1
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Wu J, Xin J, Yang X, Matkovic LA, Zhao X, Zheng N, Li R. Segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black blood magnetic resonance imaging with multi-task learning. Med Phys 2024; 51:1775-1797. [PMID: 37681965 DOI: 10.1002/mp.16728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 07/04/2023] [Accepted: 07/29/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND Atherosclerotic cardiovascular disease is the leading cause of death worldwide. Early detection of carotid atherosclerosis can prevent the progression of cardiovascular disease. Many (semi-) automatic methods have been designed for the segmentation of carotid vessel wall and the diagnosis of carotid atherosclerosis (i.e., the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis) on black blood magnetic resonance imaging (BB-MRI). However, most of these methods ignore the intrinsic correlation among different tasks on BB-MRI, leading to limited performance. PURPOSE Thus, we model the intrinsic correlation among the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis tasks on BB-MRI by using the multi-task learning technique and propose a gated multi-task network (GMT-Net) to perform three related tasks in a neural network (i.e., carotid artery lumen segmentation, outer wall segmentation, and carotid atherosclerosis diagnosis). METHODS In the proposed method, the GMT-Net is composed of three modules, including the sharing module, the segmentation module, and the diagnosis module, which interact with each other to achieve better learning performance. At the same time, two new adaptive layers, namely, the gated exchange layer and the gated fusion layer, are presented to exchange and merge branch features. RESULTS The proposed method is applied to the CAREII dataset (i.e., 1057 scans) for the lumen segmentation, the outer wall segmentation, and the carotid atherosclerosis diagnosis. The proposed method can achieve promising segmentation performances (0.9677 Dice for the lumen and 0.9669 Dice for the outer wall) and better diagnosis accuracy of carotid atherosclerosis (0.9516 AUC and 0.9024 Accuracy) in the "CAREII test" dataset (i.e., 106 scans). The results show that the proposed method has statistically significant accuracy and efficiency. CONCLUSIONS Even without the intervention of reviewers required for the previous works, the proposed method automatically segments the lumen and outer wall together and diagnoses carotid atherosclerosis with high performance. The proposed method can be used in clinical trials to help radiologists get rid of tedious reading tasks, such as screening review to separate normal carotid arteries from atherosclerotic arteries and to outline vessel wall contours.
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Affiliation(s)
- Jiayi Wu
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University
| | - Jingmin Xin
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University
| | - Xiaofeng Yang
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Luke A Matkovic
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia, USA
| | - Xihai Zhao
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Nanning Zheng
- National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University
| | - Rui Li
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
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2
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Huang X, Wang J, Li Z. 3D carotid artery segmentation using shape-constrained active contours. Comput Biol Med 2023; 153:106530. [PMID: 36610215 DOI: 10.1016/j.compbiomed.2022.106530] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 12/12/2022] [Accepted: 12/31/2022] [Indexed: 01/04/2023]
Abstract
Reconstruction of the carotid artery is demanded in the detection and characterization of atherosclerosis. This study proposes a shape-constrained active contour model for segmenting the carotid artery from MR images, which embeds the output of the deep learning network into the active contour. First the centerline of the carotid artery is localized and then modified active contour initialized from the centerline is used to extract the vessel lumen, finally the probability atlas generated by the deep learning network in polar representation domain is integrated into the active contour as a prior information to detect the outer wall. The results showed that the proposed active contour model was efficient and comparable to manual segmentation.
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Affiliation(s)
- Xianjue Huang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jun Wang
- First Affiliated Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Zhiyong Li
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China; School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, 4000, Australia; Faculty of Sports Science, Ningbo University, Ningbo, 315211, China.
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3
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Zhu C, Wang X, Chen S, Teng Z, Bai C, Huang X, Xia M, Shao Z, Gu Z, Sun P. Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning. Med Biol Eng Comput 2022; 60:2693-2706. [DOI: 10.1007/s11517-022-02622-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 04/28/2022] [Indexed: 11/30/2022]
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4
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Zhu C, Wang X, Teng Z, Chen S, Huang X, Xia M, Mao L, Bai C. Cascaded residual U-net for fully automatic segmentation of 3D carotid artery in high-resolution multi-contrast MR images. Phys Med Biol 2021; 66:045033. [PMID: 33333499 DOI: 10.1088/1361-6560/abd4bb] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Accurate and automatic carotid artery segmentation for magnetic resonance (MR) images is eagerly expected, which can greatly assist a comprehensive study of atherosclerosis and accelerate the translation. Although many efforts have been made, identification of the inner lumen and outer wall in diseased vessels is still a challenging task due to complex vascular deformation, blurred wall boundary, and confusing componential expression. In this paper, we introduce a novel fully automatic 3D framework for simultaneously segmenting the carotid artery from high-resolution multi-contrast MR sequences based on deep learning. First, an optimal channel fitting structure is designed for identity mapping, and a novel 3D residual U-net is used as a basic network. Second, high-resolution MR images are trained using both patch-level and global-level strategies, and the two pre-segmentation results are optimized based on structural characteristics. Third, the optimized pre-segmentation results are cascaded with the patch-cropped MR volume data and trained to segment the carotid lumen and wall. Extensive experiments demonstrate the proposed method outperforms the state-of-the-art 3D Unet-based segmentation models.
