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Cueto R, Zuñiga C, Afanador E. Non-traumatic (spontaneous) superficial femoral artery dissection: A case report. Radiol Case Rep 2024; 19:3922-3927. [PMID: 39040824 PMCID: PMC11261272 DOI: 10.1016/j.radcr.2024.05.089] [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: 03/06/2024] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 07/24/2024] Open
Abstract
Non-traumatic or spontaneous dissection of the superficial femoral artery is an extremely rare entity, being more common in the external iliac artery in relation to intensive physical activity, pregnancy, among others. It has a variable clinical presentation. The diagnosis is made through angio-tomography (Angio-CT), angio-resonance (Angio-MR) and/or arteriography, the last one being diagnostic and therapeutic. The case of a 62-year-old female patient with a history of high blood pressure who consulted due to intense pain in the left lower limb is discussed. The diagnosis of dissection was made through arteriography and she underwent endovascular repair, showing favorable results.
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Affiliation(s)
- Rafael Cueto
- Universidad del Norte – Hospital Universitario del Norte, Clínica General del Norte, Barranquilla, Colombia
| | - Camilo Zuñiga
- Universidad del Norte – Hospital Universitario del Norte, Clínica General del Norte, Barranquilla, Colombia
| | - Ernesto Afanador
- Clínica General del Norte, Universidad del Norte, Diagnóstico Maipú – Sociedad Argentina de Radiología, Hospital Puerta de Hierro de Majadahonda and CDITE (Centro de Diagnóstico y Terapia Endoluminal), Santa Cruz de Tenerife (Canarias), Barranquilla, Colombia
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2
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Zhong W, Zhang H, Gao Z, Hau WK, Yang G, Liu X, Xu L. Distraction-aware hierarchical learning for vascular structure segmentation in intravascular ultrasound images. Comput Med Imaging Graph 2024; 115:102381. [PMID: 38640620 DOI: 10.1016/j.compmedimag.2024.102381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/19/2024] [Accepted: 04/09/2024] [Indexed: 04/21/2024]
Abstract
Vascular structure segmentation in intravascular ultrasound (IVUS) images plays an important role in pre-procedural evaluation of percutaneous coronary intervention (PCI). However, vascular structure segmentation in IVUS images has the challenge of structure-dependent distractions. Structure-dependent distractions are categorized into two cases, structural intrinsic distractions and inter-structural distractions. Traditional machine learning methods often rely solely on low-level features, overlooking high-level features. This way limits the generalization of these methods. The existing semantic segmentation methods integrate low-level and high-level features to enhance generalization performance. But these methods also introduce additional interference, which is harmful to solving structural intrinsic distractions. Distraction cue methods attempt to address structural intrinsic distractions by removing interference from the features through a unique decoder. However, they tend to overlook the problem of inter-structural distractions. In this paper, we propose distraction-aware hierarchical learning (DHL) for vascular structure segmentation in IVUS images. Inspired by distraction cue methods for removing interference in a decoder, the DHL is designed as a hierarchical decoder that gradually removes structure-dependent distractions. The DHL includes global perception process, distraction perception process and structural perception process. The global perception process and distraction perception process remove structural intrinsic distractions then the structural perception process removes inter-structural distractions. In the global perception process, the DHL searches for the coarse structural region of the vascular structures on the slice of IVUS sequence. In the distraction perception process, the DHL progressively refines the coarse structural region of the vascular structures to remove structural distractions. In the structural perception process, the DHL detects regions of inter-structural distractions in fused structure features then separates them. Extensive experiments on 361 subjects show that the DHL is effective (e.g., the average Dice is greater than 0.95), and superior to ten state-of-the-art IVUS vascular structure segmentation methods.
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Affiliation(s)
- Wenhao Zhong
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518055, Guangdong, China
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518055, Guangdong, China
| | - Zhifan Gao
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518055, Guangdong, China
| | - William Kongto Hau
- Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, W12 7SL London, UK; Cardiovascular Research Centre, Royal Brompton Hospital, SW3 6NP London, UK; National Heart and Lung Institute, Imperial College London, SW7 2AZ London, UK; School of Biomedical Engineering and Imaging Sciences, King's College London, WC2R 2LS London, UK
| | - Xiujian Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, 518055, Guangdong, China.
| | - Lin Xu
- Department of Geriatric Cardiology, PLA General Hospital of the Southern Theatre Command, Guangzhou, China.
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Liu X, Feng T, Liu W, Song L, Yuan Y, Hau WK, Ser JD, Gao Z. Scale Mutualized Perception for Vessel Border Detection in Intravascular Ultrasound Images. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1060-1071. [PMID: 36441897 DOI: 10.1109/tcbb.2022.3224934] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent interference. Early methods usually rely on the hand-crafted features, thus not robust to this interference. The existing deep learning methods are also ineffective to solve this challenge, because these methods aggregate multi-scale features in the top-down way. This aggregation may bring in interference from the non-adjacent scale. Besides, they only combine the features in all scales, and thus may weaken their complementary information. We propose the scale mutualized perception to solve this challenge by considering the adjacent scales mutually to preserve their complementary information. First, the adjacent small scales contain certain semantics to locate different vessel tissues. Then, they can also perceive the global context to assist the representation of the local context in the adjacent large scale, and vice versa. It helps to distinguish the objects with similar local features. Second, the adjacent large scales provide detailed information to refine the vessel boundaries. The experiments show the effectiveness of our method in 153 IVUS sequences, and its superiority to ten state-of-the-art methods.
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Lu JY. Modulation of Point Spread Function for Super-Resolution Imaging. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2024; 71:153-171. [PMID: 37988211 DOI: 10.1109/tuffc.2023.3335883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
High image resolution is desired in wave-related areas such as ultrasound, acoustics, optics, and electromagnetics. However, the spatial resolution of an imaging system is limited by the spatial frequency of the point spread function (PSF) of the system due to diffraction. In this article, the PSF is modulated in amplitude, phase, or both to increase the spatial frequency to reconstruct super-resolution images of objects or wave sources/fields, where the modulator can be a focused shear wave produced remotely by, for example, a radiation force from a focused Bessel beam or X-wave, or can be a small particle manipulated remotely by a radiation-force (such as acoustic and optical tweezers) or electrical and magnetic forces. A theory of the PSF-modulation method was developed, and computer simulations and experiments were conducted. The result of an ultrasound experiment shows that a pulse-echo (two-way) image reconstructed has a super-resolution (0.65 mm) as compared to the diffraction limit (2.65 mm) using a 0.5-mm-diameter modulator at 1.483-mm wavelength, and the signal-to-noise ratio (SNR) of the image was about 31 dB. If the minimal SNR of a "visible" image is 3, the resolution can be further increased to about 0.19 mm by decreasing the size of the modulator. Another ultrasound experiment shows that a wave source was imaged (one-way) at about 30-dB SNR using the same modulator size and wavelength above. The image clearly separated two 0.5-mm spaced lines, which gives a 7.26-fold higher resolution than that of the diffraction limit (3.63 mm). Although, in theory, the method has no limit on the highest achievable image resolution, in practice, the resolution is limited by noises. Also, a PSF-weighted super-resolution imaging method based on the PSF-modulation method was developed. This method is easier to implement but may have some limitations. Finally, the methods above can be applied to imaging systems of an arbitrary PSF and can produce 4-D super-resolution images. With a proper choice of a modulator (e.g., a quantum dot) and imaging system, nanoscale (a few nanometers) imaging is possible.
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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Collins GC, Rojas SS, Bercu ZL, Desai JP, Lindsey BD. Supervised segmentation for guiding peripheral revascularization with forward-viewing, robotically steered ultrasound guidewire. Med Phys 2023; 50:3459-3474. [PMID: 36906877 PMCID: PMC10272103 DOI: 10.1002/mp.16350] [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: 05/05/2022] [Revised: 01/19/2023] [Accepted: 02/26/2023] [Indexed: 03/13/2023] Open
Abstract
BACKGROUND Approximately 500 000 patients present with critical limb ischemia (CLI) each year in the U.S., requiring revascularization to avoid amputation. While peripheral arteries can be revascularized via minimally invasive procedures, 25% of cases with chronic total occlusions are unsuccessful due to inability to route the guidewire beyond the proximal occlusion. Improvements to guidewire navigation would lead to limb salvage in a greater number of patients. PURPOSE Integrating ultrasound imaging into the guidewire could enable direct visualization of routes for guidewire advancement. In order to navigate a robotically-steerable guidewire with integrated imaging beyond a chronic occlusion proximal to the symptomatic lesion for revascularization, acquired ultrasound images must be segmented to visualize the path for guidewire advancement. METHODS The first approach for automated segmentation of viable paths through occlusions in peripheral arteries is demonstrated in simulations and experimentally-acquired data with a forward-viewing, robotically-steered guidewire imaging system. B-mode ultrasound images formed via synthetic aperture focusing (SAF) were segmented using a supervised approach (U-net architecture). A total of 2500 simulated images were used to train the classifier to distinguish the vessel wall and occlusion from viable paths for guidewire advancement. First, the size of the synthetic aperture resulting in the highest classification performance was determined in simulations (90 test images) and compared with traditional classifiers (global thresholding, local adaptive thresholding, and hierarchical classification). Next, classification performance as a function of the diameter of the remaining lumen (0.5 to 1.5 mm) in the partially-occluded artery was tested using both simulated (60 test images at each of 7 diameters) and experimental data sets. Experimental test data sets were acquired in four 3D-printed phantoms from human anatomy and six ex vivo porcine arteries. Accuracy of classifying the path through the artery was evaluated using microcomputed tomography of phantoms and ex vivo arteries as a ground truth for comparison. RESULTS An aperture size of 3.8 mm resulted in the best-performing classification based on sensitivity and Jaccard index, with a significant increase in Jaccard index (p < 0.05) as aperture diameter increased. In comparing the performance of the supervised classifier and traditional classification strategies with simulated test data, sensitivity and F1 score for U-net were 0.95 ± 0.02 and 0.96 ± 0.01, respectively, compared to 0.83 ± 0.03 and 0.41 ± 0.13 for the best-performing conventional approach, hierarchical classification. In simulated test images, sensitivity (p < 0.05) and Jaccard index both increased with increasing artery diameter (p < 0.05). Classification of images acquired in artery phantoms with remaining lumen diameters ≥ 0.75 mm resulted in accuracies > 90%, while mean accuracy decreased to 82% when artery diameter decreased to 0.5 mm. For testing in ex vivo arteries, average binary accuracy, F1 score, Jaccard index, and sensitivity each exceeded 0.9. CONCLUSIONS Segmentation of ultrasound images of partially-occluded peripheral arteries acquired with a forward-viewing, robotically-steered guidewire system was demonstrated for the first-time using representation learning. This could represent a fast, accurate approach for guiding peripheral revascularization.
