1
|
Liu P, Huang G, Jing J, Bian S, Cheng L, Lu XY, Rao C, Liu Y, Hua Y, Wang Y, He K. An Energy Matching Vessel Segmentation Framework in 3-D Medical Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1476-1488. [PMID: 38048240 DOI: 10.1109/tmi.2023.3339204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
Accurate vascular segmentation from High Resolution 3-Dimensional (HR3D) medical scans is crucial for clinicians to visualize complex vasculature and diagnose related vascular diseases. However, a reliable and scalable vessel segmentation framework for HR3D scans remains a challenge. In this work, we propose a High-resolution Energy-matching Segmentation (HrEmS) framework that utilizes deep learning to directly process the entire HR3D scan and segment the vasculature to the finest level. The HrEmS framework introduces two novel components. Firstly, it uses the real-order total variation operator to construct a new loss function that guides the segmentation network to obtain the correct topology structure by matching the energy of the predicted segment to the energy of the manual label. This is different from traditional loss functions such as dice loss, which matches the pixels between predicted segment and manual label. Secondly, a curvature-based weight-correction module is developed, which directs the network to focus on crucial and complex structural parts of the vasculature instead of the easy parts. The proposed HrEmS framework was tested on three in-house multi-center datasets and three public datasets, and demonstrated improved results in comparison with the state-of-the-art methods using both topology-relevant and volumetric-relevant metrics. Furthermore, a double-blind assessment by three experienced radiologists on the critical points of the clinical diagnostic processes provided additional evidence of the superiority of the HrEmS framework.
Collapse
|
2
|
Wang X, Wang J, Wang W, Zhu M, Guo H, Ding J, Sun J, Zhu D, Duan Y, Chen X, Zhang P, Wu Z, He K. Using artificial intelligence in the development of diagnostic models of coronary artery disease with imaging markers: A scoping review. Front Cardiovasc Med 2022; 9:945451. [PMID: 36267636 PMCID: PMC9577031 DOI: 10.3389/fcvm.2022.945451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Background Coronary artery disease (CAD) is a progressive disease of the blood vessels supplying the heart, which leads to coronary artery stenosis or obstruction and is life-threatening. Early diagnosis of CAD is essential for timely intervention. Imaging tests are widely used in diagnosing CAD, and artificial intelligence (AI) technology is used to shed light on the development of new imaging diagnostic markers. Objective We aim to investigate and summarize how AI algorithms are used in the development of diagnostic models of CAD with imaging markers. Methods This scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guideline. Eligible articles were searched in PubMed and Embase. Based on the predefined included criteria, articles on coronary heart disease were selected for this scoping review. Data extraction was independently conducted by two reviewers, and a narrative synthesis approach was used in the analysis. Results A total of 46 articles were included in the scoping review. The most common types of imaging methods complemented by AI included single-photon emission computed tomography (15/46, 32.6%) and coronary computed tomography angiography (15/46, 32.6%). Deep learning (DL) (41/46, 89.2%) algorithms were used more often than machine learning algorithms (5/46, 10.8%). The models yielded good model performance in terms of accuracy, sensitivity, specificity, and AUC. However, most of the primary studies used a relatively small sample (n < 500) in model development, and only few studies (4/46, 8.7%) carried out external validation of the AI model. Conclusion As non-invasive diagnostic methods, imaging markers integrated with AI have exhibited considerable potential in the diagnosis of CAD. External validation of model performance and evaluation of clinical use aid in the confirmation of the added value of markers in practice. Systematic review registration [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022306638], identifier [CRD42022306638].
Collapse
Affiliation(s)
- Xiao Wang
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China,Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China,Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Junfeng Wang
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
| | - Wenjun Wang
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China,Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China,Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Mingxiang Zhu
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China,Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China,Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Hua Guo
- Department of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Junyu Ding
- Department of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, China
| | - Jin Sun
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China,Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China,Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Di Zhu
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China,Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China,Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Yongjie Duan
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China,Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China,Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Xu Chen
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China,Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China,Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China
| | - Peifang Zhang
- BioMind Technology, Zhongguancun Medical Engineering Center, Beijing, China
| | - Zhenzhou Wu
- BioMind Technology, Zhongguancun Medical Engineering Center, Beijing, China
| | - Kunlun He
- Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China,Beijing Key Laboratory for Precision Medicine of Chronic Heart Failure, Chinese PLA General Hospital, Beijing, China,Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China,*Correspondence: Kunlun He, ; orcid.org/0000-0002-3335-5700
| |
Collapse
|
3
|
Infante T, Cavaliere C, Punzo B, Grimaldi V, Salvatore M, Napoli C. Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review. Circ Cardiovasc Imaging 2021; 14:1133-1146. [PMID: 34915726 DOI: 10.1161/circimaging.121.013025] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.
Collapse
Affiliation(s)
- Teresa Infante
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.)
| | | | - Bruna Punzo
- IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
| | | | | | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania "Luigi Vanvitelli", Naples, Italy (T.I., C.N.).,IRCCS SDN, Naples, Italy (C.C., B.P., V.G., M.S., C.N.)
| |
Collapse
|
4
|
Sitek A, Ahn S, Asma E, Chandler A, Ihsani A, Prevrhal S, Rahmim A, Saboury B, Thielemans K. Artificial Intelligence in PET: An Industry Perspective. PET Clin 2021; 16:483-492. [PMID: 34353746 DOI: 10.1016/j.cpet.2021.06.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing, and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This article provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom-designed data-processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients.
Collapse
Affiliation(s)
- Arkadiusz Sitek
- Sano Centre for Computational Medicine, Nawojki 11 Street, Kraków 30-072, Poland.
| | - Sangtae Ahn
- GE Research, 1 Research Circle KWC-1310C, Niskayuna, NY 12309, USA
| | - Evren Asma
- Canon Medical Research, 706 N Deerpath Drive, Vernon Hills, IL 60061, USA
| | - Adam Chandler
- Global Scientific Collaborations Group, United Imaging Healthcare, America, 9230 Kirby Drive, Houston, TX 77054, USA
| | - Alvin Ihsani
- NVIDIA, 2 Technology Park Drive, Westford, MA 01886, USA
| | - Sven Prevrhal
- Philips Research Europe, Röntgenstr. 22, Hamburg 22335, Germany
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada; Department of Physics, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Kris Thielemans
- Institute of Nuclear Medicine, University College London, UCL Hospital Tower 5, 235 Euston Road, London NW1 2BU, UK; Algorithms and Software Consulting Ltd, 10 Laneway, London SW15 5HX, UK
| |
Collapse
|