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Affiliation(s)
- Chenglu Zhu
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
| | - Xiaoyan Wang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
| | - Zhongzhao Teng
- University Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom
| | - Shengyong Chen
- Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, People's Republic of China
| | - Xiaojie Huang
- The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, People's Republic of China
| | - Ming Xia
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
| | - Lizhao Mao
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
| | - Cong Bai
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023 People's Republic of China
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5
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Murgia A, Erta M, Suri JS, Gupta A, Wintermark M, Saba L. CT imaging features of carotid artery plaque vulnerability. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1261. [PMID: 33178793 PMCID: PMC7607080 DOI: 10.21037/atm-2020-cass-13] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Despite steady advances in medical care, cardiovascular disease remains one of the main causes of death and long-term morbidity worldwide. Up to 30% of strokes are associated with the presence of carotid atherosclerotic plaques. While the degree of stenosis has long been recognized as the main guiding factor in risk stratification and therapeutical decisions, recent evidence suggests that features of unstable, or ‘vulnerable’, plaques offer better prognostication capabilities. This paradigmatic shift has motivated researchers to explore the potentialities of non-invasive diagnostic tools to image not only the lumen, but also the vascular wall and the structural characteristics of the plaque. The present review will offer a panoramic on the imaging modalities currently available to characterize carotid atherosclerotic plaques and, in particular, it will focus on the increasingly important role covered by multidetector computed tomographic angiography.
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Affiliation(s)
- Alessandro Murgia
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), Italy
| | - Marco Erta
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnosis Division, AtheroPoint(tm), Roseville, CA, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell University, New York, NY, USA
| | - Max Wintermark
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato (Cagliari), Italy
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6
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An Overview of Segmentation Algorithms for the Analysis of Anomalies on Medical Images. JOURNAL OF INTELLIGENT SYSTEMS 2018. [DOI: 10.1515/jisys-2017-0629] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Abstract
Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation are also discussed. The comparative analysis of segmentation algorithms is done based on characteristics like spatial consideration, region continuity, computation complexity, selection of parameters, noise immunity, accuracy, and computation time. Finally, in this work, some of the typical segmentation algorithms are implemented on real-time datasets using Matlab 2010 software, and the outcome of this work will be an aid for the researchers in medical image processing.
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7
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Arias-Lorza AM, Bos D, van der Lugt A, de Bruijne M. Cooperative carotid artery centerline extraction in MRI. PLoS One 2018; 13:e0197180. [PMID: 29847545 PMCID: PMC5976187 DOI: 10.1371/journal.pone.0197180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 04/27/2018] [Indexed: 12/01/2022] Open
Abstract
Centerline extraction of the carotid artery in MRI is important to analyze the artery geometry and to provide input for further processing such as registration and segmentation. The centerline of the artery bifurcation is often extracted by means of two independent minimum cost paths ranging from the common to the internal and the external carotid artery. Often the cost is not well defined at the artery bifurcation, leading to centerline errors. To solve this problem, we developed a method to cooperatively extract both centerlines, where in the cost to extract each centerline, we integrate a constraint region derived from the estimated position of the neighbor centerline. This method avoids that both centerlines follow the same cheapest path after the bifurcation, which is a common error when the paths are extracted independently. We show that this method results in less error compared to extracting them independently: 10 failed centerlines Vs. 3 failures in a data set of 161 arteries with manual annotations. Additionally, we show that the new method improves the non-cooperative approach in 28 cases (p < 0.0001) in a data set of 3,904 arteries.
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Affiliation(s)
- Andrés M. Arias-Lorza
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- * E-mail:
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam, The Netherlands
- Image Section, Department of Computer Science, University of Copenhagen, Denmark
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8
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Pérez-Carrasco JA, Acha B, Suárez-Mejías C, López-Guerra JL, Serrano C. Joint segmentation of bones and muscles using an intensity and histogram-based energy minimization approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 156:85-95. [PMID: 29428079 DOI: 10.1016/j.cmpb.2017.12.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Revised: 11/11/2017] [Accepted: 12/22/2017] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVES The segmentation of muscle and bone structures in CT is of interest to physicians and surgeons for surgical planning, disease diagnosis and/or the analysis of fractures or bone/muscle densities. Recently, the issue has been addressed in many research works. However, most studies have focused on only one of the two tissues and on the segmentation of one particular bone or muscle. This work addresses the segmentation of muscle and bone structures in 3D CT volumes. METHODS The proposed bone and muscle segmentation algorithm is based on a three-label convex relaxation approach. The main novelty is that the proposed energy function to be minimized includes distance to histogram models of bone and muscle structures combined with gray-level information. RESULTS 27 CT volumes corresponding to different sections from 20 different patients were manually segmented and used as ground-truth for training and evaluation purposes. Different metrics (Dice index, Jaccard index, Sensitivity, Specificity, Positive Predictive Value, accuracy and computational cost) were computed and compared with those used in some state-of-the art algorithms. The proposed algorithm outperformed the other methods, obtaining a Dice coefficient of 0.88 ± 0.14, a Jaccard index of 0.80 ± 0.19, a Sensitivity of 0.94 ± 0.15 and a Specificity of 0.95 ± 0.04 for bone segmentation, and 0.78 ± 0.12, 0.65 ± 0.16, 0.94 ± 0.04 and 0.95 ± 0.04 for muscle tissue. CONCLUSIONS A fast, generalized method has been presented for segmenting muscle and bone structures in 3D CT volumes using a multilabel continuous convex relaxation approach. The results obtained show that the proposed algorithm outperforms some state-of-the art methods. The algorithm will help physicians and surgeons in surgical planning, disease diagnosis and/or the analysis of fractures or bone/muscle densities.
