Automated Cardiac Diagnosis Challenge Research Articles

Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis.

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.

Motion Extraction of the Right Ventricle from 4D Cardiac Cine MRI Using A Deep Learning-Based Deformable Registration Framework.

Cardiac Cine Magnetic Resonance (CMR) Imaging has made a significant paradigm shift in medical imaging technology, thanks to its capability of acquiring high spatial and temporal resolution images of different structures within the heart that can be used for reconstructing patient-specific ventricular computational models. In this work, we describe the development of dynamic patient-specific right ventricle (RV) models associated with normal subjects and abnormal RV patients to be subsequently used to assess RV function based on motion and kinematic analysis. We first constructed static RV models using segmentation masks of cardiac chambers generated from our accurate, memory-efficient deep neural architecture - CondenseUNet - featuring both a learned group structure and a regularized weight-pruner to estimate the motion of the right ventricle. In our study, we use a deep learning-based deformable network that takes 3D input volumes and outputs a motion field which is then used to generate isosurface meshes of the cardiac geometry at all cardiac frames by propagating the end-diastole (ED) isosurface mesh using the reconstructed motion field. The proposed model was trained and tested on the Automated Cardiac Diagnosis Challenge (ACDC) dataset featuring 150 cine cardiac MRI patient datasets. The isosurface meshes generated using the proposed pipeline were compared to those obtained using motion propagation via traditional non-rigid registration based on several performance metrics, including Dice score and mean absolute distance (MAD).

Parametric-based feature selection via spherical harmonic coefficients for the left ventricle myocardial infarction screening.

Computer-aided diagnosis (CAD) of heart diseases using machine learning techniques has recently received much attention. In this study, we present a novel parametric-based feature selection method using the three-dimensional spherical harmonic (SHs) shape descriptors of the left ventricle (LV) for intelligent myocardial infarction (MI) classification. The main hypothesis is that the SH coefficients of the parameterized endocardial shapes in MI patients are recognizable and distinguishable from healthy subjects. The SH parameterization, expansion, and registration of the LV endocardial shapes were performed, then parametric-based features were extracted. The proposed method performance was investigated by varying considered phases (i.e., the end-systole (ES) or the end-diastole (ED) frames), the spatial alignment procedures based on three modes (i.e., the center of the apical (CoA), the center of mass (CoM), and the center of the basal (CoB)), and considered orders of SH coefficients. After applying principal component analysis (PCA) on the feature vectors, support vector machine (SVM), K-nearest neighbors (K-NN), and random forest (RF) were trained and tested using the leave-one-out cross-validation (LOOCV). The proposed method validation was performed via a dataset containing healthy and MI subjects selected from the automated cardiac diagnosis challenge (ACDC) database. The promising results show the effectiveness of the proposed classification model. SVM reached the best performance with accuracy, sensitivity, specificity, and F-score of 97.50%, 95.00%, 100.00%, and 97.56%, respectively, using the introduced optimum feature set. This study demonstrates the robustness of combining the SH coefficients and machine learning techniques. We also quantify and notably highlight the contribution of different parameters in the classification and finally introduce an optimal feature set with maximum discriminant strength for the MI classification task. Moreover, the obtained results confirm that the proposed method performs more accurately than conventional point-based methods and also the current start-of-the-art, i.e., clinical measures. We showed our method's generalizability using employing it in dilated cardiomyopathy (DCM) detection and achieving promising results too. Parametric-based feature selection via spherical harmonics coefficients for the left ventricle myocardial infarction screening.

Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning.

Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left ventricle segmentation. Reinforcement learning-based methods in earlier works often rely on learning proper thresholds to perform segmentation, and the segmentation results are inaccurate due to the sensitivity of the threshold. To tackle this problem, a novel DRL agent is designed to imitate the human process to perform LV segmentation. For this purpose, we formulate the segmentation problem as a Markov decision process and innovatively optimize it through DRL. The proposed DRL agent consists of two neural networks, i.e., First-P-Net and Next-P-Net. The First-P-Net locates the initial edge point, and the Next-P-Net locates the remaining edge points successively and ultimately obtains a closed segmentation result. The experimental results show that the proposed model has outperformed the previous reinforcement learning methods and achieved comparable performances compared with deep learning baselines on two widely used LV endocardium segmentation datasets, namely Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset, and Sunnybrook 2009 dataset. Moreover, the proposed model achieves higher F-measure accuracy compared with deep learning methods when training with a very limited number of samples.