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Affiliation(s)
- Graham C. Collins
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA, 30309
| | - Stephan Strassle Rojas
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA, 30309
| | - Zachary L. Bercu
- Interventional Radiology, Emory University School of Medicine, Atlanta, GA, USA, 30308
| | - Jaydev P. Desai
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA, 30309
| | - Brooks D. Lindsey
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA, 30309
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA, 30309
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Arora P, Singh P, Girdhar A, Vijayvergiya R. Calcification Detection in Intravascular Ultrasound (IVUS) Images Using Transfer Learning Based MultiSVM model. ULTRASONIC IMAGING 2023; 45:136-150. [PMID: 37052393 DOI: 10.1177/01617346231164574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Cardiovascular disease serves as the leading cause of death worldwide. Calcification detection is considered an important factor in cardiovascular diseases. Currently, medical practitioners visually inspect the presence of calcification using intravascular ultrasound (IVUS) images. The study aims to detect the extent of calcification as belonging to class I, II as mild calcification, and class III, IV as dense calcification from IVUS images acquired at 40 MHz. To detect calcification, the features were extracted using improved AlexNet architecture and then were fed into machine learning classifiers. The experiments were carried out using 14 real IVUS pullbacks of 10 patients. Experimental results show that the combination of traditional machine learning with deep learning approaches significantly improves accuracy. The results show that support vector machines outperform all other classifiers. The proposed model is compared with two other pre-trained models GoogLeNet (98.8%), SqueezeNet (99.2%), and exhibits considerable improvement in classification accuracy (99.8%). In the future other models such as Vision Transformers could be explored with additional feature selection methods such as ReliefF, PSO, ACO, etc. to improve the overall accuracy of diagnosis.
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Affiliation(s)
- Priyanka Arora
- IKG Punjab Technical University, Punjab, India
- Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
| | - Parminder Singh
- Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
| | - Akshay Girdhar
- Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
| | - Rajesh Vijayvergiya
- Department of Cardiology, Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
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Xia M, Yang H, Huang Y, Qu Y, Zhou G, Zhang F, Wang Y, Guo Y. 3D pyramidal densely connected network with cross-frame uncertainty guidance for intravascular ultrasound sequence segmentation. Phys Med Biol 2023; 68. [PMID: 36745930 DOI: 10.1088/1361-6560/acb988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/06/2023] [Indexed: 02/08/2023]
Abstract
Objective. Automatic extraction of external elastic membrane border (EEM) and lumen-intima border (LIB) in intravascular ultrasound (IVUS) sequences aids atherosclerosis diagnosis. Existing IVUS segmentation networks ignored longitudinal relations among sequential images and neglected that IVUS images of different vascular conditions vary largely in intricacy and informativeness. As a result, they suffered from performance degradation in complicated parts in IVUS sequences.Approach. In this paper, we develop a 3D Pyramidal Densely-connected Network (PDN) with Adaptive learning and post-Correction guided by a novel cross-frame uncertainty (CFU). The proposed method is named PDN-AC. Specifically, the PDN enables the longitudinal information exploitation and the effective perception of size-varied vessel regions in IVUS samples, by pyramidally connecting multi-scale 3D dilated convolutions. Additionally, the CFU enhances the robustness of the method to complicated pathology from the frame-level (f-CFU) and pixel-level (p-CFU) via exploiting cross-frame knowledge in IVUS sequences. The f-CFU weighs the complexity of IVUS frames and steers an adaptive sampling during the PDN training. The p-CFU visualizes uncertain pixels probably misclassified by the PDN and guides an active contour-based post-correction.Main results. Human and animal experiments were conducted on IVUS datasets acquired from atherosclerosis patients and pigs. Results showed that the f-CFU weighted adaptive sampling reduced the Hausdorff distance (HD) by 10.53%/7.69% in EEM/LIB detection. Improvements achieved by the p-CFU guided post-correction were 2.94%/5.56%.Significance. The PDN-AC attained mean Jaccard values of 0.90/0.87 and HD values of 0.33/0.34 mm in EEM/LIB detection, preferable to state-of-the-art IVUS segmentation methods.
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Affiliation(s)
- Menghua Xia
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China
| | - Hongbo Yang
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai 200032, People's Republic of China
| | - Yi Huang
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China
| | - Yanan Qu
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai 200032, People's Republic of China
| | - Guohui Zhou
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China.,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Fudan University, Shanghai 200032, People's Republic of China
| | - Feng Zhang
- Department of Cardiology, Zhongshan Hospital, Fudan University. Shanghai Institute of Cardiovascular Diseases, Shanghai 200032, People's Republic of China
| | - Yuanyuan Wang
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China.,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Fudan University, Shanghai 200032, People's Republic of China
| | - Yi Guo
- Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, People's Republic of China.,Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai, Fudan University, Shanghai 200032, People's Republic of China
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Arora P, Singh P, Girdhar A, Vijayvergiya R. A State-Of-The-Art Review on Coronary Artery Border Segmentation Algorithms for Intravascular Ultrasound (IVUS) Images. Cardiovasc Eng Technol 2023; 14:264-295. [PMID: 36650320 DOI: 10.1007/s13239-023-00654-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 11/28/2022] [Accepted: 01/02/2023] [Indexed: 01/19/2023]
Abstract
Intravascular Ultrasound images (IVUS) is a useful guide for medical practitioners to identify the vascular status of coronary arteries in human beings. IVUS is a unique intracoronary imaging modality that is used as an adjunct to angioplasty to view vessel structures using a catheter with high resolutions. Segmentation of IVUS images has always remained a challenging task due to various impediments, for example, similar tissue components, vessel structures, and artifacts imposed during the acquisition process. Many researchers have applied various techniques to develop standard methods of image interpretation, however, the ultimate goal is still elusive to most researchers. This challenge was presented at the MICCAI- Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop in 2011. This paper presents a major review of recently reported work in the field, with a detailed analysis of various segmentation techniques applied in IVUS, and highlights the directions for future research. The findings recommend a reference database with a larger number of samples acquired at varied transducer frequencies with special consideration towards complex lesions, suitable validation metrics, and ground-truth definition as a standard against which to compare new and current algorithms.
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Affiliation(s)
- Priyanka Arora
- Research Scholar, IKG Punjab Technical University, Punjab, India. .,Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India.
| | - Parminder Singh
- Department of Computer Science and Engineering, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
| | - Akshay Girdhar
- Department of Information Technology, Guru Nanak Dev Engineering College, Ludhiana, Punjab, India
| | - Rajesh Vijayvergiya
- Department of Cardiology, Advanced Cardiac Centre, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
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3D vessel-like structure segmentation in medical images by an edge-reinforced network. Med Image Anal 2022; 82:102581. [DOI: 10.1016/j.media.2022.102581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 05/04/2022] [Accepted: 08/11/2022] [Indexed: 11/15/2022]
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Shoaib MA, Lai KW, Chuah JH, Hum YC, Ali R, Dhanalakshmi S, Wang H, Wu X. Comparative studies of deep learning segmentation models for left ventricle segmentation. Front Public Health 2022; 10:981019. [PMID: 36091529 PMCID: PMC9453312 DOI: 10.3389/fpubh.2022.981019] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/08/2022] [Indexed: 01/25/2023] Open
Abstract
One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the left ventricle (LV) from 2D echocardiographic images is a common medical procedure for heart patients. Consistent and accurate segmentation of the LV exerts significant impact on the understanding of the normal anatomy of the heart, as well as the ability to distinguish the aberrant or diseased structure of the heart. Therefore, LV segmentation is an important and critical task in medical practice, and automated LV segmentation is a pressing need. The deep learning models have been utilized in research for automatic LV segmentation. In this work, three cutting-edge convolutional neural network architectures (SegNet, Fully Convolutional Network, and Mask R-CNN) are designed and implemented to segment the LV. In addition, an echocardiography image dataset is generated, and the amount of training data is gradually increased to measure segmentation performance using evaluation metrics. The pixel's accuracy, precision, recall, specificity, Jaccard index, and dice similarity coefficients are applied to evaluate the three models. The Mask R-CNN model outperformed the other two models in these evaluation metrics. As a result, the Mask R-CNN model is used in this study to examine the effect of training data. For 4,000 images, the network achieved 92.21% DSC value, 85.55% Jaccard index, 98.76% mean accuracy, 96.81% recall, 93.15% precision, and 96.58% specificity value. Relatively, the Mask R-CNN outperformed other architectures, and the performance achieves stability when the model is trained using more than 4,000 training images.
