Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Zhao, Chen; Bober, Robert M.; Tang, Haipeng; Dong, Minghao; Zhang, Chaoyang; He, Zhuo; Wang, Yuping; Deng, Hong‐Wen; Esposito, Michele; Xu, Zhihui; Zhou, Weihua

doi:10.1101/2020.05.26.20103440

Cited by 7 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, machine learning (ML) has been widely used in the eld of medical imaging, including eye imaging. We previously developed a multi-class three-dimensional (3D) fully convolutional neural network [8][9][10][11][12][13], also called the SV-Net method for automatic segmentation of total EOMs from orbital CT, which had good agreement with those from the ground truth (all R > 0.98, P < 0.0001) [14]. Song et al built an arti cial intelligence model for screening GO patients using orbital CT [15], and Chen et al built a deep learning model for detecting active and inactive phases of GO patients by orbital MRI [10].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Diagnosis of Disease Activity in Patients with Graves’ Orbitopathy Using Orbital SPECT/CT

Zhao

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Purpose Orbital 99mTc-DTPA SPECT/CT is an important new method for the assessment of inflammatory activity in patients with Graves' Orbitopathy (GO), but it consumes a heavy workload for physicians for interpretation. We aim to propose an automated method, called GO-Net, to detect the activity of GO to assist physicians for diagnosis. Materials and methods GO-Net had two stages: a semantic V-Net segmentation network (SV-Net) to extract extraocular muscles (EOMs) on orbital CT images; a three-channel convolutional neural network (CNN), including SPECT/CT images and segmentation results, to classify inflammatory activity. Manual corrections were applied when the segmentation results were not accurate. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) from Xiangya Hospital of Central South University were enrolled. For the segmentation, five-fold cross-validation with 194 eyes were used for training and internal validation. For the classification, 80% of eyes were trained and internally validated by five-fold cross-validation, and 20% of eyes were used for testing. The contours of the EOMs were drawn manually by an experienced physicians and used as the ground truth. The criteria for the diagnosis of GO activity were determined by the physician through the clinical activity score(CAS) and 99mTc-DTPA uptake. Results Our GO-Net method achieved an accuracy of 84.25%, a precision of 83.35%, a sensitivity of 84.63%, a specificity of 83.87%, an F1 score of 0.83, and an area under the receiver (AUC) of 0.89. For EOMs segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82. Contours of EOMs in 47 eyes (4.91%) were manually corrected and the average correction time was 5 mins for each eye. Conclusion Our proposed Go-Net model could accurately detect GO activity, which has great potential for the diagnosis of GO.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Diagnosis of Disease Activity in Patients with Graves’ Orbitopathy Using Orbital SPECT/CT

Zhao

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…A deep learning model 9 was used to extract the coronary arteries on fluoroscopy angiograms. The extracted artery contours were shown in Figure 1 B&F. Based on the extracted artery contours, a morphology thinning based algorithm 10,11 was used to skeletonize the extracted artery trees and an edge-linking algorithm 12 was then applied to link the separate skeleton pixel points, where adjacent skeleton pixel points were linked together to form vessel segments till encountering edge junctions or endpoints.…”

Section: Artery Extractionmentioning

confidence: 99%

3D fusion between fluoroscopy angiograms and SPECT myocardial perfusion images to guide percutaneous coronary intervention

Tang

Bober

Zhao

et al. 2022

Journal of Nuclear Cardiology

Self Cite

View full text Add to dashboard Cite

Background. Percutaneous coronary intervention (PCI) in stable coronary artery disease (CAD) is commonly triggered by abnormal myocardial perfusion imaging (MPI). However, due to the possibilities of multivessel disease, serial stenoses and variability of coronary artery perfusion distribution, opportunity exists to better align anatomic stenosis with perfusion abnormalities to improve revascularization decisions. This study aims to develop a 3-dimensional (3D) multi-modality fusion approach to assist decision-making for PCI.Methods. Coronary arteries from fluoroscopic angiography (FA) were reconstructed into 3D artery anatomy. Left ventricular (LV) epicardial surface was extracted from single-photon emission computed tomography (SPECT). The 3D fusion between artery anatomy and LV epicardial surface was completed with scaling iterative closest points (S-ICP) and vessel-surface overlay algorithms. The accuracy of the 3D fusion was evaluated via both computer simulation and real patient data. For technical validation, simulated FA and MPI were integrated and then compared with the ground truth from a digital phantom. For clinical validation, FA and SPECT images were integrated and then compared with the ground truth from computed tomography (CT) angiograms.Results. In the technical evaluation, the distance-based mismatch error between simulated fluoroscopy and phantom arteries is 1.86±1.43mm for left coronary arteries (LCA) and 2.21±2.50mm for right coronary arteries (RCA). In the clinical validation, the distance-based mismatch errors between the fluoroscopy and CT arteries were 3.84±3.15mm for LCA and 5.55±3.64mm for RCA. The presence of the corresponding fluoroscopy and CT arteries in the AHA 17-segment model agreed well with a Kappa value of 0.91(95% confidence interval (CI): 0.89-0.93) for LCA and a Kappa value of 0.80 (CI: 0.67-0.92) for RCA. Conclusions.Our fusion approach is technically accurate to assist PCI decision-making and is clinically feasible to be used in the catheterization laboratory. There is an opportunity to improve the decisionmaking and outcomes of PCI in stable CAD.

show abstract

“…These methods fall into two categories: segmentation approaches and semantic segmentation approaches. The semantic segmentation algorithms have demonstrated cutting-edge performance in coronary artery segmentation in uoroscopic images [7]. The main step to segmenting the coronary artery in this approach is to extract the vascular tree and the major vessel, then classify each individual coronary artery according to various classes by extracting underlying features of the vessel segments (arterial topology, position, and pixel features).…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of coronary arteries in computed tomography angiograph: A multi-center, multi-vendor and multi disease study

Triki,

AMMAR,

Mahmoudi

et al. 2024

Preprint

View full text Add to dashboard Cite

Coronary artery disease (CAD) is one of the primary causes of death worldwide accountings for almost 25% of all deaths worldwide each year. Accurate diagnosis and assessment of CAD and stenosis require the semantic segmentation of the coronary arteries and aorta in coronary computed tomography angiography (CCTA) images, which is of great significance. Nonetheless, producing semantic segmentations is particularly challenging due to the anatomical similarity between various surrounding areas, the branching of the arteries which includes numerous bifurcations, and the presence of smaller vessels that add to the complexity. Reducing the resolution of a 3D image to fit within the constraints of available GPU memory can result in a loss of detail, which is often undesirable. Instead, using patches of the image as input can help mitigate this issue. In this study, we propose a novel semantic segmentation method based on the 3D U-Net that uses three different datasets consisting of multi-center, multi-vendor, and multi-continent data CCTA images. We achieved accuracies of 91.2%, 93.9%, and 97.3%, respectively. The outcomes of the study demonstrate the efficacy of the method for accurately segmenting the aorta and/or the coronary arteries.

show abstract

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Cited by 7 publications

References 18 publications

Deep Learning-Based Diagnosis of Disease Activity in Patients with Graves’ Orbitopathy Using Orbital SPECT/CT

Deep Learning-Based Diagnosis of Disease Activity in Patients with Graves’ Orbitopathy Using Orbital SPECT/CT

3D fusion between fluoroscopy angiograms and SPECT myocardial perfusion images to guide percutaneous coronary intervention

Semantic segmentation of coronary arteries in computed tomography angiograph: A multi-center, multi-vendor and multi disease study

Contact Info

Product

Resources

About