scholarly journals Coronary Vessel Segmentation by Coarse-to-Fine Strategy Using U-nets

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Le Nhi Lam Thuy ◽  
Tan Dat Trinh ◽  
Le Hoang Anh ◽  
Jin Young Kim ◽  
Huynh Trung Hieu ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Coronary Arteries ◽  
Vessel Segmentation ◽  
Coronary Vessel ◽  
Single Model ◽  
Low Contrast ◽  
X Ray ◽  
Small Vessels ◽  
Strategic Approach ◽  
Coarse To Fine

Each level of the coronary artery has different sizes and properties. The primary coronary arteries usually have high contrast to the background, while the secondary coronary arteries have low contrast to the background and thin structures. Furthermore, several small vessels are disconnected or broken up vascular segments. It is a challenging task to use a single model to segment all coronary artery sizes. To overcome this problem, we propose a novel segmenting method for coronary artery extraction from angiograms based on the primary and secondary coronary artery. Our method is a coarse-to-fine strategic approach for extracting coronary arteries in many different sizes. We construct the first U-net model to segment the main coronary artery extraction and build a new algorithm to determine the junctions of the main coronary artery with the secondary coronary artery. Using these junctions, we determine regions of the secondary coronary arteries (rectangular regions) for a secondary coronary artery-extracted segment with the second U-net model. The experiment result is 76.40% in terms of Dice coefficient on coronary X-ray datasets. The proposed approach presents its potential in coronary vessel segmentation.

scholarly journals Retinal Vessel Segmentation by Deep Residual Learning with Wide Activation

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuliang Ma ◽  
Xue Li ◽  
Xiaopeng Duan ◽  
Yun Peng ◽  
Yingchun Zhang
Keyword(s):  
Blood Vessel ◽  
Blood Vessels ◽  
Area Under The Curve ◽  
Retinal Vessel ◽  
Vessel Segmentation ◽  
Superior Performance ◽  
Retinal Blood Vessel ◽  
Low Contrast ◽  
Blood Vessel Segmentation ◽  
Small Vessels

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks.

scholarly journals A Case Report on Single Vessel Disease

2021 ◽  
pp. 341-345
Author(s):  
Monali Walke ◽  
Ranjana Sharma ◽  
Samruddhi Gujar
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Coronary Arteries ◽  
Percutaneous Transluminal Coronary Angioplasty ◽  
Developed Country ◽  
Patient Response ◽  
Small Vessels ◽  
Risk Of Death ◽  
Artery Disease ◽  
Transluminal Coronary Angioplasty

Introduction: Coronary artery disease (CAD) involves the decrease flow of blood to  muscle of the heart because of the to build-up of plaque (atherosclerosis) in  arteries of the heart. It is one of the most common cardiovascular disease. Tiny branches off larger coronary arteries are get damaged and not able to dilate proper.  Small vessels need to expand and also providing oxygen-rich blood to heart. Coronary arteries are damaged, blood flow to the heart decreases. Background: Coronary artery disease affected 110 million people and resulted in 8.9 million deaths. It makes up 15.6% of all deaths, making it the most common cause of death globally.  In the developed country risk of death from CAD decreased between 1980 and 2010. Case Presentation: A case of 60 year old men admitted in the cardiac unit with complaints about the abdominal pain and Nausea, vomiting, weakness from the 2 days after examining and blood pressure is 110/70 mm of Hg. He had these complaints since 2 days. Interventions: The patient was treatment started with orally Tab. Ecosprin 150mg, Tab.Ultracet, Tab. Pantop 40 mg, Tab. Augmentin 625 mg. Patient is also undergone PTCA(Percutaneous Transluminal Coronary Angioplasty). Treatment consisted of medication, therapeutic exercise, and health education. Conclusion: In this study, we mainly focus on medical and surgical management and outstanding nursing care helped prevent further complication. Over all the patient response was good and improvement occur after PTCA.

