Coronary Artery Segmentation Research Articles

Ensembled-SAMs for Enhanced Small Coronary Artery Segmentation in CCTA Images.

Accurate coronary artery segmentation is crucial for quantitative analysis of coronary arteries in noninvasive coronary computed tomography angiography (CCTA) images. However, current segmentation algorithms often have unsatisfactory recall due to the small size and complex morphology of coronary arteries, particularly in the distal segments. To address this issue, we introduce a new fully automated method named Ensembled-SAMs, which harnesses the strengths of the Segment Anything Model (SAM) and the no-new-U-Net (nnU-Net). First, noisy bounding box prompts are automatically generated by a vesselness algorithm that highlights the tubular structures in the CCTA images. These noisy prompts are then used to fine-tune the SAM and its two variants separately. The SAM variants introduce a classification head in their mask decoder to alleviate the false positives. In addition, an nnU-Net segmentation network is trained from scratch. Finally, the outputs of the SAMs and the nnU-Net are strategically aggregated to obtain the final segmentation result. Experiments on both a self-built dataset and the public Automated Segmentation of Coronary Arteries (ASOCA) challenge dataset demonstrate that the proposed Ensembled-SAMs outperforms the state-of-the-arts, achieving precise segmentation of coronary arteries, with particular enhancement in delineating small coronary artery segments.

Myocardial perfusion segmentation and partitioning methods in personalized models of coronary blood flow

Abstract In this work we present methods and algorithms for construction of a personalized model of coronary haemodynamics based on computed tomography images. This model provides estimations of fractional flow reserve, coronary flow reserve, and instantaneous wave-free ratio taking into account transmural perfusion ratio indices obtained from perfusion images. The presented pipeline consists of the following steps: aorta segmentation, left ventricle wall segmentation, coronary arteries segmentation, construction of 1D network of vessels, partitioning of left ventricle wall, and personalization of the model parameters. We focus on a new technique, which generates specific perfusion zones and computes transmural perfusion ratio according to the quality of available medical images with a limited number of visible terminal coronary vessels. Numerical experiments show that accurate evaluation of stenosis before precutaneous coronary intervention should take into account both fractional flow reserve indices and myocardial perfusion, as well as other indices, in order to avoid misdiagnosis. The presented model provides better understanding of the background of clinical recommendations for possible surgical treatment of a stenosed coronary artery.

AVDNet: Joint coronary artery and vein segmentation with topological consistency

Coronary CT angiography (CCTA) is an effective and non-invasive method for coronary artery disease diagnosis. Extracting an accurate coronary artery tree from CCTA image is essential for centerline extraction, plaque detection, and stenosis quantification. In practice, data quality varies. Sometimes, the arteries and veins have similar intensities and locate closely, which may confuse segmentation algorithms, even deep learning based ones, to obtain accurate arteries. However, it is not always feasible to re-scan the patient for better image quality. In this paper, we propose an artery and vein disentanglement network (AVDNet) for robust and accurate segmentation by incorporating the coronary vein into the segmentation task. This is the first work to segment coronary artery and vein at the same time. The AVDNet consists of an image based vessel recognition network (IVRN) and a topology based vessel refinement network (TVRN). IVRN learns to segment the arteries and veins, while TVRN learns to correct the segmentation errors based on topology consistency. We also design a novel inverse distance weighted dice (IDD) loss function to recover more thin vessel branches and preserve the vascular boundaries. Extensive experiments are conducted on a multi-center dataset of 700 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method by comparing it with state-of-the-art methods and different variants. Prediction results of the AVDNet on the Automated Segmentation of Coronary Artery Challenge dataset are avaliabel at https://github.com/WennyJJ/Coronary-Artery-Vein-Segmentation for follow-up research.