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Affiliation(s)
- Muhammad Ali Shoaib
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,*Correspondence: Khin Wee Lai
| | - Joon Huang Chuah
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Kajang, Malaysia
| | - Raza Ali
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,Faculty of Information and Communication Technology, BUITEMS, Quetta, Pakistan
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India,Samiappan Dhanalakshmi
| | - Huanhuan Wang
- Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China
| | - Xiang Wu
- Institute of Medical Information Security, Xuzhou Medical University, Xuzhou, China,Xiang Wu
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Su B, Wang Z, Gong Y, Li M, Teng Y, Yu S, Zong Y, Yao W, Wang J. Anal center detection and classification of perianal healthy condition. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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13
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Bass RD, Garcia-Garcia HM, Sanz-Sánchez J, Ziemer PGP, Bulant CA, Kuku KK, Kahsay YA, Beyene S, Melaku G, Otsuka T, Choi J, Fernández-Peregrina E, Erdogan E, Gonzalo N, Bourantas CV, Blanco PJ, Räber L. Human vs. machine vs. core lab for the assessment of coronary atherosclerosis with lumen and vessel contour segmentation with intravascular ultrasound. THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2022; 38:1431-1439. [PMID: 38819542 DOI: 10.1007/s10554-022-02563-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/08/2022] [Indexed: 12/13/2022]
Abstract
A machine learning (ML) algorithm for automatic segmentation of intravascular ultrasound was previously validated. It has the potential to improve efficiency, accuracy and precision of coronary vessel segmentation compared to manual segmentation by interventional cardiology experts. The aim of this study is to compare the performance of human readers to the machine and against the readings from a Core Laboratory. This is a post-hoc, cross-sectional analysis of the IBIS-4 study. Forty frames were randomly selected and analyzed by 10 readers of varying expertise two separate times, 1 week apart. Their measurements of lumen, vessel, plaque areas, and plaque burden were performed in an offline software. Among humans, the intra-observer variability was not statistically significant. For the total 80 frames, inter-observer variability between human readers, the ML algorithm and Core Laboratory for lumen area, vessel area, plaque area and plaque burden were not statistically different. For lumen area, however, relative differences between the human readers and the Core Lab ranged from 0.26 to 12.61%. For vessel area, they ranged from 1.25 to 9.54%. Efficiency between the ML algorithm and the readers differed notably. Humans spent 47 min on average to complete the analyses, while the ML algorithm took on average less than 1 min. The overall lumen, vessel and plaque means analyzed by humans and the proposed ML algorithm are similar to those of the Core Lab. Machines, however, are more time efficient. It is warranted to consider use of the ML algorithm in clinical practice.
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Affiliation(s)
- Ronald D Bass
- School of Medicine, Georgetown University, Washington, DC, USA
| | - Hector M Garcia-Garcia
- Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA.
- Division of Interventional Cardiology of MedStar Cardiovascular Research Network at MedStar Washington Hospital Center, 110 Irving Street, Suite 4B-1, Washington, DC, 20010, USA.
| | - Jorge Sanz-Sánchez
- Hospital Universitari i Politecnic La Fe, Valencia, Spain
- Centro de Investigación Biomedica en Red (CIBERCV), Madrid, Spain
| | - Paulo G P Ziemer
- National Laboratory for Scientific Computing, Petrópolis, Brazil
| | - Carlos A Bulant
- National Scientific and Technical Research Council, Tandil, Argentina
| | - Kayode K Kuku
- Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA
| | - Yirga A Kahsay
- Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA
| | - Solomon Beyene
- Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA
| | - Gebremedhin Melaku
- Interventional Cardiology, MedStar Washington Hospital Center, Washington, DC, USA
| | - Tatsuhiko Otsuka
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - JooHee Choi
- School of Medicine, Georgetown University, Washington, DC, USA
| | | | - Emrah Erdogan
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Nieves Gonzalo
- Interventional Cardiology, Hospital Universitario Clínico San Carlos, Madrid, Spain
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Pablo J Blanco
- National Laboratory for Scientific Computing, Petrópolis, Brazil
| | - Lorenz Räber
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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15
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Sharifzadeh M, Benali H, Rivaz H. Investigating Shift Variance of Convolutional Neural Networks in Ultrasound Image Segmentation. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:1703-1713. [PMID: 35344491 DOI: 10.1109/tuffc.2022.3162800] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
While accuracy is an evident criterion for ultrasound image segmentation, output consistency across different tests is equally crucial for tracking changes in regions of interest in applications such as monitoring the patients' response to treatment, measuring the progression or regression of the disease, reaching a diagnosis, or treatment planning. Convolutional neural networks (CNNs) have attracted rapidly growing interest in automatic ultrasound image segmentation recently. However, CNNs are not shift-equivariant, meaning that, if the input translates, e.g., in the lateral direction by one pixel, the output segmentation may drastically change. To the best of our knowledge, this problem has not been studied in ultrasound image segmentation or even more broadly in ultrasound images. Herein, we investigate and quantify the shift-variance problem of CNNs in this application and further evaluate the performance of a recently published technique, called BlurPooling, for addressing the problem. In addition, we propose the Pyramidal BlurPooling method that outperforms BlurPooling in both output consistency and segmentation accuracy. Finally, we demonstrate that data augmentation is not a replacement for the proposed method. Source code is available at http://code.sonography.ai.
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16
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Detection of Healthy and Diseased Pylorus Natural Anatomical Center with Convolutional Neural Network Classification and Filters. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00696-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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17
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Blanco PJ, Ziemer PGP, Bulant CA, Ueki Y, Bass R, Räber L, Lemos PA, García-García HM. Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets. Med Image Anal 2021; 75:102262. [PMID: 34670148 DOI: 10.1016/j.media.2021.102262] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 10/20/2022]
Abstract
Segmentation of lumen and vessel contours in intravascular ultrasound (IVUS) pullbacks is an arduous and time-consuming task, which demands adequately trained human resources. In the present study, we propose a machine learning approach to automatically extract lumen and vessel boundaries from IVUS datasets. The proposed approach relies on the concatenation of a deep neural network to deliver a preliminary segmentation, followed by a Gaussian process (GP) regressor to construct the final lumen and vessel contours. A multi-frame convolutional neural network (MFCNN) exploits adjacency information present in longitudinally neighboring IVUS frames, while the GP regression method filters high-dimensional noise, delivering a consistent representation of the contours. Overall, 160 IVUS pullbacks (63 patients) from the IBIS-4 study (Integrated Biomarkers and Imaging Study-4, Trial NCT00962416), were used in the present work. The MFCNN algorithm was trained with 100 IVUS pullbacks (8427 manually segmented frames), was validated with 30 IVUS pullbacks (2583 manually segmented frames) and was blindly tested with 30 IVUS pullbacks (2425 manually segmented frames). Image and contour metrics were used to characterize model performance by comparing ground truth (GT) and machine learning (ML) contours. Median values (interquartile range, IQR) of the Jaccard index for lumen and vessel were 0.913, [0.882,0.935] and 0.940, [0.917,0.957], respectively. Median values (IQR) of the Hausdorff distance for lumen and vessel were 0.196mm, [0.146,0.275]mm and 0.163mm, [0.122,0.234]mm, respectively. Also, the mean value of lumen area predictions, and limits of agreement were -0.19mm2, [1.1,-1.5]mm2, while the mean value and limits of agreement of plaque burden were 0.0022, [0.082,-0.078]. The results obtained with the model developed in this work allow us to conclude that the proposed machine learning approach delivers accurate segmentations in terms of image metrics, contour metrics and clinically relevant variables, enabling its use in clinical routine by mitigating the costs involved in the manual management of IVUS datasets.
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Affiliation(s)
- Pablo J Blanco
- National Laboratory for Scientific Computing, LNCC/MCTI, Petrópolis, Brazil; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil.
| | - Paulo G P Ziemer
- National Laboratory for Scientific Computing, LNCC/MCTI, Petrópolis, Brazil; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil
| | - Carlos A Bulant
- Consejo Nacional de Investigaciones Científicas, CONICET, Argentina; Universidad Nacional del Centro, UNICEN, Tandil, Argentina; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil
| | - Yasushi Ueki
- Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ronald Bass
- Interventional Cardiology Department, MedStar Washington Hospital Center, Washington, DC, USA; Georgetown University School of Medicine, Washington, DC, USA
| | - Lorenz Räber
- Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Pedro A Lemos
- Hospital Israelita Albert Einstein, São Paulo, Brazil; National Institute of Science and Technology in Medicine Assisted by Scientific Computing, INCT-MACC, Petrópolis, RJ, Brazil
| | - Héctor M García-García
- Interventional Cardiology Department, MedStar Washington Hospital Center, Washington, DC, USA; Georgetown University School of Medicine, Washington, DC, USA.
