Multi-part modeling and segmentation of left atrium in C-arm CT for image-guided ablation of atrial fibrillation

Centerline depth world for left atrial appendage orifice localization using reinforcement learning

Left atrial appendage (LAA) occlusion (LAAO) is a minimally invasive implant-based method to prevent cardiovascular stroke in patients with non-valvular atrial fibrillation. Assessing the LAA orifice in preoperative CT angiography plays a crucial role in choosing an appropriate LAAO implant size and a proper C-arm angulation. However, accurate orifice localization is hard because of the high anatomic variation of LAA, and unclear position and orientation of the orifice in available CT views. With the major research focus being on LAA segmentation, the only existing computational method for orifice localization utilized a rule-based decision. Nonetheless, using such a fixed rule may yield high localization error due to the varied anatomy of LAA. While deep learning-based models usually show improvements under such variation, learning an effective localization model is difficult because of the tiny orifice structure compared to the vast search space of CT volume. In this paper, we propose a centerline depth-based reinforcement learning (RL) world for effective orifice localization in a small search space. In our scheme, an RL agent observes the centerline-to-surface distance and navigates through the LAA centerline to localize the orifice. Thus, the search space is significantly reduced facilitating improved localization. The proposed formulation could result in high localization accuracy compared to the expert annotations. Moreover, the localization process takes about 7.3 s which is 18 times more efficient than the existing method. Therefore, this can be a useful aid to physicians during the preprocedural planning of LAAO.

Adversarial-based latent space alignment network for left atrial appendage segmentation in transesophageal echocardiography images

Left atrial appendage (LAA) is a leading cause of atrial fibrillation and thrombosis in cardiovascular disease. Clinicians can rely on LAA occlusion (LAAO) to effectively prevent and treat ischaemic strokes attributed to the LAA. The correct selection of the LAAO is one of the most critical stages in the successful surgical process, which relies on the quantification of the anatomical structure of the LAA for successful intervention in LAAO. In this paper, we propose an adversarial-based latent space alignment framework for LAA segmentation in transesophageal echocardiography (TEE) images by introducing prior knowledge from the label. The proposed method consists of an LAA segmentation network, a label reconstruction network, and a latent space alignment loss. To be specific, we first employ ConvNeXt as the backbone of the segmentation and reconstruction network to enhance the feature extraction capability of the encoder. The label reconstruction network then encodes the prior shape features from the LAA labels to the latent space. The latent space alignment loss consists of the adversarial-based alignment and the contrast learning losses. It can motivate the segmentation network to learn the prior shape features of the labels, thus improving the accuracy of LAA edge segmentation. The proposed method was evaluated on a TEE dataset including 1,783 images and the experimental results showed that the proposed method outperformed other state-of-the-art LAA segmentation methods with Dice coefficient, AUC, ACC, G-mean, and Kappa of 0.831, 0.917, 0.989, 0.911, and 0.825, respectively.

Machine learning applications in cardiac computed tomography: a composite systematic review

Artificial intelligence and machine learning (ML) models are rapidly being applied to the analysis of cardiac computed tomography (CT). We sought to provide an overview of the contemporary advances brought about by the combination of ML and cardiac CT. Six searches were performed in Medline, Embase, and the Cochrane Library up to November 2021 for (i) CT-fractional flow reserve (CT-FFR), (ii) atrial fibrillation (AF), (iii) aortic stenosis, (iv) plaque characterization, (v) fat quantification, and (vi) coronary artery calcium score. We included 57 studies pertaining to the aforementioned topics. Non-invasive CT-FFR can accurately be estimated using ML algorithms and has the potential to reduce the requirement for invasive angiography. Coronary artery calcification and non-calcified coronary lesions can now be automatically and accurately calculated. Epicardial adipose tissue can also be automatically, accurately, and rapidly quantified. Effective ML algorithms have been developed to streamline and optimize the safety of aortic annular measurements to facilitate pre-transcatheter aortic valve replacement valve selection. Within electrophysiology, the left atrium (LA) can be segmented and resultant LA volumes have contributed to accurate predictions of post-ablation recurrence of AF. In this review, we discuss the latest studies and evolving techniques of ML and cardiac CT.

Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images

Cerebrovascular segmentation is important in various clinical applications, such as surgical planning and computer-aided diagnosis. In order to achieve high segmentation performance, three challenging problems should be taken into consideration: (1) large variations in vascular anatomies and voxel intensities; (2) severe class imbalance between foreground and background voxels; (3) image noise with different magnitudes. Limited accuracy was achieved without considering these challenges in deep learning-based methods for cerebrovascular segmentation. To overcome the limitations, we propose an end-to-end adversarial model called FiboNet-VANGAN. Specifically, our contributions can be summarized as follows: (1) to relieve the first problem mentioned above, a discriminator is proposed to regularize for voxel-wise distribution consistency between the segmentation results and the ground truth; (2) to mitigate the problem of class imbalance, we propose to use the addition of cross-entropy and Dice coefficient as the loss function of the generator. Focal loss is utilized as the loss function of the discriminator; (3) a new feature connection is proposed, based on which a generator called FiboNet is built. By incorporating Dice coefficient in the training of FiboNet, noise robustness can be improved by a large margin. We evaluate our method on a healthy magnetic resonance angiography (MRA) dataset to validate its effectiveness. A brain atrophy MRA dataset is also collected to test the performance of each method on abnormal cases. Results show that the three problems in cerebrovascular segmentation mentioned above can be alleviated and high segmentation accuracy can be achieved on both datasets using our method.

Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Fully automated segmentation of the left atrium, pulmonary veins, and left atrial appendage from magnetic resonance angiography by joint-atlas-optimization

PURPOSE

Atrial fibrillation (AF) originating from the left atrium (LA) and pulmonary veins (PVs) is the most prevalent cardiac electrophysiological disorder. Accurate segmentation and quantification of the LA chamber, PVs, and left atrial appendage (LAA) provides clinically important references for treatment of AF patients. The purpose of this work is to realize objective segmentation of the LA chamber, PVs, and LAA in an accurate and fully automated manner.

METHODS

In this work, we proposed a new approach, named joint-atlas-optimization, to segment the LA chamber, PVs, and LAA from magnetic resonance angiography (MRA) images. We formulated the segmentation as a single registration problem between the given image and all N atlas images, instead of N separate registration between the given image and an individual atlas image. Level sets was applied to refine the atlas-based segmentation. Using the publically available LA benchmark database, we compared the proposed joint-atlas-optimization approach to the conventional pairwise atlas approach and evaluated the segmentation performance in terms of Dice index and surface-to-surface (S2S) distance to the manual ground truth.

RESULTS

The proposed joint-atlas-optimization method showed systemically improved accuracy and robustness over the pairwise atlas approach. The Dice of LA segmentation using joint-atlas-optimization was 0.93 ± 0.04, compared to 0.91 ± 0.04 by the pairwise approach (P < 0.05). The mean S2S distance was 1.52 ± 0.58 mm, compared to 1.83 ± 0.75 mm (P < 0.05). In particular, it produced significantly improved segmentation accuracy of the LAA and PVs, the small distant part in LA geometry that is intrinsically difficult to segment using the conventional pairwise approach. The Dice of PVs segmentation was 0.69 ± 0.16, compared to 0.49 ± 0.15 (P < 0.001). The Dice of LAA segmentation was 0.91 ± 0.03, compared to 0.88 ± 0.05 (P < 0.01).

CONCLUSION

The proposed joint-atlas optimization method can segment the complex LA geometry in a fully automated manner. Compared to the conventional atlas approach in a pairwise manner, our method improves the performance on small distal parts of LA, for example, PVs and LAA, the geometrical and quantitative assessment of which is clinically interesting.

Left Atrium Wall-mapping Application for Wall Thickness Visualisation

The measurement method for the LA wall thickness (WT) using cardiac computed tomography (CT) is observer dependent and cannot provide a rapid and comprehensive visualisation of the global LA WT. We aim to develop a LA wall-mapping application to display the global LA WT on a coplanar plane. The accuracy, intra-observer, and inter-observer reproducibility of the application were validated using digital/physical phantoms, and CT images of eight patients. This application on CT-based LA WT measures were further validated by testing six pig cardiac specimens. To evaluate its accuracy, the expanded maps of the physical phantom and pig LA were generated from the CT images and compared with the expanded map of the digital phantom and LA wall of pig heart. No significant differences (p > 0.05) were found between physical phantom and digital phantom as well as pig heart specimen and CT images using our application. Moreover, the analysis was based on the LA physical phantom or images of clinical patients; the results consistently demonstrated high intra-observer reproducibility (ICC > 0.9) and inter-observer reproducibility (ICC > 0.8) and showed good correlation between measures of pig heart specimen and CT data (r = 0.96, p < 0.001). The application can process and analyse the LA architecture for further visualisation and quantification.

Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets

Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM'13 workshop, in conjunction with MICCAI'13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.