A coronary artery CTA segmentation approach based on deep learning

2021 ◽  
pp. 1-14
Author(s):  
Caiyun Huang ◽  
Changhua Yin
Keyword(s):  
Deep Learning ◽  
Coronary Artery ◽  
Blood Vessel ◽  
Vessel Segmentation ◽  
Majority Voting ◽  
Dice Similarity Coefficient ◽  
Fusion Method ◽  
Level Set Function ◽  
First Choice ◽  
Medical Image Diagnosis

Presence of plaque and coronary artery stenosis are the main causes of coronary heart disease. Detection of plaque and coronary artery segmentation have become the first choice in detecting coronary artery disease. The purpose of this study is to investigate a new method for plaque detection and automatic segmentation and diagnosis of coronary arteries and to test its feasibility of applying to clinical medical image diagnosis. A multi-model fusion coronary CT angiography (CTA) vessel segmentation method is proposed based on deep learning. The method includes three network layer models namely, an original 3-dimensional full convolutional network (3D FCN) and two networks that embed the attention gating (AG) model in the original 3D FCN. Then, the prediction results of the three networks are merged by using the majority voting algorithm and thus the final prediction result of the networks is obtained. In the post-processing stage, the level set function is used to further iteratively optimize the results of network fusion prediction. The JI (Jaccard index) and DSC (Dice similarity coefficient) scores are calculated to evaluate accuracy of blood vessel segmentations. Applying to a CTA dataset of 20 patients, accuracy of coronary blood vessel segmentation using FCN, FCN-AG1, FCN-AG2 network and the fusion method are tested. The average values of JI and DSC of using the first three networks are (0.7962, 0.8843), (0.8154, 0.8966) and (0.8119, 0.8936), respectively. When using new fusion method, average JI and DSC of segmentation results increase to (0.8214, 0.9005), which are better than the best result of using FCN, FCN-AG1 and FCN-AG2 model independently.

scholarly journals Semi-Supervised Learning Method of U-Net Deep Learning Network for Blood Vessel Segmentation in Retinal Images

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1067
Author(s):  
Dali Chen ◽  
Yingying Ao ◽  
Shixin Liu
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Supervised Learning ◽  
Vessel Segmentation ◽  
Training Dataset ◽  
Retinal Images ◽  
Learning Method ◽  
Learning Network ◽  
Blood Vessel Segmentation ◽  
Deep Learning Network

Blood vessel segmentation methods based on deep neural networks have achieved satisfactory results. However, these methods are usually supervised learning methods, which require large numbers of retinal images with high quality pixel-level ground-truth labels. In practice, the task of labeling these retinal images is very costly, financially and in human effort. To deal with these problems, we propose a semi-supervised learning method which can be used in blood vessel segmentation with limited labeled data. In this method, we use the improved U-Net deep learning network to segment the blood vessel tree. On this basis, we implement the U-Net network-based training dataset updating strategy. A large number of experiments are presented to analyze the segmentation performance of the proposed semi-supervised learning method. The experiment results demonstrate that the proposed methodology is able to avoid the problems of insufficient hand-labels, and achieve satisfactory performance.

scholarly journals Deep Learning Based Glaucoma Detection with Cropped Optic Cup and Disc and Blood Vessel Segmentation

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mir Tanvir Islam ◽  
Shafin T. Mashfu ◽  
Abrar Faisal ◽  
Sadman Chowdhury Siam ◽  
Intisar Tahmid Naheen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Vessel Segmentation ◽  
Blood Vessel Segmentation ◽  
Glaucoma Detection ◽  
Optic Cup

scholarly journals Retinal blood vessel segmentation using a deep learning method based on modified U-NET model

2021 ◽  
Author(s):  
Sanjeewani NA ◽  
arun kumar yadav ◽  
Mohd Akbar ◽  
mohit kumar ◽  
Divakar Yadav
Keyword(s):  
Deep Learning ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Automatic Segmentation ◽  
Vessel Segmentation ◽  
Great Success ◽  
Retinal Blood Vessel ◽  
Data Set ◽  
Blood Vessel Segmentation ◽  
Retinal Blood Vessels

<div>Automatic retinal blood vessel segmentation is very crucial to ophthalmology. It plays a vital role in the early detection of several retinal diseases such as Diabetic Retinopathy, hypertension, etc. In recent times, deep learning based methods have attained great success in automatic segmentation of retinal blood vessels from images. In this paper, a U-NET based architecture is proposed to segment the retinal blood vessels from fundus images of the eye. Furthermore, 3 pre-processing algorithms are also proposed to enhance the performance of the system. The proposed architecture has provided significant results. On the basis of experimental evaluation on the publicly available DRIVE data set, it has been observed that the average accuracy (Acc) is .9577, sensitivity (Se) is .7436, specificity (Sp) is .9838 and F1-score is .7931. The proposed system outperforms all recent state of art approaches mentioned in the literature.</div>

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