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18
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Bargsten L, Raschka S, Schlaefer A. Capsule networks for segmentation of small intravascular ultrasound image datasets. Int J Comput Assist Radiol Surg 2021; 16:1243-1254. [PMID: 34125391 PMCID: PMC8295165 DOI: 10.1007/s11548-021-02417-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 05/21/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE Intravascular ultrasound (IVUS) imaging is crucial for planning and performing percutaneous coronary interventions. Automatic segmentation of lumen and vessel wall in IVUS images can thus help streamlining the clinical workflow. State-of-the-art results in image segmentation are achieved with data-driven methods like convolutional neural networks (CNNs). These need large amounts of training data to perform sufficiently well but medical image datasets are often rather small. A possibility to overcome this problem is exploiting alternative network architectures like capsule networks. METHODS We systematically investigated different capsule network architecture variants and optimized the performance on IVUS image segmentation. We then compared our capsule network with corresponding CNNs under varying amounts of training images and network parameters. RESULTS Contrary to previous works, our capsule network performs best when doubling the number of capsule types after each downsampling stage, analogous to typical increase rates of feature maps in CNNs. Maximum improvements compared to the baseline CNNs are 20.6% in terms of the Dice coefficient and 87.2% in terms of the average Hausdorff distance. CONCLUSION Capsule networks are promising candidates when it comes to segmentation of small IVUS image datasets. We therefore assume that this also holds for ultrasound images in general. A reasonable next step would be the investigation of capsule networks for few- or even single-shot learning tasks.
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Affiliation(s)
- Lennart Bargsten
- Hamburg University of Technology, Institute of Medical Technology and Intelligent Systems, Hamburg, Germany.
| | - Silas Raschka
- Hamburg University of Technology, Institute of Medical Technology and Intelligent Systems, Hamburg, Germany
| | - Alexander Schlaefer
- Hamburg University of Technology, Institute of Medical Technology and Intelligent Systems, Hamburg, Germany
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19
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Szarski M, Chauhan S. Improved real-time segmentation of Intravascular Ultrasound images using coordinate-aware fully convolutional networks. Comput Med Imaging Graph 2021; 91:101955. [PMID: 34252744 DOI: 10.1016/j.compmedimag.2021.101955] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 07/01/2021] [Accepted: 07/02/2021] [Indexed: 11/26/2022]
Abstract
Segmentation of Intravascular Ultrasound (IVUS) images into Lumen and Media (interior and exterior) artery vessel walls is highly clinically relevant in the diagnosis and treatment of cardiovascular diseases such as atherosclerosis. When fused with position data, such segmentations also play a key role in reconstructing 3D representations of arteries. Automated segmentation in real-time is known to be a difficult image analysis problem, primarily due to artefacts commonly present in IVUS ultrasound images such as shadows, guide-wire effects, and side-branches. An additional challenge is the limited amount of expert labelled IVUS data, which limits the application of many well-performing deep learning models from other domains. To exploit the circular layered structure of the artery in B-Mode images, we propose a multi-class fully convolutional semantic segmentation network based on a minimal U-Net architecture augmented with learned translation dependence in the polar domain. The coordinate awareness in the multi-class segmentation allows the model to exploit relative spatial context about the interior and exterior vessel walls which are simply separable in polar coordinates. After training on 109 expert-labelled examples, our model significantly outperforms the state-of-the art in terms of mean Jaccard Measure (0.91 vs. 0.89) and Hausdorff distance (0.32 mm vs. 0.48 mm) on Media segmentation, and reaches equivalent performance on Lumen segmentation when evaluated on a standard publicly available dataset of 326 IVUS B-Mode images captured by 20 Mhz ultrasound probes. Using an order of magnitude fewer trainable parameters than the previous state-of-the-art, our model runs over 50 times faster and is able to execute in only 3 ms on a common GPU, achieving both leading accuracy and practical real-time performance.
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Affiliation(s)
- Martin Szarski
- Department of Mechanical and Aerospace Engineering, Monash University, Melbourne, Australia
| | - Sunita Chauhan
- Department of Mechanical and Aerospace Engineering, Monash University, Melbourne, Australia.
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20
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Bajaj R, Huang X, Kilic Y, Ramasamy A, Jain A, Ozkor M, Tufaro V, Safi H, Erdogan E, Serruys PW, Moon J, Pugliese F, Mathur A, Torii R, Baumbach A, Dijkstra J, Zhang Q, Bourantas CV. Advanced deep learning methodology for accurate, real-time segmentation of high-resolution intravascular ultrasound images. Int J Cardiol 2021; 339:185-191. [PMID: 34153412 DOI: 10.1016/j.ijcard.2021.06.030] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/10/2021] [Accepted: 06/15/2021] [Indexed: 11/29/2022]
Abstract
AIMS The aim of this study is to develop and validate a deep learning (DL) methodology capable of automated and accurate segmentation of intravascular ultrasound (IVUS) image sequences in real-time. METHODS AND RESULTS IVUS segmentation was performed by two experts who manually annotated the external elastic membrane (EEM) and lumen borders in the end-diastolic frames of 197 IVUS sequences portraying the native coronary arteries of 65 patients. The IVUS sequences of 177 randomly-selected vessels were used to train and optimise a novel DL model for the segmentation of IVUS images. Validation of the developed methodology was performed in 20 vessels using the estimations of two expert analysts as the reference standard. The mean difference for the EEM, lumen and plaque area between the DL-methodology and the analysts was ≤0.23mm2 (standard deviation ≤0.85mm2), while the Hausdorff and mean distance differences for the EEM and lumen borders was ≤0.19 mm (standard deviation≤0.17 mm). The agreement between DL and experts was similar to experts' agreement (Williams Index ranges: 0.754-1.061) with similar results in frames portraying calcific plaques or side branches. CONCLUSIONS The developed DL-methodology appears accurate and capable of segmenting high-resolution real-world IVUS datasets. These features are expected to facilitate its broad adoption and enhance the applications of IVUS in clinical practice and research.
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Affiliation(s)
- Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, UK
| | - Xingru Huang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
| | - Yakup Kilic
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, UK
| | - Ajay Jain
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Mick Ozkor
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Vincenzo Tufaro
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Hannah Safi
- Department of Mechanical Engineering, University College London, London, UK
| | - Emrah Erdogan
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Patrick W Serruys
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, UK
| | - James Moon
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Institute of Cardiovascular Sciences, University College London, London, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, UK
| | - Anthony Mathur
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, UK
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | - Andreas Baumbach
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, UK
| | - Jouke Dijkstra
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, the Netherlands
| | - Qianni Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Cardiovascular Devices Hub, Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, UK; Institute of Cardiovascular Sciences, University College London, London, UK.
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21
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Sung JH, Chang JH. Mechanically Rotating Intravascular Ultrasound (IVUS) Transducer: A Review. SENSORS (BASEL, SWITZERLAND) 2021; 21:3907. [PMID: 34198822 PMCID: PMC8201242 DOI: 10.3390/s21113907] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/31/2021] [Accepted: 06/03/2021] [Indexed: 12/30/2022]
Abstract
Intravascular ultrasound (IVUS) is a valuable imaging modality for the diagnosis of atherosclerosis. It provides useful clinical information, such as lumen size, vessel wall thickness, and plaque composition, by providing a cross-sectional vascular image. For several decades, IVUS has made remarkable progress in improving the accuracy of diagnosing cardiovascular disease that remains the leading cause of death globally. As the quality of IVUS images mainly depends on the performance of the IVUS transducer, various IVUS transducers have been developed. Therefore, in this review, recently developed mechanically rotating IVUS transducers, especially ones exploiting piezoelectric ceramics or single crystals, are discussed. In addition, this review addresses the history and technical challenges in the development of IVUS transducers and the prospects of next-generation IVUS transducers.
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Affiliation(s)
| | - Jin-Ho Chang
- Department of Information and Communication Engineering, Deagu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea;
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22
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Peng C, Wu H, Kim S, Dai X, Jiang X. Recent Advances in Transducers for Intravascular Ultrasound (IVUS) Imaging. SENSORS (BASEL, SWITZERLAND) 2021; 21:3540. [PMID: 34069613 PMCID: PMC8160965 DOI: 10.3390/s21103540] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/11/2021] [Accepted: 05/17/2021] [Indexed: 12/16/2022]
Abstract
As a well-known medical imaging methodology, intravascular ultrasound (IVUS) imaging plays a critical role in diagnosis, treatment guidance and post-treatment assessment of coronary artery diseases. By cannulating a miniature ultrasound transducer mounted catheter into an artery, the vessel lumen opening, vessel wall morphology and other associated blood and vessel properties can be precisely assessed in IVUS imaging. Ultrasound transducer, as the key component of an IVUS system, is critical in determining the IVUS imaging performance. In recent years, a wide range of achievements in ultrasound transducers have been reported for IVUS imaging applications. Herein, a comprehensive review is given on recent advances in ultrasound transducers for IVUS imaging. Firstly, a fundamental understanding of IVUS imaging principle, evaluation parameters and IVUS catheter are summarized. Secondly, three different types of ultrasound transducers (piezoelectric ultrasound transducer, piezoelectric micromachined ultrasound transducer and capacitive micromachined ultrasound transducer) for IVUS imaging are presented. Particularly, the recent advances in piezoelectric ultrasound transducer for IVUS imaging are extensively examined according to their different working mechanisms, configurations and materials adopted. Thirdly, IVUS-based multimodality intravascular imaging of atherosclerotic plaque is discussed. Finally, summary and perspectives on the future studies are highlighted for IVUS imaging applications.