Ori-Net: Orientation-guided Neural Network for Automated Coronary Arteries Segmentation

Coronary artery disease (CAD) is one of the diseases with high mortality, and its diagnosis is often facilitated by coronary artery segmentation in coronary computed tomography angiography (CCTA) images. However, due to the low contrast, imaging artifacts, and contrast agents in CCTA images, coronary artery segmentation models often produce fragmented segmentations due to the lack of coronary artery topology knowledge. We found that coronary arteries have cylinder-like structures that can be represented as envelopes reconstructed by orientation-guided spheres of different radii. To fully use this prior knowledge, a novel orientation-guided neural networks model, called Ori-Net, is proposed to improve the connectivity of coronary artery segmentation in CCTA images. First, Ori-Net simultaneously produces the coarse segmentation, radius, and orientation of the coronary artery in a multi-task learning framework. Then, we propose an orientation-guide tracking method using the predicted orientation and radius. It reconstructs the coronary artery iteratively, and the reconstruction is fused with the coarse segmentation to improve the segmentation performance further. The proposed method provides a new way to leverage the coronary artery shape prior. In addition, we extend the connected component ratio metric for volume data to evaluate the connectivity of the segmentation results. In the experiments, we compared the proposed Ori-Net with state-of-the-art methods on two coronary artery segmentation datasets. It demonstrates that (1) Ori-Net improves over the state-of-art by 10% in connectivity metric. (2) Ori-Net can significantly boost coronary artery segmentation performance, for example, by over 5% improvement as measured by Dice.

ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images.

Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution. Clinically, segmentation of coronary arteries is essential for the diagnosis and quantification of coronary artery disease. Recently, a variety of works have been proposed to address this problem. However, on one hand, most works rely on in-house datasets, and only a few works published their datasets to the public which only contain tens of images. On the other hand, their source code have not been published, and most follow-up works have not made comparison with existing works, which makes it difficult to judge the effectiveness of the methods and hinders the further exploration of this challenging yet critical problem in the community. In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images. In addition, we have implemented a benchmark in which we have tried our best to implement several typical existing methods. Furthermore, we propose a strong baseline method which combines multi-scale patch fusion and two-stage processing to extract the details of vessels. Comprehensive experiments show that the proposed method achieves better performance than existing works on the proposed large-scale dataset. The benchmark and the dataset are published at https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.

TCT-254 Coronary Artery Segmentation and Tissue Characterization Using Deep Learning: “Virtual” IB-IVUS

TCT-254 Coronary Artery Segmentation and Tissue Characterization Using Deep Learning: “Virtual” IB-IVUS

Positive-unlabeled learning for coronary artery segmentation in CCTA images

Accurate three-dimensional (3D) segmentation of the coronary artery is an essential step in the quantitative analysis of the coronary arteries. However, due to the small size and complex morphology of the coronary arteries, voxel-by-voxel labeling of the complete coronary artery in 3D computed coronary tomography angiography images is both difficult and laborious. To alleviate the workload of annotating, it is possible to randomly label only a fraction of the positive samples and leave all remaining instances unlabeled, known as the positive-unlabeled (PU) learning problem. Due to the presence of coronary artery-like structures and the absence of negative annotations, we propose a novel sample-selection-based PU learning method for coronary artery segmentation. Specifically, only pseudo-negative labels (PNLs) are generated during the self-training process, and all data are further exploited implicitly using the teacher–student (TS) framework. To address the difficulty of detecting tiny coronary artery branches, we propose a post-processing method by exploiting the variance of multi-scale features in the inference stage. Extensive experiments were conducted on a self-constructed dataset and the publicly available ASOCA dataset. The results demonstrate that our proposed method performs better than baseline supervised and state-of-the-art PU learning methods. Notably, even in extreme cases where more than 80% of annotations are missing, our method still achieves significant gains. When the proportion of missing annotations is relatively low, our method even outperforms the backbone trained with ground truth annotations.

Automatic coronary artery segmentation of CCTA images using UNet with a local contextual transformer.

Coronary artery segmentation is an essential procedure in the computer-aided diagnosis of coronary artery disease. It aims to identify and segment the regions of interest in the coronary circulation for further processing and diagnosis. Currently, automatic segmentation of coronary arteries is often unreliable because of their small size and poor distribution of contrast medium, as well as the problems that lead to over-segmentation or omission. To improve the performance of convolutional-neural-network (CNN) based coronary artery segmentation, we propose a novel automatic method, DR-LCT-UNet, with two innovative components: the Dense Residual (DR) module and the Local Contextual Transformer (LCT) module. The DR module aims to preserve unobtrusive features through dense residual connections, while the LCT module is an improved Transformer that focuses on local contextual information, so that coronary artery-related information can be better exploited. The LCT and DR modules are effectively integrated into the skip connections and encoder-decoder of the 3D segmentation network, respectively. Experiments on our CorArtTS2020 dataset show that the dice similarity coefficient (DSC), Recall, and Precision of the proposed method reached 85.8%, 86.3% and 85.8%, respectively, outperforming 3D-UNet (taken as the reference among the 6 other chosen comparison methods), by 2.1%, 1.9%, and 2.1%.

