel reclassification strategy using an ensemble of very light CNNs. We tested the method on a LGE-MRI database with healthy (n = 20) and diseased (n = 80) cases following a 5-fold cross-validation scheme. Our approach segmented myocardial scars with an average Dice coefficient of 77.22 ± 14.3% and with a volumetric error of 1.0 ± 6.9 cm3. In a comparison against nine reference algorithms, the proposed method achieved the highest agreement in volumetric scar quantification with the expert delineations (p< 0.001 when compared to the other approaches). Moreover, it was able to reproduce the scar segmentation intra- and inter-rater variability. Our approach was shown to be a good first attempt towards automatic and accurate myocardial scar segmentation, although validation over larger LGE-MRI databases is needed.

Evaluation of Effect of Curcumin on Psychological State of Patients with Pulmonary Hypertension by Magnetic Resonance Image under Deep Learning

This research aimed to evaluate the right ventricular segmentation ability of magnetic resonance imaging (MRI) images based on deep learning and evaluate the influence of curcumin (Cur) on the psychological state of patients with pulmonary hypertension (PH). The heart MRI images were detected based on the You Only Look Once (YOLO) algorithm, and then the MRI image right ventricle segmentation algorithm was established based on the convolutional neural network (CNN) algorithm. The segmentation effect of the right ventricle in cardiac MRI images was evaluated regarding intersection-over-union (IOU), Dice coefficient, accuracy, and Jaccard coefficient. 30 cases of PH patients were taken as the research object. According to different treatments, they were rolled into control group (conventional treatment) and Cur group (conventional treatment + Cur), with 15 cases in each group. Changes in the scores of the self-rating anxiety scale (SAS) and self-rating depression scale (SDS) of the two groups of patients before and after treatment were analyzed. It was found that the average IOU of the heart target detection frame of the MRI image and the true bounding box before correction was 0.7023, and the IOU after correction was 0.9016. The Loss of the MRI image processed by the CNN algorithm was 0.05, which was greatly smaller than those processed by other algorithms. The Dice coefficient, Jaccard coefficient, and accuracy of the MRI image processed by CNN were 0.89, 0.881, and 0.994, respectively. The MRI images of PH patients showed that the anterior wall of the right ventricle was notably thickened, and the main pulmonary artery was greatly widened. After treatment, the SAR and SDS scores of the two groups were lower than those before treatment (P < 0.05), and the SAR and SDS scores of the curcumin group were lower than those of the control group (P < 0.05). To sum up, the right ventricular segmentation ability of MRI images based on deep learning was improved, and Cur can remarkably alleviate the psychological state of PH patients, which provided a reference for the diagnosis and treatment for PH patients.

Anatomy-aided deep learning for medical image segmentation: a review

Deep learning (DL) has become widely used for medical image segmentation in recent years. However, despite these advances, there are still problems for which DL-based segmentation fails. Recently, some DL approaches had a breakthrough by using anatomical information which is the crucial cue for manual segmentation. In this paper, we provide a review of anatomy-aided DL for medical image segmentation which covers systematically summarized anatomical information categories and corresponding representation methods. We address known and potentially solvable challenges in anatomy-aided DL and present a categorized methodology overview on using anatomical information with DL from over 70 papers. Finally, we discuss the strengths and limitations of the current anatomy-aided DL approaches and suggest potential future work.

Fully Automatic Scar Segmentation for Late Gadolinium Enhancement MRI Images in Left Ventricle with Myocardial Infarction

Numerous methods have been published to segment the infarct tissue in the left ventricle, most of them either need manual work, post-processing, or suffer from poor reproducibility. We proposed an automatic segmentation method for segmenting the infarct tissue in left ventricle with myocardial infarction. Cardiac images of a total of 60 diseased hearts (55 human hearts and 5 porcine hearts) were used in this study. The epicardial and endocardial boundaries of the ventricles in every 2D slice of the cardiac magnetic resonance with late gadolinium enhancement images were manually segmented. The subsequent pipeline of infarct tissue segmentation is fully automatic. The segmentation results with the automatic algorithm proposed in this paper were compared to the consensus ground truth. The median of Dice overlap between our automatic method and the consensus ground truth is 0.79. We also compared the automatic method with the consensus ground truth using different image sources from different centers with different scan parameters and different scan machines. The results showed that the Dice overlap with the public dataset was 0.83, and the overall Dice overlap was 0.79. The results show that our method is robust with respect to different MRI image sources, which were scanned by different centers with different image collection parameters. The segmentation accuracy we obtained is comparable to or better than that of the conventional semi-automatic methods. Our segmentation method may be useful for processing large amount of dataset in clinic.

Performance of an Automated Workflow for Magnetic Resonance Imaging of the Prostate: Comparison With a Manual Workflow

OBJECTIVES

The aim of this study was to evaluate the performance of an automated workflow for multiparametric magnetic resonance imaging (mpMRI) of the prostate compared with a manual mpMRI workflow.

