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Wu Y, Xia S, Liang Z, Chen R, Qi S. Artificial intelligence in COPD CT images: identification, staging, and quantitation. Respir Res 2024; 25:319. [PMID: 39174978 PMCID: PMC11340084 DOI: 10.1186/s12931-024-02913-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 07/09/2024] [Indexed: 08/24/2024] Open
Abstract
Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.
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Affiliation(s)
- Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Shuyue Xia
- Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China
- Key Laboratory of Medicine and Engineering for Chronic Obstructive Pulmonary Disease in Liaoning Province, Shenyang, China
| | - Zhenyu Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Rongchang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Shenzhen Institute of Respiratory Disease, Shenzhen People's Hospital, Shenzhen, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
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Zhang K, Xu P, Wang M, Lin P, Crookes D, He B, Hua L. PE-Net: a parallel framework for 3D inferior mesenteric artery segmentation. Front Physiol 2023; 14:1308987. [PMID: 38169744 PMCID: PMC10758612 DOI: 10.3389/fphys.2023.1308987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 11/24/2023] [Indexed: 01/05/2024] Open
Abstract
The structural morphology of mesenteric artery vessels is of significant importance for the diagnosis and treatment of colorectal cancer. However, developing automated vessel segmentation methods for this purpose remains challenging. Existing convolution-based segmentation methods have limitations in capturing long-range dependencies, while transformer-based models require large datasets, making them less suitable for tasks with limited training samples. Moreover, over-segmentation, mis-segmentation, and vessel discontinuity are common challenges in vessel segmentation tasks. To address these issues, we propose a parallel encoding architecture that combines transformers and convolutions to retain the advantages of both approaches. The model effectively learns position deviations and enhances robustness for small-scale datasets. Additionally, we introduce a vessel edge capture module to improve vessel continuity and topology. Extensive experimental results demonstrate the improved performance of our model, with Dice Similarity Coefficient and Average Hausdorff Distance scores of 81.64% and 7.7428, respectively.
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Affiliation(s)
- Kun Zhang
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, China
- Nantong Key Laboratory of Intelligent Control and Intelligent Computing, Nantong Institute of Technology, Nantong, Jiangsu, China
- Nantong Key Laboratory of Intelligent Medicine Innovation and Transformation, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
| | - Peixia Xu
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, China
| | - Meirong Wang
- Department of Radiology, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
| | - Pengcheng Lin
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, China
| | - Danny Crookes
- School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Belfast, United Kingdom
| | - Bosheng He
- Nantong Key Laboratory of Intelligent Medicine Innovation and Transformation, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
- Department of Radiology, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
- Clinical Medicine Research Center, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China
| | - Liang Hua
- School of Electrical Engineering, Nantong University, Nantong, Jiangsu, China
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Wu Y, Qi S, Wang M, Zhao S, Pang H, Xu J, Bai L, Ren H. Transformer-based 3D U-Net for pulmonary vessel segmentation and artery-vein separation from CT images. Med Biol Eng Comput 2023; 61:2649-2663. [PMID: 37420036 DOI: 10.1007/s11517-023-02872-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 06/20/2023] [Indexed: 07/09/2023]
Abstract
Transformer-based methods have led to the revolutionizing of multiple computer vision tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced attention module to explore contextual and spatial information in non-contrast (NC) and contrast-enhanced (CE) computed tomography (CT) images for pulmonary vessel segmentation and artery-vein separation. Our proposed network employs a 3D contextual transformer module in the encoder and decoder part and a double attention module in skip connection to effectively finish high-quality vessel and artery-vein segmentation. Extensive experiments are conducted on the in-house dataset and the ISICDM2021 challenge dataset. The in-house dataset includes 56 NC CT scans with vessel annotations and the challenge dataset consists of 14 NC and 14 CE CT scans with vessel and artery-vein annotations. For vessel segmentation, Dice is 0.840 for CE CT and 0.867 for NC CT. For artery-vein separation, the proposed method achieves a Dice of 0.758 of CE images and 0.602 of NC images. Quantitative and qualitative results demonstrated that the proposed method achieved high accuracy for pulmonary vessel segmentation and artery-vein separation. It provides useful support for further research associated with the vascular system in CT images. The code is available at https://github.com/wuyanan513/Pulmonary-Vessel-Segmentation-and-Artery-vein-Separation .
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Affiliation(s)
- Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
- Department of Electronic Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Meihuan Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shuiqing Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Haowen Pang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Jiaxuan Xu
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, The National Center for Respiratory Medicine, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Long Bai
- Department of Electronic Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Hongliang Ren
- Department of Electronic Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Biomedical Engineering (BME), National University of Singapore, Singapore, Singapore.
- Research Institute, National University of Suzhou, Suzhou, Jiangsu, China.
- Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong (CUHK), Hong Kong, China.
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