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Affiliation(s)
- Chang Peng
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA; (C.P.); (H.W.)
| | - Huaiyu Wu
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA; (C.P.); (H.W.)
| | | | - Xuming Dai
- Department of Cardiology, New York-Presbyterian Queens Hospital, Flushing, NY 11355, USA;
| | - Xiaoning Jiang
- Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC 27695, USA; (C.P.); (H.W.)
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Li YC, Shen TY, Chen CC, Chang WT, Lee PY, Huang CCJ. Automatic Detection of Atherosclerotic Plaque and Calcification From Intravascular Ultrasound Images by Using Deep Convolutional Neural Networks. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2021; 68:1762-1772. [PMID: 33460377 DOI: 10.1109/tuffc.2021.3052486] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Atherosclerosis is the major cause of cardiovascular diseases (CVDs). Intravascular ultrasound (IVUS) is a common imaging modality for diagnosing CVDs. However, an efficient analyzer for IVUS image segmentation is required for assisting cardiologists. In this study, an end-to-end deep-learning convolutional neural network was developed for automatically detecting media-adventitia borders, luminal regions, and calcified plaque in IVUS images. A total of 713 grayscale IVUS images from 18 patients were used as training data for the proposed deep-learning model. The model is constructed using the three modified U-Nets and combined with the concept of cascaded networks to prevent errors in the detection of calcification owing to the interference of pixels outside the plaque regions. Three loss functions (Dice, Tversky, and focal loss) with various characteristics were tested to determine the best setting for the proposed model. The efficacy of the deep-learning model was evaluated by analyzing precision-recall curve. The average precision (AP), Dice score coefficient, precision, sensitivity, and specificity of the predicted and ground truth results were then compared. All training processes were validated using leave-one-subject-out cross-validation. The experimental results showed that the proposed deep-learning model exhibits high performance in segmenting the media-adventitia layers and luminal regions for all loss functions, with all tested metrics being higher than 0.90. For locating calcified tissues, the best result was obtained when the focal loss function was applied to the proposed model, with an AP of 0.73; however, the prediction efficacy was affected by the proportion of calcified tissues within the plaque region when the focal loss function was employed. Compared with commercial software, the proposed method exhibited high accuracy in segmenting IVUS images in some special cases, such as when shadow artifacts or side vessels surrounded the target vessel.
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24
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Cismaru G, Serban T, Tirpe A. Ultrasound Methods in the Evaluation of Atherosclerosis: From Pathophysiology to Clinic. Biomedicines 2021; 9:418. [PMID: 33924492 PMCID: PMC8070406 DOI: 10.3390/biomedicines9040418] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 12/11/2022] Open
Abstract
Atherosclerosis is a key pathological process that causes a plethora of pathologies, including coronary artery disease, peripheral artery disease, and ischemic stroke. The silent progression of the atherosclerotic disease prompts for new surveillance tools that can visualize, characterize, and provide a risk evaluation of the atherosclerotic plaque. Conventional ultrasound methods-bright (B)-mode US plus Doppler mode-provide a rapid, cost-efficient way to visualize an established plaque and give a rapid risk stratification of the patient through the Gray-Weale standardization-echolucent plaques with ≥50% stenosis have a significantly greater risk of ipsilateral stroke. Although rather disputed, the measurement of carotid intima-media thickness (C-IMT) may prove useful in identifying subclinical atherosclerosis. In addition, contrast-enhanced ultrasonography (CEUS) allows for a better image resolution and the visualization and quantification of plaque neovascularization, which has been correlated with future cardiovascular events. Newly emerging elastography techniques such as strain elastography and shear-wave elastography add a new dimension to this evaluation-the biomechanics of the arterial wall, which is altered in atherosclerosis. The invasive counterpart, intravascular ultrasound (IVUS), enables an individualized assessment of the anti-atherosclerotic therapies, as well as a direct risk assessment of these lesions through virtual histology IVUS.
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Affiliation(s)
- Gabriel Cismaru
- Fifth Department of Internal Medicine, Cardiology-Rehabilitation, Iuliu Hatieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania;
| | - Teodora Serban
- Medical Imaging Department, Iuliu Hatieganu University of Medicine and Pharmacy, 400162 Cluj-Napoca, Romania;
| | - Alexandru Tirpe
- Research Center for Functional Genomics, Biomedicine and Translational Medicine, Iuliu Hatieganu University of Medicine and Pharmacy, 23 Marinescu Street, 400337 Cluj-Napoca, Romania
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25
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Tong J, Li K, Lin W, Shudong X, Anwar A, Jiang L. Automatic lumen border detection in IVUS images using dictionary learning and kernel sparse representation. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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26
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Li K, Tong J, Zhu X, Xia S. Automatic Lumen Border Detection in IVUS Images Using Deep Learning Model and Handcrafted Features. ULTRASONIC IMAGING 2021; 43:59-73. [PMID: 33448256 DOI: 10.1177/0161734620987288] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
In the clinical analysis of Intravascular ultrasound (IVUS) images, the lumen size is an important indicator of coronary atherosclerosis, and is also the premise of coronary artery disease diagnosis and interventional treatment. In this study, a fully automatic method based on deep learning model and handcrafted features is presented for the detection of the lumen borders in IVUS images. First, 193 handcrafted features are extracted from the IVUS images. Then hybrid feature vectors are constructed by combining handcrafted features with 64 high-level features extracted from U-Net. In order to obtain the feature subsets with larger contribution, we employ the extended binary cuckoo search for feature selection. Finally, the selected 36-dimensional hybrid feature subset is used to classify the test images using dictionary learning based on kernel sparse coding. The proposed algorithm is tested on the publicly available dataset and evaluated using three indicators. Through ablation experiments, mean value of the experimental results (Jaccard: 0.88, Hausdorff distance: 0.36, Percentage of the area difference: 0.06) prove to be effective improving lumen border detection. Furthermore, compared with the recent methods used on the same dataset, the proposed method shows good performance and high accuracy.
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Affiliation(s)
- Kai Li
- Zhejiang Sci-Tech University, Hangzhou, China
| | - Jijun Tong
- Zhejiang Sci-Tech University, Hangzhou, China
| | - Xinjian Zhu
- Zhejiang University School of Medicine, Yiwu, China
| | - Shudong Xia
- Zhejiang University School of Medicine, Yiwu, China
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Sung JH, Jeong EY, Jeong JS. Intravascular Ultrasound Transducer by Using Polarization Inversion Technique for Tissue Harmonic Imaging: Modeling and Experiments. IEEE Trans Biomed Eng 2020; 67:3380-3391. [PMID: 32286955 DOI: 10.1109/tbme.2020.2986284] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Intravascular ultrasound (IVUS) tissue harmonic imaging (THI) is a useful vessel imaging technique that can provide deep penetration depth as well as high spatial and contrast resolution. Typically, a high-frequency IVUS transducer for THI requires a broad bandwidth or dual-frequency bandwidth. However, it is very difficult to make an IVUS transducer with a frequency bandwidth covering from the fundamental frequency to the second harmonic or a dual-peak at the desired frequency. To solve this problem, in this study, we applied the polarization inversion technique (PIT) to the IVUS transducer for THI. The PIT makes it relatively easy to design IVUS transducers with suitable frequency characteristics for THI depending on the inversion ratio of the piezoelectric layer and specifications of the passive materials. In this study, two types of IVUS transducers based on the PIT were developed for THI. One is a front-side inversion layer (FSIL) transducer with a broad bandwidth, and the other is a back-side inversion layer (BSIL) transducer with a dual-frequency bandwidth. These transducers were designed using finite element analysis (FEA)-based simulation, and the prototype transducers were fabricated. Subsequently, the performance was evaluated by not only electrical impedance and pulse-echo response tests but also B-mode imaging tests with a 25 μm tungsten wire and tissue-mimicking gelatin phantoms. The FEA simulation and experimental results show that the proposed scheme can successfully implement the tissue harmonic IVUS image, and thus it can be one of the promising techniques for developing IVUS transducers for THI.