Role of vasa vasorum in transendothelial solute transport in the coronary vessel wall: a study with cryostatic micro-CT

2004 ◽  
Vol 287 (5) ◽  
pp. H2346-H2351 ◽  
Author(s):  
Mario Gössl ◽  
Patricia E. Beighley ◽  
Nasser M. Malyar ◽  
Erik L. Ritman
Keyword(s):  
Coronary Artery ◽  
Surface Area ◽  
Coronary Arteries ◽  
Vessel Wall ◽  
Coronary Vessel ◽  
Vasa Vasorum ◽  
Micro Ct ◽  
Artery Wall ◽  
Coronary Vessel Wall

Using cryostatic microscopic computed tomography (micro-CT), we sought to determine the role of coronary vasa vasorum (VV) in transendothelial solute transport in arteries with normal and increased permeability due to high plasma cholesterol levels. In 6-mo-old pigs on a normal ( n = 23) and 2% high cholesterol (HC) diet ( n = 8), 2-cm segments of the proximal left anterior descending coronary arteries were removed in vivo after a selective injection of X-ray contrast solution. Harvesting of the specimens occurred at 0, 15, 25, 35, or 45 s after completion of the contrast injection. Specimens were snap frozen and scanned in our cryostatic micro-CT. The spatial distribution of contrast in the coronary artery wall was quantified using the CT images. Right coronary arteries were infused with Microfil to determine VV density (VV/mm2) and the cumulative lumen surface area (mm2/mm3). Transendothelial diffusion of contrast into the coronary vessel wall is a dynamic process starting at both the subintima and the adventitia. The subintimal opacification moves as a wave toward the adventitia, whereas the adventitial wave resolves. The coronary vessel wall in animals on a HC diet shows higher opacification than in normal coronary arteries without an increase of VV total luminal surface area. The loss of endothelial integrity in hypercholesterolemia significantly alters VV solute washin to, and washout from, the coronary artery wall.

Vessel segmentation in low contrast X-ray angiogram images

2016 ◽  
Author(s):  
B. Felfelian ◽  
H. R. Fazlali ◽  
N. Karimi ◽  
S. M. R. Soroushmehr ◽  
S. Samavi ◽  
...  
Keyword(s):  
Vessel Segmentation ◽  
Low Contrast ◽  
X Ray

scholarly journals Deep learning segmentation of major vessels in X-ray coronary angiography

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Su Yang ◽  
Jihoon Kweon ◽  
Jae-Hyung Roh ◽  
Jae-Hwan Lee ◽  
Heejun Kang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Angiography ◽  
Coronary Arteries ◽  
Coronary Vessels ◽  
Quantitative Coronary Angiography ◽  
Vessel Segmentation ◽  
Diagnostic Methods ◽  
X Ray ◽  
Fully Convolutional Networks ◽  
Major Vessel

AbstractX-ray coronary angiography is a primary imaging technique for diagnosing coronary diseases. Although quantitative coronary angiography (QCA) provides morphological information of coronary arteries with objective quantitative measures, considerable training is required to identify the target vessels and understand the tree structure of coronary arteries. Despite the use of computer-aided tools, such as the edge-detection method, manual correction is necessary for accurate segmentation of coronary vessels. In the present study, we proposed a robust method for major vessel segmentation using deep learning models with fully convolutional networks. When angiographic images of 3302 diseased major vessels from 2042 patients were tested, deep learning networks accurately identified and segmented the major vessels in X-ray coronary angiography. The average F1 score reached 0.917, and 93.7% of the images exhibited a high F1 score > 0.8. The most narrowed region at the stenosis was distinctly captured with high connectivity. Robust predictability was validated for the external dataset with different image characteristics. For major vessel segmentation, our approach demonstrated that prediction could be completed in real time with minimal image preprocessing. By applying deep learning segmentation, QCA analysis could be further automated, thereby facilitating the use of QCA-based diagnostic methods.

scholarly journals Main Coronary Vessel Segmentation Using Deep Learning in Smart Medical

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhanchao Xian ◽  
Xiaoqing Wang ◽  
Shaodi Yan ◽  
Dahao Yang ◽  
Junyu Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Automatic Segmentation ◽  
Disease Diagnosis ◽  
Vessel Segmentation ◽  
X Ray ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Main Vessel ◽  
Artery Disease

The automatic segmentation of main vessels on X-ray angiography (XRA) images is of great importance in the smart coronary artery disease diagnosis system. However, existing methods have been developed to this task, but these methods have difficulty in recognizing the coronary artery structure in XRA images. Main vessel segmentation is still a challenging task due to the diversity and small-size region of the vessel in the XRA images. In this study, we propose a robust method for main vessel segmentation by using deep learning architectures with fully convolutional networks. Four deep learning models based on the UNet architecture are evaluated on a clinical dataset, which consists of 3200 X-ray angiography images collected from 1118 patients. Using the precision (Pre), recall (Re), and F1 score (F1) as evaluation metrics, the average Pre, Re, and F1 for main vessel segmentation in the entire experimental dataset is 0.901, 0.898, and 0.900, respectively. 89.8% of the images exhibited a high F1 score >0.8. For the main vessel segmentation in XRA images, our deep learning methods demonstrated that vessels could be segmented in real time with a more optimized implementation, to further facilitate the online diagnosis in smart medical.

