A New Method for Segmentation of the Coronary Arteries of Interest and Diameter Measurement

Jiang, Lurong; Li, Yue; Pan, Jianwei; Zhu, Danhua; Tong, Jijun; Shu, Ting

doi:10.1109/access.2020.3007465

Cited by 7 publications

(3 citation statements)

References 33 publications

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“…Huang et al reported a DSC of 0.8291 [5], while Mirunalini et al achieved an intersection over union of 84.00 [6]. Additionally, Jiang et al reported a mean DSC value of 0.92 and a Jaccard value of 0.86 [7], which provides further evidence of the effectiveness of the proposed system that needs to be validated. As per the research paper, a semi-supervised approach was adopted for the experiment, as a vast collection of images was available.…”

Section: Ai-generated 3d Model For Coronary Arteriesmentioning

confidence: 93%

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3D printing of an artificial intelligence-generated patient-specific coronary artery segmentation in a support bath

Sokmen,

Cakmak,

Oksuz

2024

Biomed. Mater.

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Accurate segmentation of coronary artery tree and personalised 3D printing from medical images is essential for CAD diagnosis and treatment. The current literature on 3D printing relies solely on generic models created with different software or 3D coronary artery models manually segmented from medical images. Moreover, there are not many studies examining the bioprintability of a 3D model generated by artificial intelligence (AI) segmentation for complex and branched structures. In this study, deep learning algorithms with transfer learning have been employed for accurate segmentation of the coronary artery tree from medical images to generate printable segmentations. We propose a combination of deep learning and 3D printing, which accurately segments and prints complex vascular patterns in coronary arteries. Then, we performed the 3D printing of the AI-generated coronary artery segmentation for the fabrication of bifurcated hollow vascular structure. Our results indicate improved performance of segmentation with the aid of transfer learning with a Dice overlap score of 0.86 on a test set of 10 CTA images. Then, bifurcated regions from 3D models were printed into the Pluronic F-127 support bath using alginate+glucomannan hydrogel. We successfully fabricated the bifurcated coronary artery structures with high length and wall thickness accuracy, however, the outer diameters of the vessels and length of the bifurcation point differ from the 3D models. The extrusion of unnecessary material, primarily observed when the nozzle moves from left to the right vessel during 3D printing, can be mitigated by adjusting the nozzle speed. Moreover, the shape accuracy can also be improved by designing a multi-axis printhead that can change the printing angle in three dimensions. Thus, this study demonstrates the potential of the use of AI-segmented 3D models in the 3D printing of coronary artery structures and, when further improved, can be used for the fabrication of patient-specific vascular implants.

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Section: Ai-generated 3d Model For Coronary Arteriesmentioning

confidence: 93%

“…The method extracts the vessels of interest by interactive segmentation, thins them, and then calculates their diameter by the edge intersection method. The researchers collected angiographic images from 50 representative slices [7].…”

Section: Introductionmentioning

confidence: 99%

3D printing of an artificial intelligence-generated patient-specific coronary artery segmentation in a support bath

Sokmen,

Cakmak,

Oksuz

2024

Biomed. Mater.

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“…The threshold for noise cancellation was adaptively set, and the filter was applied to three directions: diagonal, right, and left. Furthermore, this approach was used to recover the original image from distorted and additive uncorrelated noise ( Jiang et al, 2020 ). Figure 5 represents the original image of coronary artery disease for noise removal.…”

Section: Methodsmentioning

confidence: 99%

Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images

Algarni

Al-Rezqi

Saeed

et al. 2022

PeerJ Computer Science

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Background The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. Methods In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. Results The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. Discussion The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure.

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Design of a Hybrid Bioinspired Deep Learning Model for Identification of Heart Diseases Using Clinical Parameters

Kulkarni¹,

Soni²

2023

SN COMPUT. SCI.

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A New Method for Segmentation of the Coronary Arteries of Interest and Diameter Measurement

Cited by 7 publications

References 33 publications

3D printing of an artificial intelligence-generated patient-specific coronary artery segmentation in a support bath

3D printing of an artificial intelligence-generated patient-specific coronary artery segmentation in a support bath

Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images

Design of a Hybrid Bioinspired Deep Learning Model for Identification of Heart Diseases Using Clinical Parameters

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