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Affiliation(s)
| | - Begoña Acha
- Signal and Communications Theory Department, University of Seville, Seville, Spain
| | - Cristina Suárez-Mejías
- Signal and Communications Theory Department, University of Seville, Seville, Spain; Technological Innovation Group, Virgen del Rocío University Hospital, Seville, Spain
| | | | - Carmen Serrano
- Signal and Communications Theory Department, University of Seville, Seville, Spain
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9
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Arias Lorza AM, van Engelen A, Petersen J, van der Lugt A, de Bruijne M. Maximization of regional probabilities using Optimal Surface Graphs: Application to carotid artery segmentation in MRI. Med Phys 2018; 45:1159-1169. [DOI: 10.1002/mp.12771] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Revised: 11/21/2017] [Accepted: 12/26/2017] [Indexed: 11/10/2022] Open
Affiliation(s)
- Andres M. Arias Lorza
- Biomedical Imaging Group Rotterdam; Departments of Radiology and Medical Informatics; Erasmus MC; Rotterdam The Netherlands
| | - Arna van Engelen
- Biomedical Imaging Group Rotterdam; Departments of Radiology and Medical Informatics; Erasmus MC; Rotterdam The Netherlands
| | - Jens Petersen
- Department of Computer Science; University of Copenhagen; Copenhagen Denmark
| | | | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam; Departments of Radiology and Medical Informatics; Erasmus MC; Rotterdam The Netherlands
- Department of Computer Science; University of Copenhagen; Copenhagen Denmark
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10
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Saba L, Yuan C, Hatsukami TS, Balu N, Qiao Y, DeMarco JK, Saam T, Moody AR, Li D, Matouk CC, Johnson MH, Jäger HR, Mossa-Basha M, Kooi ME, Fan Z, Saloner D, Wintermark M, Mikulis DJ, Wasserman BA. Carotid Artery Wall Imaging: Perspective and Guidelines from the ASNR Vessel Wall Imaging Study Group and Expert Consensus Recommendations of the American Society of Neuroradiology. AJNR Am J Neuroradiol 2018; 39:E9-E31. [PMID: 29326139 DOI: 10.3174/ajnr.a5488] [Citation(s) in RCA: 176] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Identification of carotid artery atherosclerosis is conventionally based on measurements of luminal stenosis and surface irregularities using in vivo imaging techniques including sonography, CT and MR angiography, and digital subtraction angiography. However, histopathologic studies demonstrate considerable differences between plaques with identical degrees of stenosis and indicate that certain plaque features are associated with increased risk for ischemic events. The ability to look beyond the lumen using highly developed vessel wall imaging methods to identify plaque vulnerable to disruption has prompted an active debate as to whether a paradigm shift is needed to move away from relying on measurements of luminal stenosis for gauging the risk of ischemic injury. Further evaluation in randomized clinical trials will help to better define the exact role of plaque imaging in clinical decision-making. However, current carotid vessel wall imaging techniques can be informative. The goal of this article is to present the perspective of the ASNR Vessel Wall Imaging Study Group as it relates to the current status of arterial wall imaging in carotid artery disease.
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Affiliation(s)
- L Saba
- From the Department of Medical Imaging (L.S.), University of Cagliari, Cagliari, Italy
| | - C Yuan
- Departments of Radiology (C.Y., N.B., M.M.-B.)
| | - T S Hatsukami
- Surgery (T.S.H.), University of Washington, Seattle, Washington
| | - N Balu
- Departments of Radiology (C.Y., N.B., M.M.-B.)
| | - Y Qiao
- The Russell H. Morgan Department of Radiology and Radiological Sciences (Y.Q., B.A.W.), Johns Hopkins Hospital, Baltimore, Maryland
| | - J K DeMarco
- Department of Radiology (J.K.D.), Walter Reed National Military Medical Center, Bethesda, Maryland
| | - T Saam
- Department of Radiology (T.S.), Ludwig-Maximilian University Hospital, Munich, Germany
| | - A R Moody
- Department of Medical Imaging (A.R.M.), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - D Li
- Biomedical Imaging Research Institute (D.L., Z.F.), Cedars-Sinai Medical Center, Los Angeles, California
| | - C C Matouk
- Departments of Neurosurgery, Neurovascular and Stroke Programs (C.C.M., M.H.J.).,Radiology and Biomedical Imaging (C.C.M., M.H.J.)
| | - M H Johnson
- Departments of Neurosurgery, Neurovascular and Stroke Programs (C.C.M., M.H.J.).,Radiology and Biomedical Imaging (C.C.M., M.H.J.).,Surgery (M.H.J.), Yale University School of Medicine, New Haven, Connecticut
| | - H R Jäger
- Neuroradiological Academic Unit (H.R.J.), Department of Brain Repair and Rehabilitation, University College London Institute of Neurology, London, UK
| | | | - M E Kooi
- Department of Radiology (M.E.K.), CARIM School for Cardiovascular Diseases, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Z Fan
- Biomedical Imaging Research Institute (D.L., Z.F.), Cedars-Sinai Medical Center, Los Angeles, California
| | - D Saloner
- Department of Radiology and Biomedical Imaging (D.S.), University of California, San Francisco, California
| | - M Wintermark
- Department of Radiology (M.W.), Neuroradiology Division, Stanford University, Stanford, California
| | - D J Mikulis
- Division of Neuroradiology (D.J.M.), Department of Medical Imaging, University Health Network
| | - B A Wasserman
- The Russell H. Morgan Department of Radiology and Radiological Sciences (Y.Q., B.A.W.), Johns Hopkins Hospital, Baltimore, Maryland
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11
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Gao S, van 't Klooster R, Kitslaar PH, Coolen BF, van den Berg AM, Smits LP, Shahzad R, Shamonin DP, de Koning PJH, Nederveen AJ, van der Geest RJ. Learning-based automated segmentation of the carotid artery vessel wall in dual-sequence MRI using subdivision surface fitting. Med Phys 2017; 44:5244-5259. [PMID: 28715090 DOI: 10.1002/mp.12476] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 07/10/2017] [Accepted: 07/10/2017] [Indexed: 01/24/2023] Open
Abstract
PURPOSE The quantification of vessel wall morphology and plaque burden requires vessel segmentation, which is generally performed by manual delineations. The purpose of our work is to develop and evaluate a new 3D model-based approach for carotid artery wall segmentation from dual-sequence MRI. METHODS The proposed method segments the lumen and outer wall surfaces including the bifurcation region by fitting a subdivision surface constructed hierarchical-tree model to the image data. In particular, a hybrid segmentation which combines deformable model fitting with boundary classification was applied to extract the lumen surface. The 3D model ensures the correct shape and topology of the carotid artery, while the boundary classification uses combined image information of 3D TOF-MRA and 3D BB-MRI to promote accurate delineation of the lumen boundaries. The proposed algorithm was validated on 25 subjects (48 arteries) including both healthy volunteers and atherosclerotic patients with 30% to 70% carotid stenosis. RESULTS For both lumen and outer wall border detection, our result shows good agreement between manually and automatically determined contours, with contour-to-contour distance less than 1 pixel as well as Dice overlap greater than 0.87 at all different carotid artery sections. CONCLUSIONS The presented 3D segmentation technique has demonstrated the capability of providing vessel wall delineation for 3D carotid MRI data with high accuracy and limited user interaction. This brings benefits to large-scale patient studies for assessing the effect of pharmacological treatment of atherosclerosis by reducing image analysis time and bias between human observers.