Automatic cardiac cine MRI segmentation and heart disease classification

Cardiac cine magnetic resonance imaging (MRI) continues to be recognized as an established modality for non-invasive assessment of the function and structure of the cardiovascular system. Making full use of fully convolutional neural networks CNNs ability to operate pixel-wise classification, cine MRI sequences can be segmented and volumetric features of three key heart structures are computed for disease prediction. The three key heart structures are the left ventricle cavity, right ventricle cavity and the left ventricle myocardium. In this paper, we suggest an automated pipeline for both cardiac segmentation and diagnosis. The study was conducted on a dataset of 150 patients from Dijon Hospital in the context of the post-2017 Medical Image Computing and Computer Assisted Intervention MICCAI, Automated Cardiac Diagnosis Challenge (ACDC). The challenge consists in two phases: (i) a segmentation contest, where performance is evaluated on dice overlap coefficient and Hausdorff distance metrics, and a (ii) diagnosis contest for heart disease classification. For this aim, we propose the use of a deep learning based network for segmentation of the three key cardiac structures within short-axis cine MRI sequences and a classifier ensemble for heart disease classification. The deep learning segmentation network is a UNet fully convolutional neural network variant with fewer trainable parameters. The classifier ensemble consists in combining three classifiers, namely a multilayer perceptron, a random forest and a support vector machine. Before feeding the segmentation network, a preliminary step consists in localizing heart region and cropping input images to a restricted region of interest (ROI). This is achieved by a signal processing based approach and aims at reducing multi-class imbalance and computational load. We achieved nearly state of the art accuracy performances for both the segmentation and disease classification challenges. Reporting a mean dice overlap coefficient of 0.92 for the three cardiac structures segmentation, along with good limits of agreement for the various derived clinical indices, leading to an accuracy of 0.92 for the disease classification on unseen data.

CNN-Based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI.

Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use the deformation field estimated from the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We assess the CNN-based propagated models against segmented models at each cardiac phase, as well as models propagated using another traditional nonrigid image registration technique. Additionally, we generate dynamic LV myocardial volume meshes at all phases of the cardiac cycle using the log barrier-based mesh warping (LBWARP) method and compare them with the CNN-propagated volume meshes.

A Convolutional Neural Network-based Deformable Image Registration Method for Cardiac Motion Estimation from Cine Cardiac MR Images.

In this work, we describe an unsupervised deep learning framework featuring a Laplacian-based operator as smoothing loss for deformable registration of 3D cine cardiac magnetic resonance (CMR) images. Before registration, the input 3D images are corrected for slice misalignment by segmenting the left ventricle (LV) blood-pool, LV myocardium and right ventricle (RV) blood-pool using a U-Net model and aligning the 2D slices along the center of the LV blood-pool. We conducted experiments using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We used the registration deformation field to warp the manually segmented LV blood-pool, LV myocardium and RV blood-pool labels from end-diastole (ED) frame to the other frames in the cardiac cycle. We achieved a mean Dice score of 94.84%, 85.22% and 84.36%, and Hausdorff distance (HD) of 2.74 mm, 5.88 mm and 9.04 mm, for the LV blood-pool, LV myocardium and RV blood-pool, respectively. We also introduce a pipeline to estimate patient tractography using the proposed CNN-based cardiac motion estimation.

Cardiac MRI Segmentation With a Dilated CNN Incorporating Domain-Specific Constraints

Semantic segmentation of cardiac MR images is a challenging task due to its importance in medical assessment of heart diseases. Having a detailed localization of specific regions of interest such as Right and Left Ventricular Cavities and Myocardium, doctors can infer important information about the presence of cardiovascular diseases, which are today a major cause of death globally. This paper addresses the problem of semantic segmentation in cardiac MR images using a dilated Convolutional Neural Network. Opting for dilated convolutions allowed us to work in full resolution throughout the network's layers, preserving localization accuracy, while maintaining a relatively small number of trainable parameters. To assist the network's training process we designed a custom loss function. Furthermore, we developed new augmentation techniques and also adapted existing ones, to cope for the lack of sufficient training images. Consequently, the training set increases not only by amount, but by substance as well, and the network trains quickly and efficiently without overfitting. Our pre- and post-processing steps are also crucial to the whole process. We apply our methodology for the Right and Left Ventricles (RV, LV) and also the Myocardium (MYO) according to the Automated Cardiac Diagnosis Challenge (ACDC) with promising results. Submitting our algorithm's predictions to the Post-2017-MICCAI-challenge testing phase, we achieved similar scores (average Dice coefficient 0.916) on the test data set compared to the state of the art featured in the ACDC leaderboard, but with significantly fewer parameters than the leading method. Our approach outperforms other methods featuring dilated convolutions in this challenge up until now.