The Application of Deep Learning for the Segmentation and Classification of Coronary Arteries.

In recent years, the prevalence of coronary artery disease (CAD) has become one of the leading causes of death around the world. Accurate stenosis detection of coronary arteries is crucial for timely treatment. Cardiologists use visual estimations when reading coronary angiography images to diagnose stenosis. As a result, they face various challenges which include high workloads, long processing times and human error. Computer-aided segmentation and classification of coronary arteries, as to whether stenosis is present or not, significantly reduces the workload of cardiologists and human errors caused by manual processes. Moreover, deep learning techniques have been shown to aid medical experts in diagnosing diseases using biomedical imaging. Thus, this study proposes the use of automatic segmentation of coronary arteries using U-Net, ResUNet-a, UNet++, models and classification using DenseNet201, EfficientNet-B0, Mobilenet-v2, ResNet101 and Xception models. In the case of segmentation, the comparative analysis of the three models has shown that U-Net achieved the highest score with a 0.8467 Dice score and 0.7454 Jaccard Index in comparison with UNet++ and ResUnet-a. Evaluation of the classification model's performances has shown that DenseNet201 performed better than other pretrained models with 0.9000 accuracy, 0.9833 specificity, 0.9556 PPV, 0.7746 Cohen's Kappa and 0.9694 Area Under the Curve (AUC).

Angiography-based coronary flow reserve: The feasibility of automatic computation by artificial intelligence.

Coronary flow reserve (CFR) has prognostic value in patients with coronary artery disease. However, its measurement is complex, and automatic methods for CFR computation are scarcely available. We developed an automatic method for CFR computation based on coronary angiography and assessed its feasibility. Coronary angiographies from the Corelab database were annotated by experienced analysts. A convolutional neural network (CNN) model was trained for automatic segmentation of the main coronary arteries during contrast injection. The segmentation performance was evaluated using 5-fold cross-validation. Subsequently, the CNN model was implemented into a prototype software package for automatic computation of the CFR (CFRauto) and applied on a different sample of patients with angiographies performed both at rest and during maximal hyperemia, to assess the feasibility of CFRauto and its agreement with the manual computational method based on frame count (CFRmanual). Altogether, 137,126 images of 5913 angiographic runs from 2407 patients were used to develop and evaluate the CNN model. Good segmentation performance was observed. CFRauto was successfully computed in 136 out of 149 vessels (91.3%). The average analysis time to derive CFRauto was 18.1 ± 10.3 s per vessel. Moderate correlation (r = 0.51, p < 0.001) was observed between CFRauto and CFRmanual, with a mean difference of 0.12 ± 0.53. Automatic computation of the CFR based on coronary angiography is feasible. This method might facilitate wider adoption of coronary physiology in the catheterization laboratory to assess microcirculatory function.

Automated Segmentation of Coronary Arteries using Attention-Gated UNet for Precise Diagnosis

Automated Segmentation of Coronary Arteries using Attention-Gated UNet for Precise Diagnosis

DBCU-Net: deep learning approach for segmentation of coronary angiography images.

Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD). However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which makes it difficult for blood vessels segmentation. In this paper, we propose a DBCU-Net for automatic segmentation of CAG images, which is an extension of U-Net, DenseNet with bi-directional ConvLSTM(BConvLSTM). The main contribution of our network is that instead of convolution in the feature extraction of U-Net, we incorporate dense connectivity and the bi-directional ConvLSTM to highlight salient features. We conduct our experiment on our private dataset, and achieve average Accuracy, Precision, Recall and F1-score for coronary artery segmentation of 0.985, 0.913, 0.847 and 0.879 respectively.

A novel multi-attention, multi-scale 3D deep network for coronary artery segmentation.