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Affiliation(s)
- Shan Gao
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Ronald van 't Klooster
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Pieter H Kitslaar
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Bram F Coolen
- Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
| | | | - Loek P Smits
- Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
| | - Rahil Shahzad
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Denis P Shamonin
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Patrick J H de Koning
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
| | - Aart J Nederveen
- Department of Radiology, Academic Medical Center, 1100 DD, Amsterdam, The Netherlands
| | - Rob J van der Geest
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, 2300 RC, Leiden, The Netherlands
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12
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Rajchl M, Lee MCH, Oktay O, Kamnitsas K, Passerat-Palmbach J, Bai W, Damodaram M, Rutherford MA, Hajnal JV, Kainz B, Rueckert D. DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:674-683. [PMID: 27845654 PMCID: PMC7115996 DOI: 10.1109/tmi.2016.2621185] [Citation(s) in RCA: 129] [Impact Index Per Article: 18.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut [1] method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naïve approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.
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13
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Xu XP, Zhang X, Liu Y, Tian Q, Zhang GP, Yang ZY, Lu HB, Yuan J. Simultaneous Segmentation of Multiple Regions in 3D Bladder MRI by Efficient Convex Optimization of Coupled Surfaces. LECTURE NOTES IN COMPUTER SCIENCE 2017. [DOI: 10.1007/978-3-319-71589-6_46] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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14
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Baxter JSH, Inoue J, Drangova M, Peters TM. Shape complexes: the intersection of label orderings and star convexity constraints in continuous max-flow medical image segmentation. J Med Imaging (Bellingham) 2016; 3:044005. [PMID: 28018937 DOI: 10.1117/1.jmi.3.4.044005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 11/28/2016] [Indexed: 11/14/2022] Open
Abstract
Optimization-based segmentation approaches deriving from discrete graph-cuts and continuous max-flow have become increasingly nuanced, allowing for topological and geometric constraints on the resulting segmentation while retaining global optimality. However, these two considerations, topological and geometric, have yet to be combined in a unified manner. The concept of "shape complexes," which combine geodesic star convexity with extendable continuous max-flow solvers, is presented. These shape complexes allow more complicated shapes to be created through the use of multiple labels and super-labels, with geodesic star convexity governed by a topological ordering. These problems can be optimized using extendable continuous max-flow solvers. Previous approaches required computationally expensive coordinate system warping, which are ill-defined and ambiguous in the general case. These shape complexes are demonstrated in a set of synthetic images as well as vessel segmentation in ultrasound, valve segmentation in ultrasound, and atrial wall segmentation from contrast-enhanced CT. Shape complexes represent an extendable tool alongside other continuous max-flow methods that may be suitable for a wide range of medical image segmentation problems.
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Affiliation(s)
- John S H Baxter
- Western University, Robarts Research Institute, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada; Western University, Biomedical Engineering Graduate Program, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada
| | - Jiro Inoue
- Western University , Robarts Research Institute, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada
| | - Maria Drangova
- Western University, Robarts Research Institute, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada; Western University, Biomedical Engineering Graduate Program, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada
| | - Terry M Peters
- Western University, Robarts Research Institute, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada; Western University, Biomedical Engineering Graduate Program, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada
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15
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Gao S, van 't Klooster R, Brandts A, Roes SD, Alizadeh Dehnavi R, de Roos A, Westenberg JJ, van der Geest RJ. Quantification of common carotid artery and descending aorta vessel wall thickness from MR vessel wall imaging using a fully automated processing pipeline. J Magn Reson Imaging 2016; 45:215-228. [DOI: 10.1002/jmri.25332] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 05/20/2016] [Indexed: 11/08/2022] Open
Affiliation(s)
- Shan Gao
- Division of Image Processing; Department of Radiology, Leiden University Medical Center; Leiden Netherlands
| | - Ronald van 't Klooster
- Division of Image Processing; Department of Radiology, Leiden University Medical Center; Leiden Netherlands
| | - Anne Brandts
- Department of Radiology; Leiden University Medical Center; Leiden Netherlands
| | - Stijntje D. Roes
- Department of Radiology; Leiden University Medical Center; Leiden Netherlands
| | | | - Albert de Roos
- Department of Radiology; Leiden University Medical Center; Leiden Netherlands
| | - Jos J.M. Westenberg
- Division of Image Processing; Department of Radiology, Leiden University Medical Center; Leiden Netherlands
| | - Rob J. van der Geest
- Division of Image Processing; Department of Radiology, Leiden University Medical Center; Leiden Netherlands
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16
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Suárez-Mejías C, Pérez-Carrasco JA, Serrano C, López-Guerra JL, Parra-Calderón C, Gómez-Cía T, Acha B. Three-dimensional segmentation of retroperitoneal masses using continuous convex relaxation and accumulated gradient distance for radiotherapy planning. Med Biol Eng Comput 2016; 55:1-15. [PMID: 27099157 DOI: 10.1007/s11517-016-1505-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 03/28/2016] [Indexed: 11/25/2022]
Abstract
An innovative algorithm has been developed for the segmentation of retroperitoneal tumors in 3D radiological images. This algorithm makes it possible for radiation oncologists and surgeons semiautomatically to select tumors for possible future radiation treatment and surgery. It is based on continuous convex relaxation methodology, the main novelty being the introduction of accumulated gradient distance, with intensity and gradient information being incorporated into the segmentation process. The algorithm was used to segment 26 CT image volumes. The results were compared with manual contouring of the same tumors. The proposed algorithm achieved 90 % sensitivity, 100 % specificity and 84 % positive predictive value, obtaining a mean distance to the closest point of 3.20 pixels. The algorithm's dependence on the initial manual contour was also analyzed, with results showing that the algorithm substantially reduced the variability of the manual segmentation carried out by different specialists. The algorithm was also compared with four benchmark algorithms (thresholding, edge-based level-set, region-based level-set and continuous max-flow with two labels). To the best of our knowledge, this is the first time the segmentation of retroperitoneal tumors for radiotherapy planning has been addressed.