Quantification of Coronary Artery Stenosis with 16-Slice MSCT in Patients before CABG Surgery: Comparison to Standard Invasive Coronary Angiography

2005 ◽  
Vol 8 (1) ◽  
pp. 42 ◽  
Author(s):  
C. Probst ◽  
A. Kovacs ◽  
C. Schmitz ◽  
W. Schiller ◽  
H. Schild ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Coronary Angiography ◽  
Coronary Arteries ◽  
Artery Bypass ◽  
Invasive Coronary Angiography ◽  
Bypass Graft ◽  
Heart Association ◽  
Artery Stenosis ◽  
Vessel Disease ◽  
Selective Coronary Angiography

Objective: Invasive, selective coronary angiography is the gold standard for evaluation of coronary artery disease (CAD) and degree of stenosis. The purpose of this study was to compare 3-dimensional (3D) reconstructed 16-slice multislice computed tomographic (MSCT) angiography and selective coronary angiography in patients before elective coronary artery bypass graft (CABG) procedure. Methods: Sixteen-slice MSCT scans (Philips Mx8000 IDT) were performed in 50 patients (42 male/8 female; mean age, 64.44 8.66 years) scheduled for elective CABG procedure. Scans were retrospectively electrocardiogram-gated 3D reconstructed. The images of the coronary arteries were evaluated for stenosis by 2 independent radiologists. The results were compared with the coronary angiography findings using the American Heart Association segmental classification for coronary arteries. Results: Four patients (8%) were excluded for technical reasons. Thirty-eight patients (82.6%) had 3-vessel disease, 4 (8.7 %) had 2-vessel disease, and 4 (8.7%) had an isolated left anterior descending artery stenosis. In the proximal segments all stenoses >50% (56/56) were detected by MSCT; medial segment sensitivity was 97% (73/75), specificity 90.3%; distal segment sensitivity was 90.7% (59/65), specificity 77%. Conclusion: Accurate quantification of coronary stenosis greater than 50% in the proximal and medial segments is possible with high sensitivity and specificity using the new generation of 16-slice MSCTs. There is still a tendency to overestimate stenosis in the distal segments. MSCT seems to be an excellent diagnostic tool for screening patients with possible CAD.

Congenital Left Main Coronary Artery to Coronary Sinus Fistula

2011 ◽  
Vol 14 (4) ◽  
pp. 255 ◽  
Author(s):  
Fotios A. Mitropoulos ◽  
Meletios A. Kanakis ◽  
Periklis A. Davlouros ◽  
George Triantis
Keyword(s):  
Coronary Artery ◽  
Coronary Arteries ◽  
Coronary Sinus ◽  
Left Main Coronary Artery ◽  
Coronary Artery Fistula ◽  
Left Main ◽  
Coronary Fistula ◽  
Excellent Outcome ◽  
Surgical Ligation ◽  
Congenital Coronary Artery Fistula

Congenital coronary artery fistula is an extremely rare anomaly that may involve any of the coronary arteries and any of the cardiac chambers. We report the case of a 14-year-old female patient with a symptomatic congenital coronary fistula starting from the left main coronary artery and draining to the coronary sinus. The patient underwent surgical ligation of the fistula and had an excellent outcome.

Coronary artery embolism in a patient with mechanical heart valve and normal coronary arteries (clinical case)

2019 ◽  
Vol 17 (2) ◽  
pp. 153-157
Author(s):  
A. Z. Sharafeev ◽  
◽  
A. F. Khalirakhmanov ◽  
A. I. Alkhazurov ◽  
E. A. Gaziev ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Heart Valve ◽  
Coronary Arteries ◽  
Clinical Case ◽  
Mechanical Heart Valve ◽  
Normal Coronary Arteries ◽  
Artery Embolism ◽  
Coronary Artery Embolism
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