Automatic segmentation of coronary arteries provides vital assistance to enable accurate and efficient diagnosis and evaluation of coronary artery disease (CAD). However, the task of coronary artery segmentation (CAS) remains highly challenging due to the large-scale variations exhibited by coronary arteries, their complicated anatomical structures and morphologies, as well as the low contrast between vessels and their background. To comprehensively tackle these challenges, we propose a novel multi-attention, multi-scale 3D deep network for CAS, which we call CAS-Net. Specifically, we first propose an attention-guided feature fusion (AGFF) module to efficiently fuse adjacent hierarchical features in the encoding and decoding stages to capture more effectively latent semantic information. Then, we propose a scale-aware feature enhancement (SAFE) module, aiming to dynamically adjust the receptive fields to extract more expressive features effectively, thereby enhancing the feature representation capability of the network. Furthermore, we employ the multi-scale feature aggregation (MSFA) module to learn a more distinctive semantic representation for refining the vessel maps. In addition, considering that the limited training data annotated with a quality golden standard are also a significant factor restricting the development of CAS, we construct a new dataset containing 119 cases consisting of coronary computed tomographic angiography (CCTA) volumes and annotated coronary arteries. Extensive experiments on our self-collected dataset and three publicly available datasets demonstrate that the proposed method has good segmentation performance and generalization ability, outperforming multiple state-of-the-art algorithms on various metrics. Compared with U-Net3D, the proposed method significantly improves the Dice similarity coefficient (DSC) by at least 4% on each dataset, due to the synergistic effect among the three core modules, AGFF, SAFE, and MSFA. Our implementation is released at https://github.com/Cassie-CV/CAS-Net.

A U-Shaped Network Based on Multi-level Feature and Dual-Attention Coordination Mechanism for Coronary Artery Segmentation of CCTA Images.

Computed tomography coronary angiography (CCTA) images provide optimal visualization of coronary arteries to aid in diagnosing coronary heart disease (CHD). With the deep convolutional neural network, this work aims to develop an intelligent and lightweight coronary artery segmentation algorithm that can be deployed in hospital systems to assist clinicians in quantitatively analyzing CHD. With the multi-level feature fusion, we proposed Dual-Attention Coordination U-Net (DAC-UNet) that achieves automated coronary artery segmentation in 2D CCTA images. The coronary artery occupies a small region, and the foreground and background are extremely unbalanced. For this reason, the more original information can be retained by fusing related features between adjacent layers, which is conducive to recovering the small coronary artery area. The dual-attention coordination mechanism can select valid information and filter redundant information. Moreover, the complementation and coordination of double attention factors can enhance the integrity of features of coronary arteries, reduce the interference of non-coronary arteries, and prevent over-learning. With gradual learning, the balanced character of double attention factors promotes the generalization ability of the model to enhance coronary artery localization and contour detail segmentation. Compared with existing related segmentation methods, our method achieves a certain degree of improvement in 2D CCTA images for the segmentation accuracy of coronary arteries with a mean Dice index of 0.7920. Furthermore, the method can obtain relatively accurate results even in a small sample dataset and is easy to implement and deploy, which is promising. The code is available at: https://github.com/windfly666/Segmentation . Our method can capture the coronary artery structure end-to-end, which can be used as a fundamental means for automatic detection of coronary artery stenosis, blood flow reserve fraction analysis, and assisting clinicians in diagnosing CHD.

Contrastive Semi-Supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures.

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for medical image segmentation, yet need plenty of manual annotations for training. Semi-Supervised Learning (SSL) methods are promising to reduce the requirement of annotations, but their performance is still limited when the dataset size and the number of annotated images are small. Leveraging existing annotated datasets with similar anatomical structures to assist training has a potential for improving the model's performance. However, it is further challenged by the cross-anatomy domain shift due to the image modalities and even different organs in the target domain. To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain. We use Domain-Specific Batch Normalization (DSBN) to individually normalize feature maps for the two anatomical domains, and propose a cross-domain contrastive learning strategy to encourage extracting domain invariant features. They are integrated into a Self-Ensembling Mean-Teacher (SE-MT) framework to exploit unlabeled target domain images with a prediction consistency constraint. Extensive experiments show that our CS-CADA is able to solve the challenging cross-anatomy domain shift problem, achieving accurate segmentation of coronary arteries in X-ray images with the help of retinal vessel images and cardiac MR images with the help of fundus images, respectively, given only a small number of annotations in the target domain. Our code is available at https://github.com/HiLab-git/DAG4MIA.

Coronary Artery Segmentation Based on Transfer Learning and UNet Architecture on Computed Tomography Coronary Angiography Images

Coronary Artery Segmentation Based on Transfer Learning and UNet Architecture on Computed Tomography Coronary Angiography Images

Deep learning applications in coronary anatomy imaging: a systematic review and meta-analysis.