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Affiliation(s)
- Cristina Suárez-Mejías
- Technological Innovation Group, Virgen del Rocío University Hospital, Seville, Spain.
- Signal Theory and Communications Department, University of Seville, Seville, Spain.
| | | | - Carmen Serrano
- Signal Theory and Communications Department, University of Seville, Seville, Spain
| | | | - Carlos Parra-Calderón
- Technological Innovation Group, Virgen del Rocío University Hospital, Seville, Spain
| | - Tomás Gómez-Cía
- Surgery Unit, Virgen del Rocío University Hospital, Seville, Spain
| | - Begoña Acha
- Signal Theory and Communications Department, University of Seville, Seville, Spain
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17
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Arias-Lorza AM, Petersen J, van Engelen A, Selwaness M, van der Lugt A, Niessen WJ, de Bruijne M. Carotid Artery Wall Segmentation in Multispectral MRI by Coupled Optimal Surface Graph Cuts. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:901-911. [PMID: 26595912 DOI: 10.1109/tmi.2015.2501751] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We present a new three-dimensional coupled optimal surface graph-cut algorithm to segment the wall of the carotid artery bifurcation from Magnetic Resonance (MR) images. The method combines the search for both inner and outer borders into a single graph cut and uses cost functions that integrate information from multiple sequences. Our approach requires manual localization of only three seed points indicating the start and end points of the segmentation in the internal, external, and common carotid artery. We performed a quantitative validation using images of 57 carotid arteries. Dice overlap of 0.86 ± 0.06 for the complete vessel and 0.89 ± 0.05 for the lumen compared to manual annotation were obtained. Reproducibility tests were performed in 60 scans acquired with an interval of 15 ± 9 days, showing good agreement between baseline and follow-up segmentations with intraclass correlations of 0.96 and 0.74 for the lumen and complete vessel volumes respectively.
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18
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Kuo JW, Mamou J, Aristizábal O, Zhao X, Ketterling JA, Wang Y. Nested Graph Cut for Automatic Segmentation of High-Frequency Ultrasound Images of the Mouse Embryo. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:427-441. [PMID: 26357396 DOI: 10.1109/tmi.2015.2477395] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
We propose a fully automatic segmentation method called nested graph cut to segment images (2D or 3D) that contain multiple objects with a nested structure. Compared to other graph-cut-based methods developed for multiple regions, our method can work well for nested objects without requiring manual selection of initial seeds, even if different objects have similar intensity distributions and some object boundaries are missing. Promising results were obtained for separating the brain ventricles, the head, and the uterus region in the mouse-embryo head images obtained using high-frequency ultrasound imaging. The proposed method achieved mean Dice similarity coefficients of 0.87 ±0.04 and 0.89 ±0.06 for segmenting BVs and the head, respectively, compared to manual segmentation results by experts on 40 3D images over five gestation stages.
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19
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Abstract
Plaque imaging by MR imaging provides a wealth of information on the characteristics of individual plaque that may reveal vulnerability to rupture, likelihood of progression, or optimal treatment strategy. T1-weighted and T2-weighted images among other options reveal plaque morphology and composition. Dynamic contrast-enhanced-MR imaging reveals plaque activity. To extract this information, image processing tools are needed. Numerous approaches for analyzing such images have been developed, validated against histologic gold standards, and used in clinical studies. These efforts are summarized in this article.
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Affiliation(s)
- Huijun Chen
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Room No. 109, Haidian District, Beijing, China
| | - Qiang Zhang
- Department of Biomedical Engineering, Center for Biomedical Imaging Research, School of Medicine, Tsinghua University, Room No. 120, Haidian District, Beijing, China
| | - William Kerwin
- Department of Radiology, School of Medicine, University of Washington, 850 Republican Street, Seattle, WA 98109, USA.
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20
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Ukwatta E, Yuan J, Qiu W, Rajchl M, Chiu B, Fenster A. Joint segmentation of lumen and outer wall from femoral artery MR images: Towards 3D imaging measurements of peripheral arterial disease. Med Image Anal 2015; 26:120-32. [PMID: 26387053 DOI: 10.1016/j.media.2015.08.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Revised: 08/17/2015] [Accepted: 08/19/2015] [Indexed: 10/23/2022]
Abstract
Three-dimensional (3D) measurements of peripheral arterial disease (PAD) plaque burden extracted from fast black-blood magnetic resonance (MR) images have shown to be more predictive of clinical outcomes than PAD stenosis measurements. To this end, accurate segmentation of the femoral artery lumen and outer wall is required for generating volumetric measurements of PAD plaque burden. Here, we propose a semi-automated algorithm to jointly segment the femoral artery lumen and outer wall surfaces from 3D black-blood MR images, which are reoriented and reconstructed along the medial axis of the femoral artery to obtain improved spatial coherence between slices of the long, thin femoral artery and to reduce computation time. The developed segmentation algorithm enforces two priors in a global optimization manner: the spatial consistency between the adjacent 2D slices and the anatomical region order between the femoral artery lumen and outer wall surfaces. The formulated combinatorial optimization problem for segmentation is solved globally and exactly by means of convex relaxation using a coupled continuous max-flow (CCMF) model, which is a dual formulation to the convex relaxed optimization problem. In addition, the CCMF model directly derives an efficient duality-based algorithm based on the modern multiplier augmented optimization scheme, which has been implemented on a GPU for fast computation. The computed segmentations from the developed algorithm were compared to manual delineations from experts using 20 black-blood MR images. The developed algorithm yielded both high accuracy (Dice similarity coefficients ≥ 87% for both the lumen and outer wall surfaces) and high reproducibility (intra-class correlation coefficient of 0.95 for generating vessel wall area), while outperforming the state-of-the-art method in terms of computational time by a factor of ≈ 20.
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Affiliation(s)
- Eranga Ukwatta
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
| | - Jing Yuan
- Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada
| | - Wu Qiu
- Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada
| | - Martin Rajchl
- Department of Computing, Imperial College London, London, UK
| | - Bernard Chiu
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong
| | - Aaron Fenster
- Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada
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21
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Qiu W, Yuan J, Rajchl M, Kishimoto J, Chen Y, de Ribaupierre S, Chiu B, Fenster A. 3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets. Neuroimage 2015; 118:13-25. [DOI: 10.1016/j.neuroimage.2015.05.099] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 05/19/2015] [Accepted: 05/21/2015] [Indexed: 11/15/2022] Open
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22
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Guo F, Yuan J, Rajchl M, Svenningsen S, Capaldi DPI, Sheikh K, Fenster A, Parraga G. Globally optimal co-segmentation of three-dimensional pulmonary ¹H and hyperpolarized ³He MRI with spatial consistence prior. Med Image Anal 2015; 23:43-55. [PMID: 25958028 DOI: 10.1016/j.media.2015.04.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2014] [Revised: 04/05/2015] [Accepted: 04/08/2015] [Indexed: 10/23/2022]
Abstract
Pulmonary imaging using hyperpolarized (3)He/(129)Xe gas is emerging as a new way to understand the regional nature of pulmonary ventilation abnormalities in obstructive lung diseases. However, the quantitative information derived is completely dependent on robust methods to segment both functional and structural/anatomical data. Here, we propose an approach to jointly segment the lung cavity from (1)H and (3)He pulmonary magnetic resonance images (MRI) by constraining the spatial consistency of the two segmentation regions, which simultaneously employs the image features from both modalities. We formulated the proposed co-segmentation problem as a coupled continuous min-cut model and showed that this combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In particular, we introduced a dual coupled continuous max-flow model to study the convex relaxed coupled continuous min-cut model under a primal and dual perspective. This gave rise to an efficient duality-based convex optimization algorithm. We implemented the proposed algorithm in parallel using general-purpose programming on graphics processing unit (GPGPU), which substantially increased its computational efficiency. Our experiments explored a clinical dataset of 25 subjects with chronic obstructive pulmonary disease (COPD) across a wide range of disease severity. The results showed that the proposed co-segmentation approach yielded superior performance compared to single-channel image segmentation in terms of precision, accuracy and robustness.
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Affiliation(s)
- Fumin Guo
- Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Graduate Program in Biomedical Engineering, The University of Western Ontario, London, ON, Canada.
| | - Jing Yuan
- Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.
| | - Martin Rajchl
- Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Graduate Program in Biomedical Engineering, The University of Western Ontario, London, ON, Canada.
| | - Sarah Svenningsen
- Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.
| | - Dante P I Capaldi
- Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.
| | - Khadija Sheikh
- Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.
| | - Aaron Fenster
- Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Graduate Program in Biomedical Engineering, The University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.
| | - Grace Parraga
- Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Graduate Program in Biomedical Engineering, The University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.
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23
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Qiu W, Yuan J, Kishimoto J, McLeod J, Chen Y, de Ribaupierre S, Fenster A. User-guided segmentation of preterm neonate ventricular system from 3-D ultrasound images using convex optimization. ULTRASOUND IN MEDICINE & BIOLOGY 2015; 41:542-556. [PMID: 25542486 DOI: 10.1016/j.ultrasmedbio.2014.09.019] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2014] [Revised: 09/05/2014] [Accepted: 09/11/2014] [Indexed: 06/04/2023]
Abstract
A three-dimensional (3-D) ultrasound (US) system has been developed to monitor the intracranial ventricular system of preterm neonates with intraventricular hemorrhage (IVH) and the resultant dilation of the ventricles (ventriculomegaly). To measure ventricular volume from 3-D US images, a semi-automatic convex optimization-based approach is proposed for segmentation of the cerebral ventricular system in preterm neonates with IVH from 3-D US images. The proposed semi-automatic segmentation method makes use of the convex optimization technique supervised by user-initialized information. Experiments using 58 patient 3-D US images reveal that our proposed approach yielded a mean Dice similarity coefficient of 78.2% compared with the surfaces that were manually contoured, suggesting good agreement between these two segmentations. Additional metrics, the mean absolute distance of 0.65 mm and the maximum absolute distance of 3.2 mm, indicated small distance errors for a voxel spacing of 0.22 × 0.22 × 0.22 mm(3). The Pearson correlation coefficient (r = 0.97, p < 0.001) indicated a significant correlation of algorithm-generated ventricular system volume (VSV) with the manually generated VSV. The calculated minimal detectable difference in ventricular volume change indicated that the proposed segmentation approach with 3-D US images is capable of detecting a VSV difference of 6.5 cm(3) with 95% confidence, suggesting that this approach might be used for monitoring IVH patients' ventricular changes using 3-D US imaging. The mean segmentation times of the graphics processing unit (GPU)- and central processing unit-implemented algorithms were 50 ± 2 and 205 ± 5 s for one 3-D US image, respectively, in addition to 120 ± 10 s for initialization, less than the approximately 35 min required by manual segmentation. In addition, repeatability experiments indicated that the intra-observer variability ranges from 6.5% to 7.5%, and the inter-observer variability is 8.5% in terms of the coefficient of variation of the Dice similarity coefficient. The intra-class correlation coefficient for ventricular system volume measurements for each independent observer ranged from 0.988 to 0.996 and was 0.945 for three different observers. The coefficient of variation and intra-class correlation coefficient revealed that the intra- and inter-observer variability of the proposed approach introduced by the user initialization was small, indicating good reproducibility, independent of different users.
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Affiliation(s)
- Wu Qiu
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada.
| | - Jing Yuan
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Jessica Kishimoto
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Jonathan McLeod
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Yimin Chen
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
| | - Sandrine de Ribaupierre
- Neurosurgery, Department of Clinical Neurologic Sciences, University of Western Ontario, London, Ontario, Canada
| | - Aaron Fenster
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
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24
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Qiu W, Yuan J, Ukwatta E, Fenster A. Rotationally resliced 3D prostate TRUS segmentation using convex optimization with shape priors. Med Phys 2015; 42:877-91. [DOI: 10.1118/1.4906129] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
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25
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van Engelen A, Niessen WJ, Klein S, Groen HC, Verhagen HJM, Wentzel JJ, van der Lugt A, de Bruijne M. Atherosclerotic plaque component segmentation in combined carotid MRI and CTA data incorporating class label uncertainty. PLoS One 2014; 9:e94840. [PMID: 24762678 PMCID: PMC3999092 DOI: 10.1371/journal.pone.0094840] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Accepted: 03/19/2014] [Indexed: 11/22/2022] Open
Abstract
Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for training is directly obtained from 3D registration with histology for fibrous and lipid-rich necrotic tissue, and with μCT for calcification. This registration does, however, not provide accurate voxelwise correspondence. We therefore evaluate three approaches that incorporate uncertainty in the ground truth used for training: I) soft labels are created by Gaussian blurring of the original binary histology segmentations to reduce weights at the boundaries between components, and are weighted by the estimated registration accuracy of the histology and in vivo imaging data (measured by overlap), II) samples are weighted by the local contour distance of the lumen and outer wall between histology and in vivo data, and III) 10% of each class is rejected by Gaussian outlier rejection. Classification was evaluated on the relative volumes (% of tissue type in the vessel wall) for calcified, fibrous and lipid-rich necrotic tissue, using linear discriminant (LDC) and support vector machine (SVM) classification. In addition, the combination of MRI and CTA data was compared to using only one imaging modality. Best results were obtained by LDC and outlier rejection: the volume error per vessel was 0.9±1.0% for calcification, 12.7±7.6% for fibrous and 12.1±8.1% for necrotic tissue, with Spearman rank correlation coefficients of 0.91 (calcification), 0.80 (fibrous) and 0.81 (necrotic). While segmentation using only MRI features yielded low accuracy for calcification, and segmentation using only CTA features yielded low accuracy for necrotic tissue, the combination of features from MRI and CTA gave good results for all studied components.
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Affiliation(s)
- Arna van Engelen
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, the Netherlands
| | - Wiro J. Niessen
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Delft, the Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, the Netherlands
| | - Harald C. Groen
- Department of Biomedical Engineering, Erasmus MC, Rotterdam, the Netherlands
- Department of Radiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands
| | | | - Jolanda J. Wentzel
- Department of Biomedical Engineering, Erasmus MC, Rotterdam, the Netherlands
| | | | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics & Radiology, Erasmus MC, Rotterdam, the Netherlands
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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26
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Qiu W, Yuan J, Ukwatta E, Sun Y, Rajchl M, Fenster A. Dual optimization based prostate zonal segmentation in 3D MR images. Med Image Anal 2014; 18:660-73. [PMID: 24721776 DOI: 10.1016/j.media.2014.02.009] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2013] [Revised: 02/18/2014] [Accepted: 02/24/2014] [Indexed: 10/25/2022]
Abstract
Efficient and accurate segmentation of the prostate and two of its clinically meaningful sub-regions: the central gland (CG) and peripheral zone (PZ), from 3D MR images, is of great interest in image-guided prostate interventions and diagnosis of prostate cancer. In this work, a novel multi-region segmentation approach is proposed to simultaneously segment the prostate and its two major sub-regions from only a single 3D T2-weighted (T2w) MR image, which makes use of the prior spatial region consistency and incorporates a customized prostate appearance model into the segmentation task. The formulated challenging combinatorial optimization problem is solved by means of convex relaxation, for which a novel spatially continuous max-flow model is introduced as the dual optimization formulation to the studied convex relaxed optimization problem with region consistency constraints. The proposed continuous max-flow model derives an efficient duality-based algorithm that enjoys numerical advantages and can be easily implemented on GPUs. The proposed approach was validated using 18 3D prostate T2w MR images with a body-coil and 25 images with an endo-rectal coil. Experimental results demonstrate that the proposed method is capable of efficiently and accurately extracting both the prostate zones: CG and PZ, and the whole prostate gland from the input 3D prostate MR images, with a mean Dice similarity coefficient (DSC) of 89.3±3.2% for the whole gland (WG), 82.2±3.0% for the CG, and 69.1±6.9% for the PZ in 3D body-coil MR images; 89.2±3.3% for the WG, 83.0±2.4% for the CG, and 70.0±6.5% for the PZ in 3D endo-rectal coil MR images. In addition, the experiments of intra- and inter-observer variability introduced by user initialization indicate a good reproducibility of the proposed approach in terms of volume difference (VD) and coefficient-of-variation (CV) of DSC.