MATERIALS AND METHODS

This retrospective study was approved by the local ethics committee. Two MR technicians scanned 2 healthy volunteers with a prototypical highly automated workflow (Siemens Healthineers GmbH, Erlangen, Germany) and with a manually adjusted scan protocol each. Thirty patients (mean age ± standard deviation, 68 ± 11 years; range, 41-93 years) with suspected prostate cancer underwent mpMRI on a 3 T MRI scanner. Fifteen patients were examined with the automated workflow and 15 patients with a conventional manual workflow. Two readers assessed image quality (contrast, zone distinction, organ margins, seminal vesicles, lymph nodes), organ coverage, orientation (T2w sequences), and artifacts (motion, susceptibility, noise) on a 5-point scale (1, poor; 5, excellent). Examination time and MR technicians' acceptance were compared between both groups. Interreader agreement was evaluated with Cohen's kappa (κ).

RESULTS

The automated workflow proved consistent for sequence orientation and image quality in the intraindividual comparisons. There were no significant differences in examination time (automated vs manual; median 26 vs 28 minutes; interquartile range [IQR], 25-28 minutes each; P = 0.57), study volume coverage, artifacts, or scores for T2w sequence orientation (5 vs 4 each; P > 0.3). Overall image quality was superior for automated MRI (4.6 vs 3.8; IQR, 3.9-4.8 vs 3.2-4.3; P = 0.002), especially concerning organ delineation and seminal vesicles (P = 0.045 and P = 0.013). The acceptance score was higher for the manual workflow (median, 10 vs 8; IQR, 10 vs 7-10; P = 0.002). General interreader agreement was excellent (κ = 0.832; P < 0.001).

CONCLUSIONS

The automated workflow for prostate MRI ensures accurate sequence orientation and maintains high image quality, whereas examination time remained unaffected compared with the manual procedure in our institution.

The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies - First Application Examples

PURPOSE

 The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets.

MATERIALS AND METHODS

 The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP.

RESULTS

 First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria.

CONCLUSION

 It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups.

KEY POINTS

  · The DRG-ÖRG IRP is a web/cloud-based radiomics platform based on a public-private partnership.. · The DRG-ÖRG IRP can be used for the creation of quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis.. · First results show the applicability of left ventricular myocardial segmentation using a neural network and segment-based LGE detection using radiomic image features.. · The DRG-ÖRG IRP offers the possibility of integrating pre-trained neural networks and networking of scientific groups..

CITATION FORMAT

· Overhoff D, Kohlmann P, Frydrychowicz A et al. The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies. Fortschr Röntgenstr 2021; 193: 276 - 287.

Artificial intelligence in cardiac radiology

Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.

Machine Learning-Based Segmentation of Left Ventricular Myocardial Fibrosis from Magnetic Resonance Imaging

PURPOSE OF REVIEW

Myocardial fibrosis (MF) arises due to myocardial infarction and numerous cardiac diseases. MF may lead to several heart disorders, such as heart failure, arrhythmias, and ischemia. Cardiac magnetic resonance (CMR) imaging techniques, such as late gadolinium enhancement (LGE) CMR, enable non-invasive assessment of MF in the left ventricle (LV). Manual assessment of MF on CMR is a tedious and time-consuming task that is subject to high observer variability. Automated segmentation and quantification of MF is important for risk stratification and treatment planning in patients with heart disorders. This article aims to review the machine learning (ML)-based methodologies developed for MF quantification in the LV using CMR images.

RECENT FINDINGS

With the availability of relatively large labeled datasets supervised learning methods based on both conventional ML and state-of-the-art deep learning (DL) methods have been successfully applied for automated segmentation of MF. The incorporation of ML algorithms into imaging techniques such as 3D LGE CMR permits fast characterization of MF on CMR imaging and may enhance the diagnosis and prognosis of patients with heart disorders. Concurrently, the studies using cine CMR images have revealed that accurate segmentation of MF on non-contrast CMR imaging might be possible. The application of ML/DL tools in CMR image interpretation is likely to result in accurate and efficient quantification of MF.

Artificial intelligence: improving the efficiency of cardiovascular imaging

INTRODUCTION

Artificial intelligence (AI) describes the use of computational techniques to mimic human intelligence. In healthcare, this typically involves large medical datasets being used to predict a diagnosis, identify new disease genotypes or phenotypes, or guide treatment strategies. Noninvasive imaging remains a cornerstone for the diagnosis, risk stratification, and management of patients with cardiovascular disease. AI can facilitate every stage of the imaging process, from acquisition and reconstruction, to segmentation, measurement, interpretation, and subsequent clinical pathways.

AREAS COVERED

In this paper, we review state-of-the-art AI techniques and their current applications in cardiac imaging, and discuss the future role of AI as a precision medicine tool.