The application of deep learning on medical imaging is growing in prevalence in the recent literature. One of the most studied areas is coronary artery disease (CAD). Imaging of coronary artery anatomy is fundamental, which has led to a high number of publications describing a variety of techniques. The aim of this systematic review is to review the evidence behind the accuracy of deep learning applications in coronary anatomy imaging. The search for the relevant studies, which applied deep learning on coronary anatomy imaging, was performed in a systematic approach on MEDLINE and EMBASE databases, followed by reviewing of abstracts and full texts. The data from the final studies was retrieved using data extraction forms. A meta-analysis was performed on a subgroup of studies, which looked at fractional flow reserve (FFR) prediction. Heterogeneity was tested using tau2, I2 and Q tests. Finally, a risk of bias was performed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS) approach. A total of 81 studies met the inclusion criteria. The most common imaging modality was coronary computed tomography angiography (CCTA) (58%) and the most common deep learning method was convolutional neural network (CNN) (52%). The majority of studies demonstrated good performance metrics. The most common outputs were focused on coronary artery segmentation, clinical outcome prediction, coronary calcium quantification and FFR prediction, and most studies reported area under the curve (AUC) of ≥80%. The pooled diagnostic odds ratio (DOR) derived from 8 studies looking at FFR prediction using CCTA was 12.5 using the Mantel-Haenszel (MH) method. There was no significant heterogeneity amongst studies according to Q test (P=0.2496). Deep learning has been used in many applications on coronary anatomy imaging, most of which are yet to be externally validated and prepared for clinical use. The performance of deep learning, especially CNN models, proved to be powerful and some applications have already translated into medical practice, such as computed tomography (CT)-FFR. These applications have the potential to translate technology into better care of CAD patients.

Optimized Adaptive Frangi-based Coronary Artery Segmentation using Genetic Algorithm

Optimized Adaptive Frangi-based Coronary Artery Segmentation using Genetic Algorithm

Two-stage training strategy combined with neural network for segmentation of internal mammary artery graft

Cardiovascular disease is a major health condition affecting the global population, leading to 420 million diagnoses and nearly 20 million deaths annually. Coronary heart disease is one of the most prevalent cardiovascular diseases, and its diagnosis and treatment creates a large social and economic burden, necessitating a large number of imaging evaluations and repetitive manual assessments by expert radiologists. Recently, deep learning-based methods have demonstrated excellent performance in coronary artery recognition (coronary artery segmentation and centerline extraction, etc.). However, current research focuses on CTA and MRI data types, and for inexperienced cardiologists, even if the neural network gives accurate segmentation results, it is impossible to determine its specific location in the heart. In response to the above problems, we constructed a novel dataset, CardiacVessel, which contains 2D slices from CTA images through 3D reconstruction techniques, and performed pixel-level annotations of vessels. We then propose a simple and effective two-stage training strategy for internal mammary artery graft segmentation. We conduct extensive experiments to compare the performance of various CNN- and transformer-based network architectures. The proposed two-stage training strategy can significantly improve network performance.

Automatic Coronary Artery Segmentation of CCTA Images With an Efficient Feature-Fusion-and-Rectification 3D-UNet.

Automatic coronary artery segmentation is of great value in diagnosing coronary disease. In this paper, we propose an automatic coronary artery segmentation method for coronary computerized tomography angiography (CCTA) images based on a deep convolutional neural network. The proposed method consists of three steps. First, to improve the efficiency and effectiveness of the segmentation, a 2D DenseNet classification network is utilized to screen out the non-coronary-artery slices. Second, we propose a coronary artery segmentation network based on the 3D-UNet, which is capable of extracting, fusing and rectifying features efficiently for accurate coronary artery segmentation. Specifically, in the encoding process of the 3D-UNet network, we adapt the dense block into the 3D-UNet so that it can extract rich and representative features for coronary artery segmentation; In the decoding process, 3D residual blocks with feature rectification capability are applied to improve the segmentation quality further. Third, we introduce a Gaussian weighting method to obtain the final segmentation results. This operation can highlight the more reliable segmentation results at the center of the 3D data blocks while weakening the less reliable segmentations at the block boundary when merging the segmentation results of spatially overlapping data blocks. Experiments demonstrate that our proposed method achieves a Dice Similarity Coefficient (DSC) value of 0.826 on a CCTA dataset constructed by us. The code of the proposed method is available at https://github.com/alongsong/3D_CAS.