Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

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

Scar conducting channel wall thickness characterization to predict arrhythmogenicity during ventricular tachycardia ablation

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2020-2021. Tutora: Paz Garre Anguera de Sojo.The obtention of cardiac images before the surgery ablation of ventricular tachycardia is widely used to obtain more and better information from the patient than the information obtained during the procedure. This technique is commonly performed using cardiac magnetic resonance since it allows to study and characterise the tissue, which is crucial to detect quantify scarred tissue and the particular region that triggers the tachycardia. In this project, the arrhythmogenicity of different conducting channels from patients subjected to ventricular tachycardia ablation has been studied along with their wall thickness in order to assess a correlation using late gadolinium enhancement cardiac magnetic resonance imaging. In addition, the correlation between the left ventricle wall thickness of the conducting channels and the outcome of the cardiac catheter ablation performed from the endocardial region of the heart has also been studied. This project emerges from a previous study performed in the Hospital Clínic de Barcelona that characterized several features of the main conducting channel that triggers the ventricular tachycardia. To perform this study, the images used and the information regarding the arrhythmogenic conducting channel of every patient were obtained from the previous research, using 26 patients for the main objective of this project and using 10 of them for the study of the outcome of the ventricular tachycardia ablation The study of the wall thickness and the visualization of the conducting channels were performed using ADAS 3D software. Results showed that there was not a significative difference between the wall thickness from arrhythmogenic conducting channels and from the non-arrhythmogenic conducting channels within the patients studied but it is important to highlight that the p-value obtained was too large, which might have been caused by the lack of patients to include to this study. However, an interesting distribution of the arrhythmogenic conducting channel was noticed in the inferior-septum region of the heart, which is interesting to study further in the future using more patients and, hence, more conducting channels to study. To conclude, it is important to highlight the role of technology and biomedical engineering in this field to achieve better image acquisition to improve therapeutical techniques for the patient and this project has contributed to the awareness and the comprehension of the role of a biomedical engineer in a clinical environment

Deep learning tools for outcome prediction in a trial fibrilation from cardiac MRI

Tese de mestrado integrado em Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica), Universidade de Lisboa, Faculdade de Ciências, 2021Atrial fibrillation (AF), is the most frequent sustained cardiac arrhythmia, described by an irregular and rapid contraction of the two upper chambers of the heart (the atria). AF development is promoted and predisposed by atrial dilation, which is a consequence of atria adaptation to AF. However, it is not clear whether atrial dilation appears similarly over the cardiac cycle and how it affects ventricular volumes. Catheter ablation is arguably the AF gold standard treatment. In their current form, ablations are capable of directly terminating AF in selected patients but are only first-time effective in approximately 50% of the cases. In the first part of this work, volumetric functional markers of the left atrium (LA) and left ventricle (LV) of AF patients were studied. More precisely, a customised convolutional neural network (CNN) was proposed to segment, across the cardiac cycle, the LA from short axis CINE MRI images acquired with full cardiac coverage in AF patients. Using the proposed automatic LA segmentation, volumetric time curves were plotted and ejection fractions (EF) were automatically calculated for both chambers. The second part of the project was dedicated to developing classification models based on cardiac MR images. The EMIDEC STACOM 2020 challenge was used as an initial project and basis to create binary classifiers based on fully automatic classification neural networks (NNs), since it presented a relatively simple binary classification task (presence/absence of disease) and a large dataset. For the challenge, a deep learning NN was proposed to automatically classify myocardial disease from delayed enhancement cardiac MR (DE-CMR) and patient clinical information. The highest classification accuracy (100%) was achieved with Clinic-NET+, a NN that used information from images, segmentations and clinical annotations. For the final goal of this project, the previously referred NNs were re-trained to predict AF recurrence after catheter ablation (CA) in AF patients using pre-ablation LA short axis in CINE MRI images. In this task, the best overall performance was achieved by Clinic-NET+ with a test accuracy of 88%. This work shown the potential of NNs to interpret and extract clinical information from cardiac MRI. If more data is available, in the future, these methods can potentially be used to help and guide clinical AF prognosis and diagnosis

Landmark Detection in Cardiac MRI Using a Convolutional Neural Network

Purpose: To develop a convolutional neural network (CNN) solution for landmark detection in cardiac MRI. / Materials and Methods: This retrospective study included cine, late-gadolinium enhancement (LGE), and T1 mapping scans from two hospitals. The training set included 2329 patients (34019 images; mean age 54.1 years; 1471 men; December 2017-March 2020). A hold-out test set included 531 patients (7723 images; mean age 51.5 years, 323 men; May 2020-July 2020). CNN models were developed to detect two mitral valve plane and apical points on long-axis images. On short-axis images, anterior and posterior right ventricular insertion points and left ventricle center were detected. Model outputs were compared with manual labels by two readers. The trained model was deployed to MR scanners. / Results: For the long-axis images, successful detection of cardiac landmarks ranged from 99.7% to 100% for cine images and from 99.2% to 99.5% for LGE images. For the short-axis, detection rates was 96.6% for cine, 97.6% for LGE, and 98.9% for T1-mapping. The Euclidean distances between model and manual labels ranged from 2 to 3.5 mm for different landmarks, indicating close agreement between model landmarks to manual labels. No differences were found for the anterior right ventricular insertion angle and left ventricle length by the models and readers for all views and imaging sequences. Model inference on MR scanner took 610 msec on the graphics processing unit and 5.6 sec on central processing unit, respectively, for a typical cardiac cine series. / Conclusion: A CNN was developed for landmark detection in both long and short-axis cardiac MR images for cine, LGE and T1 mapping sequences, with the accuracy comparable to the interreader variation

Improving the domain generalization and robustness of neural networks for medical imaging

Deep neural networks are powerful tools to process medical images, with great potential to accelerate clinical workflows and facilitate large-scale studies. However, in order to achieve satisfactory performance at deployment, these networks generally require massive labeled data collected from various domains (e.g., hospitals, scanners), which is rarely available in practice. The main goal of this work is to improve the domain generalization and robustness of neural networks for medical imaging when labeled data is limited. First, we develop multi-task learning methods to exploit auxiliary data to enhance networks. We first present a multi-task U-net that performs image classification and MR atrial segmentation simultaneously. We then present a shape-aware multi-view autoencoder together with a multi-view U-net, which enables extracting useful shape priors from complementary long-axis views and short-axis views in order to assist the left ventricular myocardium segmentation task on the short-axis MR images. Experimental results show that the proposed networks successfully leverage complementary information from auxiliary tasks to improve model generalization on the main segmentation task. Second, we consider utilizing unlabeled data. We first present an adversarial data augmentation method with bias fields to improve semi-supervised learning for general medical image segmentation tasks. We further explore a more challenging setting where the source and the target images are from different data distributions. We demonstrate that an unsupervised image style transfer method can bridge the domain gap, successfully transferring the knowledge learned from labeled balanced Steady-State Free Precession (bSSFP) images to unlabeled Late Gadolinium Enhancement (LGE) images, achieving state-of-the-art performance on a public multi-sequence cardiac MR segmentation challenge. For scenarios with limited training data from a single domain, we first propose a general training and testing pipeline to improve cardiac image segmentation across various unseen domains. We then present a latent space data augmentation method with a cooperative training framework to further enhance model robustness against unseen domains and imaging artifacts.Open Acces