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Li X, D'Amico G, Quintini C, Uso TD, Gadani S, Romero-Marrero C, Martin C, Partovi S. Intravascular ultrasound in the diagnosis and treatment of central venous diseases. VASA 2020; 50:2-10. [PMID: 33138741 DOI: 10.1024/0301-1526/a000914] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Intravascular ultrasound (IVUS) has been used extensively in coronary applications. Its use in venous applications has increased as endovascular therapy has increasingly become the mainstay therapy for central venous diseases. IVUS has been used for both diagnostic and therapeutic purposes in managing venous stenotic disease, venous occlusive disease, and IVC filter placement and removal. IVUS has been proven to be effective in providing detailed measurement of the venous anatomy, which aid in determining the appropriate size and the approach for venous stent placement. In IVC filter placement, IVUS can provide detailed measurement and guide IVC filter placement in emergent and critical care settings. It also has certain utility in filter removal. At any rate, to date there are only a few studies examining its impact on patient outcomes. Prospective randomized controlled trials are warranted in the future.
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Affiliation(s)
- Xin Li
- Section of Interventional Radiology, Imaging Institute, Cleveland Clinic Main Campus, Cleveland, OH, USA
| | - Giuseppe D'Amico
- Department of Transplant Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Cristiano Quintini
- Department of Transplant Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Teresa Diago Uso
- Department of Transplant Surgery, Digestive Disease & Surgery Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Sameer Gadani
- Section of Interventional Radiology, Imaging Institute, Cleveland Clinic Main Campus, Cleveland, OH, USA
| | - Carlos Romero-Marrero
- Gastroenterology and Hepatology Department, Digestive Disease Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Charles Martin
- Section of Interventional Radiology, Imaging Institute, Cleveland Clinic Main Campus, Cleveland, OH, USA
| | - Sasan Partovi
- Section of Interventional Radiology, Imaging Institute, Cleveland Clinic Main Campus, Cleveland, OH, USA
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Xia M, Yan W, Huang Y, Guo Y, Zhou G, Wang Y. Extracting Membrane Borders in IVUS Images Using a Multi-Scale Feature Aggregated U-Net. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1650-1653. [PMID: 33018312 DOI: 10.1109/embc44109.2020.9175970] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Automatic extraction of the lumen-intima border (LIB) and the media-adventitia border (MAB) in intravascular ultrasound (IVUS) images is of high clinical interest. Despite the superior performance achieved by deep neural networks (DNNs) on various medical image segmentation tasks, there are few applications to IVUS images. The complicated pathological presentation and the lack of enough annotation in IVUS datasets make the learning process challenging. Several existing networks designed for IVUS segmentation train two groups of weights to detect the MAB and LIB separately. In this paper, we propose a multi-scale feature aggregated U-Net (MFAU-Net) to extract two membrane borders simultaneously. The MFAU-Net integrates multi-scale inputs, the deep supervision, and a bi-directional convolutional long short-term memory (BConvLSTM) unit. It is designed to sufficiently learn features from complicated IVUS images through a small number of training samples. Trained and tested on the publicly available IVUS datasets, the MFAU-Net achieves both 0.90 Jaccard measure (JM) for the MAB and LIB detection on 20 MHz dataset. The corresponding metrics on 40 MHz dataset are 0.85 and 0.84 JM respectively. Comparative evaluations with state-of-the-art published results demonstrate the competitiveness of the proposed MFAU-Net.
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Sikorski J, Mohanty S, Misra S. MILiMAC: Flexible Catheter With Miniaturized Electromagnets as a Small-Footprint System for Microrobotic Tasks. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.3004323] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
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31
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Liu S, Neleman T, Hartman EMJ, Ligthart JMR, Witberg KT, van der Steen AFW, Wentzel JJ, Daemen J, van Soest G. Automated Quantitative Assessment of Coronary Calcification Using Intravascular Ultrasound. ULTRASOUND IN MEDICINE & BIOLOGY 2020; 46:2801-2809. [PMID: 32636052 DOI: 10.1016/j.ultrasmedbio.2020.04.032] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 04/08/2020] [Accepted: 04/28/2020] [Indexed: 06/11/2023]
Abstract
Coronary calcification represents a challenge in the treatment of coronary artery disease by stent placement. It negatively affects stent expansion and has been related to future adverse cardiac events. Intravascular ultrasound (IVUS) is known for its high sensitivity in detecting coronary calcification. At present, automated quantification of calcium as detected by IVUS is not available. For this reason, we developed and validated an optimized framework for accurate automated detection and quantification of calcified plaque in coronary atherosclerosis as seen by IVUS. Calcified lesions were detected by training a supported vector classifier per IVUS A-line on manually annotated IVUS images, followed by post-processing using regional information. We applied our framework to 35 IVUS pullbacks from each of the three commonly used IVUS systems. Cross-validation accuracy for each system was >0.9, and the testing accuracy was 0.87, 0.89 and 0.89 for the three systems. Using the detection result, we propose an IVUS calcium score, based on the fraction of calcium-positive A-lines in a pullback segment, to quantify the extent of calcified plaque. The high accuracy of the proposed classifier suggests that it may provide a robust and accurate tool to assess the presence and amount of coronary calcification and, thus, may play a role in image-guided coronary interventions.
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Affiliation(s)
- Shengnan Liu
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Tara Neleman
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Eline M J Hartman
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Jurgen M R Ligthart
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Karen T Witberg
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Antonius F W van der Steen
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, The Netherlands; Shenzhen Institutes of Advanced Technologies, Shenzhen, China
| | - Jolanda J Wentzel
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Joost Daemen
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Gijs van Soest
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands.
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Gao Z, Chung J, Abdelrazek M, Leung S, Hau WK, Xian Z, Zhang H, Li S. Privileged Modality Distillation for Vessel Border Detection in Intracoronary Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1524-1534. [PMID: 31715563 DOI: 10.1109/tmi.2019.2952939] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Intracoronary imaging is a crucial imaging technology in coronary disease diagnosis as it visualizes the internal tissue morphologies of coronary arteries. Vessel border detection in intracoronary images (VBDI) is desired because it can help the succeeding procedures of computer-aided disease diagnosis. However, existing VDBI methods suffer from the challenge of vessel-environment variability (i.e. high intra- and inter-subject diversity of vessels and their surrounding tissues appeared in images). This challenge leads to the ineffectiveness in the vessel region representation for hand-crafted features, in the receptive field extraction for deeply-represented features, as well as performance suppression derived from clinical data limitation. To solve this challenge, we propose a novel privileged modality distillation (PMD) framework for VBDI. PMD transforms the single-input-single-task (SIST) learning problem in the single-mode VBDI to a multiple-input-multiple-task (MIMT) problem by using the privileged image modality to help the learning model in the target modality. This learns the enriched high-level knowledge with similar semantics and generalizes PMD on diversity-increased low-level image features for improving the model adaptation to diverse vessel environments. Moreover, PMD refines MIMT to SIST by distilling the learned knowledge from multiple to one modality. This eliminates the reliance on privileged modality in the test phase, and thus enables the applicability to each of different intracoronary modalities. A structure-deformable neural network is proposed as an elaborately-designed implementation of PMD. It expands a conventional SIST network structure to the MIMT structure, and then recovers it to the final SIST structure. The PMD is validated on intravascular ultrasound imaging and optical coherence tomography imaging. One modality is the target, and the other one can be considered as the privileged modality owing to their semantic relatedness. The experiments show that our PMD is effective in VBDI (e.g. the Dice index is larger than 0.95), as well as superior to six state-of-the-art VBDI methods.
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On the Achievable Capacity of MIMO-OFDM Systems in the CathLab Environment. SENSORS 2020; 20:s20030938. [PMID: 32050676 PMCID: PMC7039008 DOI: 10.3390/s20030938] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 01/23/2020] [Accepted: 02/04/2020] [Indexed: 11/17/2022]
Abstract
In the last years, the evolution of digital communications has been harnessed by medical applications. In that context, wireless communications are preferable over wired communications, as they facilitate the work of health technicians by reducing cabling on the stretchers. However, the use of wireless communications is challenging, especially when high data rates and low latencies are required. In those scenarios, multiple-input multiple-output (MIMO) techniques might have an important role, thanks to the high capacity gains that they can exhibit, which ideally increase with the MIMO size. In this work, we study the propagation scenario of a typical medical laboratory through ray-tracing techniques. By taking into account the derived channel model, we study the potential of MIMO techniques in an IEEE 802.11ax environment. Through a set of performance results regarding the system capacity, we show that the MIMO gains might not be as high as supposed in the medical laboratory, being far from the ideal scenario. Therefore, the large data rates required by the modern medical imaging applications might only be achieved with a combination of MIMO systems and large bandwidths.
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Lo Vercio L, Del Fresno M, Larrabide I. Lumen-intima and media-adventitia segmentation in IVUS images using supervised classifications of arterial layers and morphological structures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 177:113-121. [PMID: 31319939 DOI: 10.1016/j.cmpb.2019.05.021] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 04/26/2019] [Accepted: 05/20/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND Intravascular ultrasound (IVUS) provides axial grey-scale images of blood vessels. The large number of images require automatic analysis, specifically to identify the lumen and outer vessel wall. However, the high amount of noise, the presence of artifacts and anatomical structures, such as bifurcations, calcifications and fibrotic plaques, usually hinder the proper automatic segmentation of the vessel wall. METHODS Lumen, media, adventitia and surrounding tissues are automatically detected using Support Vector Machines (SVMs). The classification performance of the SVMs vary according to the kind of structure present within each region of the image. Random Forest (RF) is used to detect different morphological structures and to modify the initial layer classification depending on the detected structure. The resulting classification maps are fed into a segmentation method based on deformable contours to detect lumen-intima (LI) and media-adventitia (MA) interfaces. RESULTS The modifications in the layer classifications according to the presence of structures proved to be effective improving LI and MA segmentations. The proposed method reaches a Jaccard Measure (JM) of 0.88 ± 0.08 for LI segmentation, compared with 0.88 ± 0.05 of a semiautomatic method. When looking at MA, our method reaches a JM of 0.84 ± 0.09, and outperforms previous automatic methods in terms of HD, with 0.51mm ± 0.30. CONCLUSIONS A simple modification to the arterial layer classification produces results that match and improve state-of-the-art fully-automatic segmentation methods for LI and MA in 20MHz IVUS images. For LI segmentation, the proposed automatic method performs accurately as semi-automatic methods. For MA segmentation, our method matched the quality of state-of-the-art automatic methods described in the literature. Furthermore, our implementation is modular and open-source, allowing for future extensions and improvements.
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Affiliation(s)
- Lucas Lo Vercio
- Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina.
| | - Mariana Del Fresno
- Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Comisión de Investigaciones Científicas de la Provincia deBuenos Aires (CICPBA), Argentina
| | - Ignacio Larrabide
- Pladema Institute, UNCPBA, Gral. Pinto 399, Tandil, Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
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35
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IVUS images segmentation using spatial fuzzy clustering and hierarchical level set evolution. Comput Biol Med 2019; 109:207-217. [DOI: 10.1016/j.compbiomed.2019.04.029] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 04/22/2019] [Accepted: 04/22/2019] [Indexed: 11/22/2022]
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36
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Hammouche A, Cloutier G, Tardif JC, Hammouche K, Meunier J. Automatic IVUS lumen segmentation using a 3D adaptive helix model. Comput Biol Med 2019; 107:58-72. [DOI: 10.1016/j.compbiomed.2019.01.023] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 01/23/2019] [Accepted: 01/24/2019] [Indexed: 10/27/2022]
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Moshfegh A, Javadzadegan A, Mohammadi M, Ravipudi L, Cheng S, Martins R. Development of an innovative technology to segment luminal borders of intravascular ultrasound image sequences in a fully automated manner. Comput Biol Med 2019; 108:111-121. [PMID: 31003174 DOI: 10.1016/j.compbiomed.2019.03.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 03/09/2019] [Accepted: 03/09/2019] [Indexed: 11/17/2022]
Abstract
Although intravascular ultrasound (IVUS) is the commonest intravascular imaging modality, it still is inefficient for clinical use as it requires laborious manual analysis. This study demonstrates the feasibility of a near real-time fully automated technology for accurate identification, detection, and quantification of luminal borders in intravascular images. This technology uses a combination of the novel approaches of a self-tuning engine, dynamic and static masking systems, radar-wise scan, and contour correction cycle method. The performance of the computer algorithm developed based on this technology was tested on a sequence of IVUS and True Vessel Characterization (TVC) images obtained from the left anterior descending (LAD) artery of 6 patients with coronary artery disease. The accuracy of the algorithm was evaluated by comparing luminal borders traced manually with those detected automatically. The processing time of the developed algorithm was also tested on a Dell laptop with an Intel Core i7-8750H Processor (4.1 GHz with 6 cores, 9 MB Cache). Linear regression and Bland-Altman analyses indicated high correlation between manual and automatic tracings (Y = 0.80 × X+1.70, R2 = 0.88 & 0.67 ± 1.31 (bias±SD)). Whereas analysis of 2000 IVUS images using one CPU core with a 30% load took 23.12 min, the same analysis using six CPU cores with 90% load took 1.0 min. The performance, accuracy, and speed of the presented state-of-the-art technology demonstrates its capacity for use in clinical settings.
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Affiliation(s)
- Abouzar Moshfegh
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia; ANZAC Research Institute, The University of Sydney, Sydney, NSW, 2139, Australia.
| | - Ashkan Javadzadegan
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia; ANZAC Research Institute, The University of Sydney, Sydney, NSW, 2139, Australia
| | - Maryam Mohammadi
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia
| | - Lakshitha Ravipudi
- School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW, 2006, Australia
| | - Shaokoon Cheng
- School of Engineering, Macquarie University, Sydney, NSW, 2109, Australia
| | - Ralph Martins
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, 2109, Australia; School of Exercise, Biomedical and Health Sciences, Edith Cowan University, Perth, Australia
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Wang YY, Qiu CH, Jiang J, Xia SR. Detecting the Media-adventitia Border in Intravascular Ultrasound Images through a Classification-based Approach. ULTRASONIC IMAGING 2019; 41:78-93. [PMID: 30556484 DOI: 10.1177/0161734618820112] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The detection of the media-adventitia (MA) border in intravascular ultrasound (IVUS) images is essential for vessel assessment and disease diagnosis. However, it remains a challenging task, considering the existence of plaque, calcification, and various artifacts. In this article, an effective method based on classification is proposed to extract the MA border in IVUS images. First, a novel morphologic feature describing the relative position of each structure relative to the MA border, called RPES for short, is proposed. Then, the RPES feature and other features are employed in a multiclass extreme learning machine (ELM) to classify IVUS images into nine classes including the MA border and other structures. At last, a modified snake model is employed to effectively detect the MA border in the rectangular domain, in which a modified external force field is constructed on the basis of local border appearances and classification results. The proposed method is evaluated on a public dataset with 77 IVUS images by three indicators in eight situations, such as calcification and a guide wire artifact. With the proposed RPES feature, detection performances are improved by more than 39 percent, which shows an apparent advantage in comparative experiments. Furthermore, compared with two other existing methods used on the same dataset, the proposed method achieves 18 of the best indicators among 24, demonstrating its higher capability in detecting the MA border.
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Affiliation(s)
- Yuan-Yuan Wang
- School of Information & Electrical Engineering, Zhejiang University City College, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Chen-Hui Qiu
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
| | - Jun Jiang
- Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shun-Ren Xia
- School of Information & Electrical Engineering, Zhejiang University City College, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
- Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China
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Wang YY, Peng WX, Qiu CH, Jiang J, Xia SR. Fractional-order Darwinian PSO-based feature selection for media-adventitia border detection in intravascular ultrasound images. ULTRASONICS 2019; 92:1-7. [PMID: 30205179 DOI: 10.1016/j.ultras.2018.06.012] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 05/25/2018] [Accepted: 06/16/2018] [Indexed: 06/08/2023]
Abstract
Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.
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Affiliation(s)
- Yuan-Yuan Wang
- Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China
| | - Wen-Xian Peng
- Radiology Department of Hangzhou Medical College, China
| | - Chen-Hui Qiu
- Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China
| | - Jun Jiang
- Second Affiliated Hospital, Zhejiang University School of Medicine, China
| | - Shun-Ren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education Zhejiang University, China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Zhejiang University, China.
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40
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Development of High-Frequency (>60 MHz) Intravascular Ultrasound (IVUS) Transducer by Using Asymmetric Electrodes for Improved Beam Profile. SENSORS 2018; 18:s18124414. [PMID: 30551639 PMCID: PMC6308511 DOI: 10.3390/s18124414] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/07/2018] [Accepted: 12/08/2018] [Indexed: 02/01/2023]
Abstract
In most commercial single-element intravascular ultrasound (IVUS) transducers, with 20 MHz to 40 MHz center frequencies, a conductive adhesive is used to bond a micro-sized cable for the signal line to the surface of the transducer aperture (<1 mm × 1 mm size) where ultrasound beam is generated. Therefore, the vibration of the piezoelectric layer is significantly disturbed by the adhesive with the signal line, thereby causing problems, such as reduced sensitivity, shortened penetration depth, and distorted beam profile. This phenomenon becomes more serious as the center frequency of the IVUS transducer is increased, and the aperture size becomes small. Therefore, we propose a novel IVUS acoustic stack employing asymmetric electrodes with conductive and non-conductive backing blocks. The purpose of this study is to verify the extent of performance degradation caused by the adhesive with the signal line, and to demonstrate how much performance degradation can be minimized by the proposed scheme. Finite element analysis (FEA) simulation was conducted, and the results show that −3 dB, −6 dB, and −10 dB penetration depths of the conventional transducer were shortened by 20%, 25%, and 19% respectively, while those of the proposed transducer were reduced only 3%, 4%, and 0% compared with their ideal transducers which have the same effective aperture size. Besides, the proposed transducer improved the −3 dB, −6 dB, and −10 dB penetration depths by 15%, 12%, and 10% respectively, compared with the conventional transducer. We also fabricated a 60 MHz IVUS transducer by using the proposed technique, and high-resolution IVUS B-mode (brightness mode) images were obtained. Thus, the proposed scheme can be one of the potential ways to provide more uniform beam profile resulting in improving the signal to noise ratio (SNR) in IVUS image.
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41
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Kermani A, Ayatollahi A. A new nonparametric statistical approach to detect lumen and Media-Adventitia borders in intravascular ultrasound frames. Comput Biol Med 2018; 104:10-28. [PMID: 30419417 DOI: 10.1016/j.compbiomed.2018.10.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 10/20/2018] [Accepted: 10/23/2018] [Indexed: 11/18/2022]
Abstract
Intravascular ultrasound (IVUS) imaging is widely known as a powerful interventional imaging modality for diagnosing atherosclerosis, and for treatment planning. In this regard, the detection of lumen and media-adventitia (MA) borders is considered to be a vital process. However, the manual detection of these two borders by the physician is cumbersome due to the large number of frames in a sequence. In addition, no approved universal automatic method has been presented so far due to the great diversity in the appearance of the coronary artery in the images acquired by different IVUS systems. To this end, the present study aimed to provide a new border search theory on the radial profile, based upon the nonparametric statistical approach, and to develop a generic and fully automatic three-step process for extracting the lumen and MA borders in IVUS frames based on the proposed theory. Thereafter, the proposed theory and three-step process were evaluated on synthetic images, as well as on a test set of standard publicly available images, respectively. The results showed that our three-step process could segment the borders with ≥0.82 and with ≥0.75 Jaccard measure (JM) to manual borders in IVUS frames acquired by the 20 MHz and 40 MHz probes, respectively. Based on the results, the lumen and MA borders can be extracted automatically, and the border extraction process can be implemented in parallel for a polar image due to the capability of the present proposed method to estimate the borders for each angle independently.
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Affiliation(s)
- Ali Kermani
- School of Electrical Engineering, Iran University of Science and Technology, Iran
| | - Ahmad Ayatollahi
- School of Electrical Engineering, Iran University of Science and Technology, Iran.
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Banchhor SK, Londhe ND, Araki T, Saba L, Radeva P, Khanna NN, Suri JS. Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. Comput Biol Med 2018; 101:184-198. [DOI: 10.1016/j.compbiomed.2018.08.017] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 08/14/2018] [Accepted: 08/14/2018] [Indexed: 01/04/2023]
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43
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Shi C, Luo X, Guo J, Najdovski Z, Fukuda T, Ren H. Three-Dimensional Intravascular Reconstruction Techniques Based on Intravascular Ultrasound: A Technical Review. IEEE J Biomed Health Inform 2018; 22:806-817. [DOI: 10.1109/jbhi.2017.2703903] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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44
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Faraji M, Cheng I, Naudin I, Basu A. Segmentation of arterial walls in intravascular ultrasound cross-sectional images using extremal region selection. ULTRASONICS 2018; 84:356-365. [PMID: 29241056 DOI: 10.1016/j.ultras.2017.11.020] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2017] [Revised: 11/30/2017] [Accepted: 11/30/2017] [Indexed: 06/07/2023]
Abstract
Intravascular Ultrasound (IVUS) is an intra-operative imaging modality that facilitates observing and appraising the vessel wall structure of the human coronary arteries. Segmentation of arterial wall boundaries from the IVUS images is not only crucial for quantitative analysis of the vessel walls and plaque characteristics, but is also necessary for generating 3D reconstructed models of the artery. The aim of this study is twofold. Firstly, we investigate the feasibility of using a recently proposed region detector, namely Extremal Region of Extremum Level (EREL) to delineate the luminal and media-adventitia borders in IVUS frames acquired by 20 MHz probes. Secondly, we propose a region selection strategy to label two ERELs as lumen and media based on the stability of their textural information. We extensively evaluated our selection strategy on the test set of a standard publicly available dataset containing 326 IVUS B-mode images. We showed that in the best case, the average Hausdorff Distances (HD) between the extracted ERELs and the actual lumen and media were 0.22 mm and 0.45 mm, respectively. The results of our experiments revealed that our selection strategy was able to segment the lumen with ⩽0.3 mm HD to the gold standard even though the images contained major artifacts such as bifurcations, shadows, and side branches. Moreover, when there was no artifact, our proposed method was able to delineate media-adventitia boundaries with 0.31 mm HD to the gold standard. Furthermore, our proposed segmentation method runs in time that is linear in the number of pixels in each frame. Based on the results of this work, by using a 20 MHz IVUS probe with controlled pullback, not only can we now analyze the internal structure of human arteries more accurately, but also segment each frame during the pullback procedure because of the low run time of our proposed segmentation method.
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Affiliation(s)
- Mehdi Faraji
- Department of Computing Science, University of Alberta, Canada.
| | - Irene Cheng
- Department of Computing Science, University of Alberta, Canada.
| | | | - Anup Basu
- Department of Computing Science, University of Alberta, Canada.
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Chen F, Ma R, Liu J, Zhu M, Liao H. Lumen and media-adventitia border detection in IVUS images using texture enhanced deformable model. Comput Med Imaging Graph 2018; 66:1-13. [PMID: 29481899 DOI: 10.1016/j.compmedimag.2018.02.003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Revised: 01/30/2018] [Accepted: 02/12/2018] [Indexed: 10/18/2022]
Abstract
Lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images are critical for assessing the dimensions of vascular structures and providing plaque information in the diagnosis and navigation of vascular interventions. However, manual delineation of the lumen and MA borders is an intricate and time-consuming process. In this paper, a texture-enhanced deformable model (TEDM) is proposed to accurately detect these borders by incorporating texture information with the morphological factors of deformable model. An ensemble support vector machine classifier is used to classify IVUS pixels presented by texture features into different tissue types. The image regionalization maps of different tissue types are further used for texture enhancement modules in the TEDM. The proposed TEDM method has been tested on 1500 images from 15 clinical IVUS datasets by comparing with the manual delineations. Evaluation results demonstrate that our method can accurately detect lumen and MA surfaces with small surface distance errors of 0.17 and 0.19 mm, respectively. Accurate segmentation results provide 2D measurements of MA/lumen areas and 3D vessel visualizations for vascular interventions.
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Affiliation(s)
- Fang Chen
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Ruibin Ma
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | - Jia Liu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Mingyu Zhu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Hongen Liao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
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46
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Meiburger KM, Acharya UR, Molinari F. Automated localization and segmentation techniques for B-mode ultrasound images: A review. Comput Biol Med 2017; 92:210-235. [PMID: 29247890 DOI: 10.1016/j.compbiomed.2017.11.018] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Revised: 11/30/2017] [Accepted: 11/30/2017] [Indexed: 12/14/2022]
Abstract
B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed.
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Affiliation(s)
- Kristen M Meiburger
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy
| | - U Rajendra Acharya
- Department of Electronic & Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Torino, Italy.
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Banchhor SK, Londhe ND, Araki T, Saba L, Radeva P, Laird JR, Suri JS. Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm. Comput Biol Med 2017; 91:198-212. [PMID: 29100114 DOI: 10.1016/j.compbiomed.2017.10.019] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 10/19/2017] [Accepted: 10/19/2017] [Indexed: 12/31/2022]
Abstract
BACKGROUND Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk. METHOD This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases. RESULTS The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively. CONCLUSIONS The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features.
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Affiliation(s)
| | | | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Petia Radeva
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | | | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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48
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Li Z, Zhang X, Müller H, Zhang S. Large-scale retrieval for medical image analytics: A comprehensive review. Med Image Anal 2017; 43:66-84. [PMID: 29031831 DOI: 10.1016/j.media.2017.09.007] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 08/01/2017] [Accepted: 09/29/2017] [Indexed: 12/27/2022]
Abstract
Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis.
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Affiliation(s)
- Zhongyu Li
- Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Xiaofan Zhang
- Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA
| | - Henning Müller
- Information Systems Institute, HES-SO Valais, Sierre, Switzerland
| | - Shaoting Zhang
- Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
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49
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Jodas DS, Pereira AS, Tavares JMRS. Automatic segmentation of the lumen region in intravascular images of the coronary artery. Med Image Anal 2017. [PMID: 28624754 DOI: 10.1016/j.media.2017.06.006] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 ± 0.06, 0.29 ± 0.17 mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction.
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Affiliation(s)
- Danilo Samuel Jodas
- CAPES Foundation, Ministry of Education of Brazil, Brasília - DF, 70040-020, Brazil; Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
| | - Aledir Silveira Pereira
- Universidade Estadual Paulista "Júlio de Mesquita Filho", Rua Cristóvão Colombo, 2265, 15054-000, S. J. do Rio Preto, Brazil.
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal. http://www.fe.up.pt/~tavares
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50
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Abstract
Due to its real-time, non-radiation based three-dimensional (3D) imaging capabilities, ultrasound (US) has been incorporated into various orthopedic procedures. However, imaging artifacts, low signal-to-noise ratio (SNR) and bone boundaries appearing several mm in thickness make the analysis of US data difficult. This paper provides a review about the state-of-the-art bone segmentation and enhancement methods developed for two-dimensional (2D) and 3D US data. First, an overview for the appearance of bone surface response in B-mode data is presented. Then, classification of the proposed techniques in terms of the image information being used is provided. Specifically, the focus is given on segmentation and enhancement of B-mode US data. The review is concluded by discussing future directions of research and additional challenges which need to be overcome in order to make this imaging modality more successful in orthopedics.
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Affiliation(s)
- Ilker Hacihaliloglu
- Department of Biomedical Engineering, Rutgers University, NJ, USA
- Department of Radiology, Rutgers University Robert Wood Johnson Medical School, NJ, USA
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