EXPERT OPINION

Cardiovascular medicine is primed for scalable AI applications which can interpret vast amounts of clinical and imaging data in greater depth than ever before. AI-augmented medical systems have the potential to improve workflow and provide reproducible and objective quantitative results which can inform clinical decisions. In the foreseeable future, AI may work in the background of cardiac image analysis software and routine clinical reporting, automatically collecting data and enabling real-time diagnosis and risk stratification.

Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography

Epicardial adipose tissue (EAT) is a visceral fat deposit, that's known for its association with factors, such as obesity, diabetes mellitus, age, and hypertension. Segmentation of the EAT in a fast and reproducible way is important for the interpretation of its role as an independent risk marker intricate. However, EAT has a variable distribution, and various diseases may affect the volume of the EAT, which can increase the complexity of the already time-consuming manual segmentation work. We propose a 3D deep attention U-Net method to automatically segment the EAT from coronary computed tomography angiography (CCTA). Five-fold cross-validation and hold-out experiments were used to evaluate the proposed method through a retrospective investigation of 200 patients. The automatically segmented EAT volume was compared with physician-approved clinical contours. Quantitative metrics used were the Dice similarity coefficient (DSC), sensitivity, specificity, Jaccard index (JAC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). For cross-validation, the median DSC, sensitivity, and specificity were 92.7%, 91.1%, and 95.1%, respectively, with JAC, HD, CMD, MSD, and RMSD are 82.9% ± 8.8%, 3.77 ± 1.86 mm, 1.98 ± 1.50 mm, 0.37 ± 0.24 mm, and 0.65 ± 0.37 mm, respectively. For the hold-out test, the accuracy of the proposed method remained high. We developed a novel deep learning-based approach for the automated segmentation of the EAT on CCTA images. We demonstrated the high accuracy of the proposed learning-based segmentation method through comparison with ground truth contour of 200 clinical patient cases using 8 quantitative metrics, Pearson correlation, and Bland-Altman analysis. Our automatic EAT segmentation results show the potential of the proposed method to be used in computer-aided diagnosis of coronary artery diseases (CADs) in clinical settings.

Myocardial Radiomics in Cardiac MRI

OBJECTIVE. The purpose of this article is to review the nascent field of radiomics in cardiac MRI. CONCLUSION. Cardiac MRI produces a large number of images in a fairly inefficient manner with sometimes limited clinical application. In the era of precision medicine, there is increasing need for imaging to account for a broader array of diseases in an efficient and objective manner. Radiomics, the extraction and analysis of quantitative imaging features from medical imaging, may offer potential solutions to this need.

Deep Learning for Quantitative Cardiac MRI

OBJECTIVE. The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardiac MRI, and discuss its limitations and challenges. CONCLUSION. Deep learning has shown state-of-the-art performance on quantitative analysis of multiple cardiac MRI sequences and holds great promise for future use in clinical practice and scientific research.

Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery

PURPOSE

Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is time consuming and subject to inter- and intraobserver variation. To address these drawbacks, we proposed a deep learning-based method to automatically segment AVMs on CT simulation image sets.

METHODS

We developed a deep learning-based method using a deeply supervised three-dimensional (3D) V-Net with a compound loss function. A 3D supervision mechanism was integrated into a residual network, V-Net, to deal with the optimization difficulties when training deep networks with limited training data. The proposed compound loss function including logistic and Dice losses encouraged similarity and penalized discrepancy simultaneously between prediction and training dataset; this was utilized to supervise the 3D V-Net at different stages. To evaluate the accuracy of segmentation, we retrospectively investigated 80 AVM patients who had CT simulation and digital subtraction angiography (DSA) acquired prior to treatment. The AVM target volume was segmented by our proposed method. They were compared with clinical contours approved by physicians with regard to Dice overlapping, difference in volume and centroid, and dose coverage changes on original plan.

RESULTS

Contours created by the proposed method demonstrated very good visual agreement to the ground truth contours. The mean Dice similarity coefficient (DSC), sensitivity and specificity of the contours delineated by our method were >0.85 among all patients. The mean centroid distance between our results and ground truth was 0.675 ± 0.401 mm, and was not significantly different in any of the three orthogonal directions. The correlation coefficient between ground truth and AVM volume resulting from the proposed method was 0.992 with statistical significance. The mean volume difference among all patients was 0.076 ± 0.728 cc; there was no statistically significant difference. The average differences in dose metrics were all less than 0.2 Gy, with standard deviation less than 1 Gy. No statistically significant differences were observed in any of the dose metrics.

CONCLUSION

We developed a novel, deeply supervised, deep learning-based approach to automatically segment the AVM volume on CT images. We demonstrated its clinical feasibility by validating the shape and positional accuracy, and dose coverage of the automatic volume. These results demonstrate the potential of a learning-based segmentation method for delineating AVMs in the clinical setting.