Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries
X-ray angiography is commonly used in the diagnosis and treatment of coronary artery disease with the advantage of visualization of the inside of blood vessels in real-time. However, it has several disadvantages that occur in the acquisition process, which causes inconvenience and difficulty. Here,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/4/778 |
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| author | Taeyong Park Seungwoo Khang Heeryeol Jeong Kyoyeong Koo Jeongjin Lee Juneseuk Shin Ho Chul Kang |
| author_facet | Taeyong Park Seungwoo Khang Heeryeol Jeong Kyoyeong Koo Jeongjin Lee Juneseuk Shin Ho Chul Kang |
| author_sort | Taeyong Park |
| collection | DOAJ |
| description | X-ray angiography is commonly used in the diagnosis and treatment of coronary artery disease with the advantage of visualization of the inside of blood vessels in real-time. However, it has several disadvantages that occur in the acquisition process, which causes inconvenience and difficulty. Here, we propose a novel segmentation and nonrigid registration method to provide useful real-time assistive images and information. A convolutional neural network is used for the segmentation of coronary arteries in 2D X-ray angiography acquired from various angles in real-time. To compensate for errors that occur during the 2D X-ray angiography acquisition process, 3D CT angiography is used to analyze the topological structure. A novel energy function-based 3D deformation and optimization is utilized to implement real-time registration. We evaluated the proposed method for 50 series from 38 patients by comparing the ground truth. The proposed segmentation method showed that Precision, Recall, and F1 score were 0.7563, 0.6922, and 0.7176 for all vessels, 0.8542, 0.6003, and 0.7035 for markers, and 0.8897, 0.6389, and 0.7386 for bifurcation points, respectively. In the nonrigid registration method, the average distance of 0.8705, 1.06, and 1. 5706 mm for all vessels, markers, and bifurcation points was achieved. The overall process execution time was 0.179 s. |
| first_indexed | 2024-03-09T10:58:41Z |
| format | Article |
| id | doaj.art-e8f5fecd43594f87affe57c38222b080 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T10:58:41Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-e8f5fecd43594f87affe57c38222b0802023-12-01T01:29:04ZengMDPI AGDiagnostics2075-44182022-03-0112477810.3390/diagnostics12040778Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary ArteriesTaeyong Park0Seungwoo Khang1Heeryeol Jeong2Kyoyeong Koo3Jeongjin Lee4Juneseuk Shin5Ho Chul Kang6Department of Biomedical Informatics, Hallym University Medical Center, 22 Gwanpyeong-ro, 170 beon-gil, Dongan-gu, Anyang-si 14068, Gyeonggi-do, KoreaSchool of Computer Science and Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-Gu, Seoul 06978, Gyeonggi-do, KoreaSchool of Computer Science and Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-Gu, Seoul 06978, Gyeonggi-do, KoreaSchool of Computer Science and Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-Gu, Seoul 06978, Gyeonggi-do, KoreaSchool of Computer Science and Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-Gu, Seoul 06978, Gyeonggi-do, KoreaDepartment of Systems Management Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si 16419, Gyeonggi-do, KoreaDepartment of Media Technology & Media Contents, The Catholic University of Korea, 43 Jibong-ro, Bucheon-si 14662, Gyeonggi-do, KoreaX-ray angiography is commonly used in the diagnosis and treatment of coronary artery disease with the advantage of visualization of the inside of blood vessels in real-time. However, it has several disadvantages that occur in the acquisition process, which causes inconvenience and difficulty. Here, we propose a novel segmentation and nonrigid registration method to provide useful real-time assistive images and information. A convolutional neural network is used for the segmentation of coronary arteries in 2D X-ray angiography acquired from various angles in real-time. To compensate for errors that occur during the 2D X-ray angiography acquisition process, 3D CT angiography is used to analyze the topological structure. A novel energy function-based 3D deformation and optimization is utilized to implement real-time registration. We evaluated the proposed method for 50 series from 38 patients by comparing the ground truth. The proposed segmentation method showed that Precision, Recall, and F1 score were 0.7563, 0.6922, and 0.7176 for all vessels, 0.8542, 0.6003, and 0.7035 for markers, and 0.8897, 0.6389, and 0.7386 for bifurcation points, respectively. In the nonrigid registration method, the average distance of 0.8705, 1.06, and 1. 5706 mm for all vessels, markers, and bifurcation points was achieved. The overall process execution time was 0.179 s.https://www.mdpi.com/2075-4418/12/4/778percutaneous coronary interventionimage segmentationconvolutional neural networknonrigid registrationmultimodality registration |
| spellingShingle | Taeyong Park Seungwoo Khang Heeryeol Jeong Kyoyeong Koo Jeongjin Lee Juneseuk Shin Ho Chul Kang Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries Diagnostics percutaneous coronary intervention image segmentation convolutional neural network nonrigid registration multimodality registration |
| title | Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries |
| title_full | Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries |
| title_fullStr | Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries |
| title_full_unstemmed | Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries |
| title_short | Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries |
| title_sort | deep learning segmentation in 2d x ray images and non rigid registration in multi modality images of coronary arteries |
| topic | percutaneous coronary intervention image segmentation convolutional neural network nonrigid registration multimodality registration |
| url | https://www.mdpi.com/2075-4418/12/4/778 |
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