Validation of a diffeomorphic registration algorithm using true deformation computed from thin plate spline interpolation.

Despite the inter and intraobserver variabilities, manual contours are commonly used as surrogates for ground truth in the validation process for nonrigid medical image registration. In contrast, this study proposes the use of thin plate spline interpolation to create a true deformation field. A diffeomorphic registration method was compared to the true deformation field along with three other algorithms and was evaluated on simulated cardiac motion deformation over 10 subjects from the Automated Cardiac Diagnosis Challenge (ACDC) dataset. Two sequential registration approaches were undertaken: with respect to the first frame, and with respect to the previous frame. The Dice score was calculated between the simulated and warped contours for the two approaches: diffeomorphic registration method =0.991 and 0.997, RealTITracker (L2L2 method) = 0.971 and 0.977, RealTITracker (L2L1 method) = 0.975 and 0.978, and Elastix = 0.976 and 0.994. The results demonstrate the robust performance of the diffeomorphic registration method.Clinical relevance This establishes a validation of a registration method that can be used for segmentation of chambers of the heart.

CondenseUNet: A memory-efficient condensely-connected architecture for bi-ventricular blood pool and myocardium segmentation.

With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there has been a paradigm shift in medical technology, thanks to its capability of imaging different structures within the heart without ionizing radiation. However, it is very challenging to conduct pre-operative planning of minimally invasive cardiac procedures without accurate segmentation and identification of the left ventricle (LV), right ventricle (RV) blood-pool, and LV-myocardium. Manual segmentation of those structures, nevertheless, is time-consuming and often prone to error and biased outcomes. Hence, automatic and computationally efficient segmentation techniques are paramount. In this work, we propose a novel memory-efficient Convolutional Neural Network (CNN) architecture as a modification of both CondenseNet, as well as DenseNet for ventricular blood-pool segmentation by introducing a bottleneck block and an upsampling path. Our experiments show that the proposed architecture runs on the Automated Cardiac Diagnosis Challenge (ACDC) dataset using half (50%) the memory requirement of DenseNet and one-twelfth (~ 8%) of the memory requirements of U-Net, while still maintaining excellent accuracy of cardiac segmentation. We validated the framework on the ACDC dataset featuring one healthy and four pathology groups whose heart images were acquired throughout the cardiac cycle and achieved the mean dice scores of 96.78% (LV blood-pool), 93.46% (RV blood-pool) and 90.1% (LV-Myocardium). These results are promising and promote the proposed methods as a competitive tool for cardiac image segmentation and clinical parameter estimation that has the potential to provide fast and accurate results, as needed for pre-procedural planning and / or pre-operative applications.

A Deep Learning Segmentation Approach in Free-Breathing Real-Time Cardiac Magnetic Resonance Imaging.

Objectives The purpose of this study was to segment the left ventricle (LV) blood pool, LV myocardium, and right ventricle (RV) blood pool of end-diastole and end-systole frames in free-breathing cardiac magnetic resonance (CMR) imaging. Automatic and accurate segmentation of cardiac structures could reduce the postprocessing time of cardiac function analysis. MethodWe proposed a novel deep learning network using a residual block for the segmentation of the heart and a random data augmentation strategy to reduce the training time and the problem of overfitting. Automated cardiac diagnosis challenge (ACDC) data were used for training, and the free-breathing CMR data were used for validation and testing. Results The average Dice was 0.919 (LV), 0.806 (myocardium), and 0.818 (RV). The average IoU was 0.860 (LV), 0.699 (myocardium), and 0.761 (RV). Conclusions The proposed method may aid in the segmentation of cardiac images and improves the postprocessing efficiency of cardiac function analysis.

Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks.

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): A novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image; second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation; and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA; four-chamber, 4C; two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained using three-fold cross-validation on a cohort of patients with hypertrophic cardiomyopathy (HCM, N=42) and healthy control subjects (N=21). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch using five-fold cross-validation on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude that this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally.