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Zhang Y, Yu M, Tong C, Zhao Y, Han J. CA-UNet Segmentation Makes a Good Ischemic Stroke Risk Prediction. Interdiscip Sci 2024; 16:58-72. [PMID: 37626263 DOI: 10.1007/s12539-023-00583-x] [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: 04/10/2023] [Revised: 07/13/2023] [Accepted: 07/19/2023] [Indexed: 08/27/2023]
Abstract
Stroke is still the World's second major factor of death, as well as the third major factor of death and disability. Ischemic stroke is a type of stroke, in which early detection and treatment are the keys to preventing ischemic strokes. However, due to the limitation of privacy protection and labeling difficulties, there are only a few studies on the intelligent automatic diagnosis of stroke or ischemic stroke, and the results are unsatisfactory. Therefore, we collect some data and propose a 3D carotid Computed Tomography Angiography (CTA) image segmentation model called CA-UNet for fully automated extraction of carotid arteries. We explore the number of down-sampling times applicable to carotid segmentation and design a multi-scale loss function to resolve the loss of detailed features during the process of down-sampling. Moreover, based on CA-Unet, we propose an ischemic stroke risk prediction model to predict the risk in patients using their 3D CTA images, electronic medical records, and medical history. We have validated the efficacy of our segmentation model and prediction model through comparison tests. Our method can provide reliable diagnoses and results that benefit patients and medical professionals.
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Affiliation(s)
- Yuqi Zhang
- School of Computer Science and Engineering, Beihang University, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Mengbo Yu
- School of Computer Science and Engineering, Beihang University, Beijing, China
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
| | - Chao Tong
- School of Computer Science and Engineering, Beihang University, Beijing, China.
- State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China.
| | - Yanqing Zhao
- Department of Interventional Radiology and Vascular Surgery, Peking University Third Hospital, Beijing, China
| | - Jintao Han
- Department of Interventional Radiology and Vascular Surgery, Peking University Third Hospital, Beijing, China
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2
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Wu L, Huang W, Liu X, Yang B. Investigation of the clinical effects of acipimox in patients with vulnerable carotid atherosclerosis. Vascular 2023; 31:1201-1208. [PMID: 35857037 DOI: 10.1177/17085381221112551] [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] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To investigate the clinical effects of acipimox in patients with vulnerable carotid atherosclerosis. METHODS 80 patients with vulnerable carotid atherosclerosis who were admitted to the Department of Cardiology in Wuxi Second People's Hospital between February 2020 and October 2021 were enrolled in this study. All of these patients were randomly divided into an observation group (n = 40), who were given acipimox and conventional treatment, and a control group (n = 40), who were given conventional treatment. The levels of blood lipids and adiponectin (APN), the carotid intima-media thickness (IMT), the area, thickness and number of CAS, peak systolic velocities (PSV) and end-diastolic blood velocity (EDV) of common carotid artery (CCA), and the level of inflammatory markers were measured and compared between the two groups pretherapy and posttreatment. Then, the adverse events were collected and compared between the two groups posttreatment. RESULTS The demographics and basic clinical characteristics were not significantly different between the two groups. At posttreatment, the levels of TC, LDL-C, ANP, IL-6, TNF-α and hs-CRP in the observation group were significantly lower than those in the control group at posttreatment. Moreover, the IMT and the area and thickness of CAS in the observation group were significantly lower than those in the control group. After treatment, PSV was lower and EDV was higher in two groups than before treatment; after treatment, compared with control group, PSV in observation group was lower, while EDV was higher. Most importantly, the rate of adverse events was similar in the two groups. CONCLUSIONS Acipimox reduced the blood lipid levels in patients with vulnerable carotid atherosclerosis. It also stabilized vulnerable plaques and reduced CAS.
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Affiliation(s)
- Lin Wu
- Department of Pharmacy, The Affiliated Wuxi NO. 2 People's Hospital of Nanjing Medical University, Wuxi, China
| | - Weiyi Huang
- Department of Neurosurgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi, China
| | - Xiaoxiao Liu
- Department of Cardiology, The Affiliated Wuxi NO. 2 People's Hospital of Nanjing Medical University, Wuxi, China
| | - Baochun Yang
- Department of Pharmacy, The Affiliated Wuxi NO. 2 People's Hospital of Nanjing Medical University, Wuxi, China
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Zhou T, Tan T, Pan X, Tang H, Li J. Fully automatic deep learning trained on limited data for carotid artery segmentation from large image volumes. Quant Imaging Med Surg 2021; 11:67-83. [PMID: 33392012 PMCID: PMC7719941 DOI: 10.21037/qims-20-286] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Accepted: 07/21/2020] [Indexed: 11/06/2022]
Abstract
BACKGROUND The objectives of this study were to develop a 3D convolutional deep learning framework (CarotidNet) for fully automatic segmentation of carotid bifurcations in computed tomography angiography (CTA) images and to facilitate the quantification of carotid stenosis and risk assessment of stroke. METHODS Our pipeline was a two-stage cascade network that included a localization phase and a segmentation phase. The network framework was based on the 3D version of U-Net, but was refined in three ways: (I) by adding residual connections and a deep supervision strategy to cope with the vanishing problem in back-propagation; (II) by adopting dilated convolution in order to strengthen the capacity to capture contextual information; and (III) by establishing a hybrid objective function to address the extreme imbalance between foreground and background voxels. RESULTS We trained our networks on 15 cases and evaluated their performance based on 41 cases from the MICCAI Challenge 2009 dataset. A Dice similarity coefficient of 82.3% was achieved for the test cases. CONCLUSIONS We developed a carotid segmentation method based on U-Net that can segment tiny carotid bifurcation lumens from very large backgrounds with no manual intervention. This was the first attempt to use deep learning to achieve carotid bifurcation segmentation in 3D CTA images. Our results indicate that deep learning is a promising method for automatically extracting carotid bifurcation lumens.
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Affiliation(s)
- Tianshu Zhou
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Tao Tan
- Department of Mathematics and Computer Science, Eindhoven University of Technology and Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Xiaoyan Pan
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Hui Tang
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Jingsong Li
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
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Zhu G, Hom J, Li Y, Jiang B, Rodriguez F, Fleischmann D, Saloner D, Porcu M, Zhang Y, Saba L, Wintermark M. Carotid plaque imaging and the risk of atherosclerotic cardiovascular disease. Cardiovasc Diagn Ther 2020; 10:1048-1067. [PMID: 32968660 DOI: 10.21037/cdt.2020.03.10] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Carotid artery plaque is a measure of atherosclerosis and is associated with future risk of atherosclerotic cardiovascular disease (ASCVD), which encompasses coronary, cerebrovascular, and peripheral arterial diseases. With advanced imaging techniques, computerized tomography (CT) and magnetic resonance imaging (MRI) have shown their potential superiority to routine ultrasound to detect features of carotid plaque vulnerability, such as intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC), and calcification. The correlation between imaging features and histological changes of carotid plaques has been investigated. Imaging of carotid features has been used to predict the risk of cardiovascular events. Other techniques such as nuclear imaging and intra-vascular ultrasound (IVUS) have also been proposed to better understand the vulnerable carotid plaque features. In this article, we review the studies of imaging specific carotid plaque components and their correlation with risk scores.
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Affiliation(s)
- Guangming Zhu
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jason Hom
- Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Ying Li
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA.,Clinical Medical Research Center, Luye Pharma Group Ltd., Beijing 100000, China
| | - Bin Jiang
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Fatima Rodriguez
- Division of Cardiovascular Medicine and the Cardiovascular Institute, Stanford University, Palo Alto, CA, USA
| | - Dominik Fleischmann
- Department of Radiology, Cardiovascular Imaging Section, Stanford University School of Medicine, Palo Alto, CA, USA
| | - David Saloner
- Department of Radiology, University of California San Francisco, San Francisco, CA, USA
| | - Michele Porcu
- Dipartimento di Radiologia, Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italy
| | - Yanrong Zhang
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Luca Saba
- Dipartimento di Radiologia, Azienda Ospedaliero Universitaria di Cagliari, Cagliari, Italy
| | - Max Wintermark
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Palo Alto, CA, USA
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3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review. J Digit Imaging 2019; 31:799-850. [PMID: 29915942 DOI: 10.1007/s10278-018-0101-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.
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Semiautomated Characterization of Carotid Artery Plaque Features From Computed Tomography Angiography to Predict Atherosclerotic Cardiovascular Disease Risk Score. J Comput Assist Tomogr 2019; 43:452-459. [PMID: 31082951 DOI: 10.1097/rct.0000000000000862] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE To investigate whether selected carotid computed tomography angiography (CTA) quantitative features can predict 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores. METHODS One hundred seventeen patients with calculated ASCVD risk scores were considered. A semiautomated imaging analysis software was used to segment and quantify plaque features. Eighty patients were randomly selected to build models using 14 imaging variables and the calculated ASCVD risk score as the end point (continuous and binarized). The remaining 37 patients were used as the test set to generate predicted ASCVD scores. The predicted and observed ASCVD risk scores were compared to assess properties of the predictive model. RESULTS Nine of 14 CTA imaging variables were included in a model that considered the plaque features in a continuous fashion (model 1) and 6 in a model that considered the plaque features dichotomized (model 2). The predicted ASCVD risk scores were 18.87% ± 13.26% and 18.39% ± 11.6%, respectively. There were strong correlations between the observed ASCVD and the predicted ASCVDs, with r = 0.736 for model 1 and r = 0.657 for model 2. The mean biases between observed ASCVD and predicted ASCVDs were -1.954% ± 10.88% and -1.466% ± 12.04%, respectively. CONCLUSIONS Selected quantitative imaging carotid features extracted from the semiautomated carotid artery analysis can predict the ASCVD risk scores.
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3D Shape-Weighted Level Set Method for Breast MRI 3D Tumor Segmentation. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:7097498. [PMID: 30008992 PMCID: PMC6020458 DOI: 10.1155/2018/7097498] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Accepted: 04/30/2018] [Indexed: 11/23/2022]
Abstract
Three-dimensional (3D) medical image segmentation is used to segment the target (a lesion or an organ) in 3D medical images. Through this process, 3D target information is obtained; hence, this technology is an important auxiliary tool for medical diagnosis. Although some methods have proved to be successful for two-dimensional (2D) image segmentation, their direct use in the 3D case has been unsatisfactory. To obtain more precise tumor segmentation results from 3D MR images, in this paper, we propose a method known as the 3D shape-weighted level set method (3D-SLSM). The proposed method first converts the LSM, which is superior with respect to 2D image segmentation, into a 3D algorithm that is suitable for overall calculations in 3D image models, and which improves the efficiency and accuracy of calculations. A 3D shape-weighted value is then added for each 3D-SLSM iterative process according to the changes in volume. Besides increasing the convergence rate and eliminating background noise, this shape-weighted value also brings the segmented contour closer to the actual tumor margins. To perform a quantitative analysis of 3D-SLSM and to examine its feasibility in clinical applications, we have divided our experiments into computer-simulated sequence images and actual breast MRI cases. Subsequently, we simultaneously compared various existing 3D segmentation methods. The experimental results demonstrated that 3D-SLSM exhibited precise segmentation results for both types of experimental images. In addition, 3D-SLSM showed better results for quantitative data compared with existing 3D segmentation methods.
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Hemmati HR, Alizadeh M, Kamali-Asl A, Shirani S. Semi-automated carotid lumen segmentation in computed tomography angiography images. J Biomed Res 2017; 31:548. [PMID: 29109328 PMCID: PMC6307665 DOI: 10.7555/jbr.31.20160107] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 03/30/2017] [Indexed: 11/25/2022] Open
Abstract
Carotid artery stenosis causes narrowing of carotid lumens and may lead to brain infarction. The purpose of this study was to develop a semi-automated method of segmenting vessel walls, surrounding tissues, and more importantly, the carotid artery lumen by contrast computed tomography angiography (CTA) images and to define the severity of stenosis and present a three-dimensional model of the carotid for visual inspection. In vivo contrast CTA images of 14 patients (7 normal subjects and 7 patients undergoing endarterectomy) were analyzed using a multi-step segmentation algorithm. This method uses graph cut followed by watershed and Hessian based shortest path method in order to extract lumen boundary correctly without being corrupted in the presence of surrounding tissues. Quantitative measurements of the proposed method were compared with those of manual delineation by independent board-certified radiologists. The results were quantitatively evaluated using spatial overlap surface distance indices. A slightly strong match was shown in terms of dice similarity coefficient (DSC) = 0.87±0.08; mean surface distance (Dmsd) = 0.32±0.32; root mean squared surface distance (Drmssd) = 0.49±0.54 and maximum surface distance (Dmax) = 2.14±2.08 between manual and automated segmentation of common, internal and external carotid arteries, carotid bifurcation and stenotic artery, respectively. Quantitative measurements showed that the proposed method has high potential to segment the carotid lumen and is robust to the changes of the lumen diameter and the shape of the stenosis area at the bifurcation site. The proposed method for CTA images provides a fast and reliable tool to quantify the severity of carotid artery stenosis.
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Affiliation(s)
- Hamid Reza Hemmati
- . Radiation Medicine Engineering Department, Shahid Beheshti University, Tehran 1983963113, Iran
| | - Mahdi Alizadeh
- . Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, PA19107, USA
- . Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA 19107, USA
| | - Alireza Kamali-Asl
- . Radiation Medicine Engineering Department, Shahid Beheshti University, Tehran 1983963113, Iran
| | - Shapour Shirani
- . Department of Imaging, Tehran University of Medical Science, Tehran 1983963113, Iran
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Zeng YZ, Zhao YQ, Tang P, Liao M, Liang YX, Liao SH, Zou BJ. Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 150:31-39. [PMID: 28859828 DOI: 10.1016/j.cmpb.2017.07.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 06/26/2017] [Accepted: 07/18/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method. METHODS Firstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein. RESULTS The experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction. CONCLUSIONS The proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein.
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Affiliation(s)
- Ye-Zhan Zeng
- School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China
| | - Yu-Qian Zhao
- School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China.
| | - Ping Tang
- School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China
| | - Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Yi-Xiong Liang
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Sheng-Hui Liao
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Bei-Ji Zou
- School of Information Science and Engineering, Central South University, Changsha 410083, China
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Computer aided diagnosis of Coronary Artery Disease, Myocardial Infarction and carotid atherosclerosis using ultrasound images: A review. Phys Med 2017; 33:1-15. [DOI: 10.1016/j.ejmp.2016.12.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 11/21/2016] [Accepted: 12/04/2016] [Indexed: 02/08/2023] Open
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Liao M, Zhao YQ, Wang W, Zeng YZ, Yang Q, Shih FY, Zou BJ. Efficient liver segmentation in CT images based on graph cuts and bottleneck detection. Phys Med 2016; 32:1383-1396. [PMID: 27771278 DOI: 10.1016/j.ejmp.2016.10.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 10/05/2016] [Accepted: 10/05/2016] [Indexed: 12/20/2022] Open
Abstract
Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes. An intensity model is combined with a principal component analysis (PCA) based appearance model to exclude complex background and highlight liver region. They are then integrated with location information from neighboring slices into graph cuts to segment the liver in each slice automatically. Finally, a boundary refinement method based on bottleneck detection is used to increase the segmentation accuracy. Our method does not require heavy training process or statistical model construction, and is capable of dealing with complicated shape and intensity variations. We apply the proposed method on XHCSU14 and SLIVER07 databases, and evaluate it by MICCAI criteria and Dice similarity coefficient. Experimental results show our method outperforms several existing methods on liver segmentation.
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Affiliation(s)
- Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Yu-Qian Zhao
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Wei Wang
- The Third Xiangya Hospital, Central South University, Changsha 410083, China.
| | - Ye-Zhan Zeng
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Qing Yang
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Frank Y Shih
- College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Bei-Ji Zou
- School of Information Science and Engineering, Central South University, Changsha 410083, China
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12
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Liver vessel segmentation based on extreme learning machine. Phys Med 2016; 32:709-16. [PMID: 27132031 DOI: 10.1016/j.ejmp.2016.04.003] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2015] [Revised: 02/06/2016] [Accepted: 04/08/2016] [Indexed: 01/15/2023] Open
Abstract
Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.
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