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Affiliation(s)
- Wu Qiu
- Robarts Research Institute, University of Western Ontario, London, ON, Canada.
| | - Jing Yuan
- Robarts Research Institute, University of Western Ontario, London, ON, Canada
| | - Eranga Ukwatta
- Robarts Research Institute, University of Western Ontario, London, ON, Canada; Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada
| | - Yue Sun
- Robarts Research Institute, University of Western Ontario, London, ON, Canada; Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada
| | - Martin Rajchl
- Robarts Research Institute, University of Western Ontario, London, ON, Canada; Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada
| | - Aaron Fenster
- Robarts Research Institute, University of Western Ontario, London, ON, Canada; Biomedical Engineering Graduate Program, University of Western Ontario, London, ON, Canada; Medical Biophysics, University of Western Ontario, London, ON, Canada
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27
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Ukwatta E, Yuan J, Qiu W, Rajchl M, Chiu B, Shavakh S, Xu J, Fenster A. Joint segmentation of 3D femoral lumen and outer wall surfaces from MR images. ACTA ACUST UNITED AC 2014; 16:534-41. [PMID: 24505708 DOI: 10.1007/978-3-642-40811-3_67] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2023]
Abstract
We propose a novel algorithm to jointly delineate the femoral artery lumen and outer wall surfaces from 3D black-blood MR images, while enforcing the spatial consistency of the reoriented MR slices along the medial axis of the femoral artery. We demonstrate that the resulting optimization problem of the proposed segmentation can be solved globally and exactly by means of convex relaxation, for which we introduce a novel coupled continuous max-flow (CCOMF) model based on an Ishikawa-type flow configuration and show its duality to the studied convex relaxed optimization problem. Using the proposed CCMF model, the exactness and globalness of its dual convex relaxation problem is proven. Experiment results demonstrate that the proposed method yielded high accuracy (i.e. Dice similarity coefficient > 85%) for both the lumen and outer wall and high reproducibility (intra-class correlation coefficient of 0.95) for generating vessel wall area. The proposed method outperformed the previous method, in terms of computation time, by a factor of pproximately 20.
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Affiliation(s)
- Eranga Ukwatta
- Robarts Research Institute, Western University, London, ON, Canada.
| | - Jing Yuan
- Robarts Research Institute, Western University, London, ON, Canada
| | - Wu Qiu
- Robarts Research Institute, Western University, London, ON, Canada
| | - Martin Rajchl
- Robarts Research Institute, Western University, London, ON, Canada
| | - Bernard Chiu
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong
| | - Shadi Shavakh
- Robarts Research Institute, Western University, London, ON, Canada
| | - Jianrong Xu
- Renji Hospital, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Aaron Fenster
- Robarts Research Institute, Western University, London, ON, Canada
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28
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Rajchl M, Yuan J, White JA, Ukwatta E, Stirrat J, Nambakhsh CMS, Li FP, Peters TM. Interactive Hierarchical-Flow Segmentation of Scar Tissue From Late-Enhancement Cardiac MR Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:159-172. [PMID: 24107924 DOI: 10.1109/tmi.2013.2282932] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We propose a novel multi-region image segmentation approach to extract myocardial scar tissue from 3-D whole-heart cardiac late-enhancement magnetic resonance images in an interactive manner. For this purpose, we developed a graphical user interface to initialize a fast max-flow-based segmentation algorithm and segment scar accurately with progressive interaction. We propose a partially-ordered Potts (POP) model to multi-region segmentation to properly encode the known spatial consistency of cardiac regions. Its generalization introduces a custom label/region order constraint to Potts model to multi-region segmentation. The combinatorial optimization problem associated with the proposed POP model is solved by means of convex relaxation, for which a novel multi-level continuous max-flow formulation, i.e., the hierarchical continuous max-flow (HMF) model, is proposed and studied. We demonstrate that the proposed HMF model is dual or equivalent to the convex relaxed POP model and introduces a new and efficient hierarchical continuous max-flow based algorithm by modern convex optimization theory. In practice, the introduced hierarchical continuous max-flow based algorithm can be implemented on the parallel GPU to achieve significant acceleration in numerics. Experiments are performed in 50 whole heart 3-D LE datasets, 35 with left-ventricular and 15 with right-ventricular scar. The experimental results are compared to full-width-at-half-maximum and Signal-threshold to reference-mean methods using manual expert myocardial segmentations and operator variabilities and the effect of user interaction are assessed. The results indicate a substantial reduction in image processing time with robust accuracy for detection of myocardial scar. This is achieved without the need for additional region constraints and using a single optimization procedure, substantially reducing the potential for error.
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29
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Saba L, Anzidei M, Marincola BC, Piga M, Raz E, Bassareo PP, Napoli A, Mannelli L, Catalano C, Wintermark M. Imaging of the carotid artery vulnerable plaque. Cardiovasc Intervent Radiol 2013; 37:572-85. [PMID: 23912494 DOI: 10.1007/s00270-013-0711-2] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Accepted: 07/03/2013] [Indexed: 11/28/2022]
Abstract
Atherosclerosis involving the carotid arteries has a high prevalence in the population worldwide. This condition is significant because accidents of the carotid artery plaque are associated with the development of cerebrovascular events. For this reason, carotid atherosclerotic disease needs to be diagnosed and those determinants that are associated to an increased risk of stroke need to be identified. The degree of stenosis typically has been considered the parameter of choice to determine the therapeutical approach, but several recently published investigations have demonstrated that the degree of luminal stenosis is only an indirect indicator of the atherosclerotic process and that direct assessment of the plaque structure and composition may be key to predict the development of future cerebrovascular ischemic events. The concept of "vulnerable plaque" was born, referring to those plaque's parameters that concur to the instability of the plaque making it more prone to the rupture and distal embolization. The purpose of this review is to describe the imaging characteristics of "vulnerable carotid plaques."
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Affiliation(s)
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554, 09045, Monserrato, Cagliari, Italy,
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Qiu W, Yuan J, Kishimoto J, Ukwatta E, Fenster A. Lateral Ventricle Segmentation of 3D Pre-term Neonates US Using Convex Optimization. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION – MICCAI 2013 2013; 16:559-66. [DOI: 10.1007/978-3-642-40760-4_70] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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