The auto segmentation for cardiac structures using a dual‐input deep learning network based on vision saliency and transformer

Wang, Jing; Wang, Shuyu; Liang, Wei; Zhang, Nan; Zhang, Yan

doi:10.1002/acm2.13597

Cited by 4 publications

(1 citation statement)

References 44 publications

(64 reference statements)

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“…When evaluating the degree of coronary artery stenosis, deep learning analysis of the left ventricular myocardium can improve diagnostic performance and increase the specificity of resting CTA. [51][52][53][54]…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning

Zhang

2023

Angiology

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The aim of this review is to introduce some applications of artificial intelligence (AI) algorithms for the detection and quantification of coronary stenosis using computed tomography angiography (CTA). The realization of automatic/semi-automatic stenosis detection and quantification includes the following steps: vessel central axis extraction, vessel segmentation, stenosis detection, and quantification. Many new AI techniques, such as machine learning and deep learning, have been widely used in medical image segmentation and stenosis detection. This review also summarizes the recent progress regarding coronary stenosis detection and quantification, and discusses the development trends in this field. Through evaluation and comparison, researchers can better understand the research frontier in related fields, compare the advantages and disadvantages of various methods, and better optimize the new technologies. Machine learning and deep learning will promote the process of automatic detection and quantification of coronary artery stenosis. However, the machine learning and the deep learning methods need a large amount of data, so they also face some challenges because of the lack of professional image annotations (manually add labels by experts).

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Section: Deep Learning Methodsmentioning

confidence: 99%

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning

Zhang

2023

Angiology

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The auto segmentation for cardiac structures using a dual‐input deep learning network based on vision saliency and transformer

Wang

Liang³

et al. 2022

J Applied Clin Med Phys

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Accurate segmentation of cardiac structures on coronary CT angiography (CCTA) images is crucial for the morphological analysis, measurement, and functional evaluation. In this study, we achieve accurate automatic segmentation of cardiac structures on CCTA image by adopting an innovative deep learning method based on visual attention mechanism and transformer network, and its practical application value is discussed. Methods: We developed a dual-input deep learning network based on visual saliency and transformer (VST), which consists of self -attention mechanism for cardiac structures segmentation. Sixty patients' CCTA subjects were randomly selected as a development set, which were manual marked by an experienced technician. The proposed vision attention and transformer mode was trained on the patients CCTA images, with a manual contour-derived binary mask used as the learning-based target.We also used the deep supervision strategy by adding auxiliary losses. The loss function of our model was the sum of the Dice loss and cross-entropy loss. To quantitatively evaluate the segmentation results, we calculated the Dice similarity coefficient (DSC) and Hausdorff distance (HD). Meanwhile, we compare the volume of automatic segmentation and manual segmentation to analyze whether there is statistical difference. Results: Fivefold cross-validation was used to benchmark the segmentation method. The results showed the left ventricular myocardium (LVM, DSC = 0.87), the left ventricular (LV, DSC = 0.94), the left atrial (LA, DSC = 0.90), the right ventricular (RV,DSC = 0.92),the right atrial (RA,DSC = 0.91),and the aortic (AO, DSC = 0.96). The average DSC was 0.92, and HD was 7.2 ± 2.1 mm. In volume comparison, except LVM and LA (p < 0.05), there was no significant statistical difference in other structures. Proposed method for structural segmentation fit well with the true profile of the cardiac substructure, and the model prediction results closed to the manual annotation. Conclusions: The adoption of the dual-input and transformer architecture based on visual saliency has high sensitivity and specificity to cardiac structures segmentation, which can obviously improve the accuracy of automatic substructure segmentation. This is of gr K E Y W O R D S coronary CT angiography, deep learning, self -attention, transformers, visual attention mechanismThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Stereotactic arrhythmia radioablation for ventricular tachycardia: a review of clinical trials and emerging roles of imaging

Kawamura,

Shimojo,

Tatsugami

et al. 2024

Journal of Radiation Research

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Ventricular tachycardia (VT) is a severe arrhythmia commonly treated with implantable cardioverter defibrillators, antiarrhythmic drugs and catheter ablation (CA). Although CA is effective in reducing recurrent VT, its impact on survival remains uncertain, especially in patients with extensive scarring. Stereotactic arrhythmia radioablation (STAR) has emerged as a novel treatment for VT in patients unresponsive to CA, leveraging techniques from stereotactic body radiation therapy used in cancer treatments. Recent clinical trials and case series have demonstrated the short-term efficacy and safety of STAR, although long-term outcomes remain unclear. Imaging techniques, such as electroanatomical mapping, contrast-enhanced magnetic resonance imaging and nuclear imaging, play a crucial role in treatment planning by identifying VT substrates and guiding target delineation. However, challenges persist owing to the complex anatomy and variability in target volume definitions. Advances in imaging and artificial intelligence are expected to improve the precision and efficacy of STAR. The exact mechanisms underlying the antiarrhythmic effects of STAR, including potential fibrosis and improvement in cardiac conduction, are still being explored. Despite its potential, STAR should be cautiously applied in prospective clinical trials, with a focus on optimizing dose delivery and understanding long-term outcomes. Collaborative efforts are necessary to standardize treatment strategies and enhance the quality of life for patients with refractory VT.

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The auto segmentation for cardiac structures using a dual‐input deep learning network based on vision saliency and transformer

Cited by 4 publications

References 44 publications

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning

Stenosis Detection and Quantification of Coronary Artery Using Machine Learning and Deep Learning

The auto segmentation for cardiac structures using a dual‐input deep learning network based on vision saliency and transformer

Stereotactic arrhythmia radioablation for ventricular tachycardia: a review of clinical trials and emerging roles of imaging

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