A novel end-to-end deep learning solution for coronary artery segmentation from CCTA

Deep learning techniques for automated coronary artery segmentation and coronary artery disease detection: A systematic review of the last decade (2013-2024)

BACKGROUND

Coronary artery disease (CAD) is the most common cardiovascular disease, exacting high morbidity and mortality worldwide. CAD is detected on coronary artery imaging; coronary artery segmentation (CAS) of the images is essential for coronary lesion characterization. Both CAD detection and CAS require expert input, are labor-intensive, and error-prone.

OBJECTIVES

Deep learning (DL) techniques have achieved significant success in CAS and CAD detection, with many studies published recently. This study is an up-to-date systematic review of research on automated DL models for CAS and CAD detection in the past decade (2013-2024).

METHOD

Using PRISMA methodology, an initial literature search of 1,589 publications was conducted, from which 97 high-impact Q1 studies were selected based on pre-defined eligibility criteria. These studies were analyzed in terms of DL techniques employed, datasets, modalities, and performance metrics.

RESULTS

Of the 97 studies, most of which were published after 2016, 47 focused on CAS, 49 on CAD detection, and one on both tasks. CNN-based models were dominant in both domains. For CAS, CCTA was the most frequently used input modality, and U-Net was employed in 38 out of 48 studies, with recent works incorporating attention mechanisms and graph neural networks. ASOCA was the most widely used benchmark dataset. For CAD detection, ECG was the most common modality, with 45 out of 50 studies utilizing CNNs, and 20 of those relying purely on CNN architectures. Hybrid and multimodal models have become more prominent in recent years.

CONCLUSION

This review identified several challenges, including limited public datasets, variability in performance metrics, and model complexity. Future studies should focus on larger, diverse datasets and lightweight models integrating explainable artificial intelligence and uncertainty quantification to improve clinical applicability.

Coronary artery disease classification using ConvMixer based classifier from CT angiography images

Coronary artery disease (CAD) has recently emerged as a predominant source of morbidity and death worldwide. Assessing the existence and severity of CAD in people is crucial for determining the optimal treatment strategy. Currently, computed tomography (CT) delivers excellent spatial resolution pictures of the heart and coronary arteries at a rapid pace. Conversely, several problems exist in the analysis of cardiac CT images for indications of CAD. Research investigations employ machine learning (ML) and deep learning (DL) techniques to achieve high accuracy and consistent performance, hence addressing existing restrictions. This research proposes convMixer with median filter and morphological operations for the classification of the coronary artery disease from computed tomography angiography images. A total of 5,959 CT angiography images were used for classification. The model achieved an accuracy of 96.30%, sensitivity of 94.39%, and specificity of 99.16% for combination of the morphological operations and convMixer, 88.92% of accuracy and 89.56% of sensitivity, and 93.10% of specificity for the combination of median filter and convMixer and 94.63% of accuracy, 95.82% of sensitivity, and 93.10% of specificity for convMixer. The findings indicate the viability of automated non-invasive identification of individuals necessitating invasive coronary angiography images and maybe future coronary artery operations. This may potentially decrease the number of people who receive invasive coronary angiography images. Lastly, post-image analysis was conducted using DL heat maps to understand the decisions made by the proposed model. The proposed integrated DL intelligent system enhances the efficiency of illness diagnosis, reduces manual involvement in diagnostic processes, supports medical professionals in diagnostic decision-making, and offers supplementary techniques for future medical diagnostic systems based on coronary angioplasty.

Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine-learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using HiP-CT as part of the Human Organ Atlas Project. HiP-CT pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering a resolution of around 20 μm/voxel and enabling highly detailed localised zooms up to 1-2 μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice similarity coefficient (DSC) scores of 0.9523, 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline DSC ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydrostatic pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Our study establishes the benchmark across various evaluation metrics, for vascular segmentation of HiP-CT imaging data, an imaging technology that has the potential to substantively shift our understanding of human vascular networks.

A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software

Automatic segmentation of the coronary artery using coronary computed tomography angiography (CCTA) images can facilitate several analyses related to coronary artery disease (CAD). Accurate segmentation of the lumen or plaque region is one of the most important factors. This study aimed to analyze the performance of the coronary artery segmentation of a software platform with a deep learning-based location-adaptive threshold method (DL-LATM) against commercially available software platforms using CCTA. The dataset from intravascular ultrasound (IVUS) of 26 vessel segments from 19 patients was used as the gold standard to evaluate the performance of each software platform. Statistical analyses (Pearson correlation coefficient [PCC], intraclass correlation coefficient [ICC], and Bland-Altman plot) were conducted for the lumen or plaque parameters by comparing the dataset of each software platform with IVUS. The software platform with DL-LATM showed the bias closest to zero for detecting lumen volume (mean difference = -9.1 mm3, 95% confidence interval [CI] = -18.6 to 0.4 mm3) or area (mean difference = -0.72 mm2, 95% CI = -0.80 to -0.64 mm2) with the highest PCC and ICC. Moreover, lumen or plaque area in the stenotic region was analyzed. The software platform with DL-LATM showed the bias closest to zero for detecting lumen (mean difference = -0.07 mm2, 95% CI = -0.16 to 0.02 mm2) or plaque area (mean difference = 1.70 mm2, 95% CI = 1.37 to 2.03 mm2) in the stenotic region with significantly higher correlation coefficient than other commercially available software platforms (p < 0.001). The result shows that the software platform with DL-LATM has the potential to serve as an aiding system for CAD evaluation.

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%.

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.

Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images

Coronary artery disease (CAD) is one of the major causes of fatalities across the globe. The recent developments in convolutional neural networks (CNN) allow researchers to detect CAD from computed tomography (CT) images. The CAD detection model assists physicians in identifying cardiac disease at earlier stages. The recent CAD detection models demand a high computational cost and a more significant number of images. Therefore, this study intends to develop a CNN-based CAD detection model. The researchers apply an image enhancement technique to improve the CT image quality. The authors employed You look only once (YOLO) V7 for extracting the features. Aquila optimization is used for optimizing the hyperparameters of the UNet++ model to predict CAD. The proposed feature extraction technique and hyperparameter tuning approach reduces the computational costs and improves the performance of the UNet++ model. Two datasets are utilized for evaluating the performance of the proposed CAD detection model. The experimental outcomes suggest that the proposed method achieves an accuracy, recall, precision, F1-score, Matthews correlation coefficient, and Kappa of 99.4, 98.5, 98.65, 98.6, 95.35, and 95 and 99.5, 98.95, 98.95, 98.95, 96.35, and 96.25 for datasets 1 and 2, respectively. In addition, the proposed model outperforms the recent techniques by obtaining the area under the receiver operating characteristic and precision-recall curve of 0.97 and 0.95, and 0.96 and 0.94 for datasets 1 and 2, respectively. Moreover, the proposed model obtained a better confidence interval and standard deviation of [98.64-98.72] and 0.0014, and [97.41-97.49] and 0.0019 for datasets 1 and 2, respectively. The study's findings suggest that the proposed model can support physicians in identifying CAD with limited resources.