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Zhang Y, Luo G, Wang W, Cao S, Dong S, Yu D, Wang X, Wang K. TTN: Topological Transformer Network for Automated Coronary Artery Branch Labeling in Cardiac CT Angiography. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 12:129-139. [PMID: 38074924 PMCID: PMC10706468 DOI: 10.1109/jtehm.2023.3329031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 10/05/2023] [Accepted: 10/23/2023] [Indexed: 12/18/2023]
Abstract
OBJECTIVE Existing methods for automated coronary artery branch labeling in cardiac CT angiography face two limitations: 1) inability to model overall correlation of branches, since differences between branches cannot be captured directly. 2) a serious class imbalance between main and side branches. METHODS AND PROCEDURES Inspired by the application of Transformer in sequence data, we propose a topological Transformer network (TTN), which solves the vessel branch labeling from a novel perspective of sequence labeling learning. TTN detects differences between branches by establishing their overall correlation. A topological encoding that represents the positions of vessel segments in the artery tree, is proposed to assist the model in classifying branches. Also, a segment-depth loss is introduced to solve the class imbalance between main and side branches. RESULTS On a dataset with 325 CCTA, our method obtains the best overall result on all branches, the best result on side branches, and a competitive result on main branches. CONCLUSION TTN solves two limitations in existing methods perfectly, thus achieving the best result in coronary artery branch labeling task. It is the first Transformer based vessel branch labeling method and is notably different from previous methods. CLINICAL IMPACT This Pre-Clinical Research can be integrated into a computer-aided diagnosis system to generate cardiovascular disease diagnosis report, assisting clinicians in locating the atherosclerotic plaques.
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Affiliation(s)
- Yuyang Zhang
- Faculty of ComputingHarbin Institute of TechnologyHarbin150001China
| | - Gongning Luo
- Faculty of ComputingHarbin Institute of TechnologyHarbin150001China
| | - Wei Wang
- Faculty of ComputingHarbin Institute of TechnologyHarbin150001China
- School of Computer Science and TechnologyHarbin Institute of TechnologyShenzhen518000China
| | - Shaodong Cao
- Department of RadiologyThe Fourth Hospital of Harbin Medical UniversityHarbin150001China
| | - Suyu Dong
- College of Computer and Control EngineeringNortheast Forestry UniversityHarbin150040China
| | - Daren Yu
- Department of CardiologyThe Fourth Hospital of Harbin Medical UniversityHarbin150001China
| | - Xiaoyun Wang
- Department of CardiologyThe Fourth Hospital of Harbin Medical UniversityHarbin150001China
| | - Kuanquan Wang
- Faculty of ComputingHarbin Institute of TechnologyHarbin150001China
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Hong SW, Song HN, Choi JU, Cho HH, Baek IY, Lee JE, Kim YC, Chung D, Chung JW, Bang OY, Kim GM, Park HJ, Liebeskind DS, Seo WK. Automated in-depth cerebral arterial labelling using cerebrovascular vasculature reframing and deep neural networks. Sci Rep 2023; 13:3255. [PMID: 36828857 PMCID: PMC9957982 DOI: 10.1038/s41598-023-30234-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 02/20/2023] [Indexed: 02/26/2023] Open
Abstract
Identifying the cerebral arterial branches is essential for undertaking a computational approach to cerebrovascular imaging. However, the complexity and inter-individual differences involved in this process have not been thoroughly studied. We used machine learning to examine the anatomical profile of the cerebral arterial tree. The method is less sensitive to inter-subject and cohort-wise anatomical variations and exhibits robust performance with an unprecedented in-depth vessel range. We applied machine learning algorithms to disease-free healthy control subjects (n = 42), patients with stroke with intracranial atherosclerosis (ICAS) (n = 46), and patients with stroke mixed with the existing controls (n = 69). We trained and tested 70% and 30% of each study cohort, respectively, incorporating spatial coordinates and geometric vessel feature vectors. Cerebral arterial images were analyzed based on the 'segmentation-stacking' method using magnetic resonance angiography. We precisely classified the cerebral arteries across the exhaustive scope of vessel components using advanced geometric characterization, redefinition of vessel unit conception, and post-processing algorithms. We verified that the neural network ensemble, with multiple joint models as the combined predictor, classified all vessel component types independent of inter-subject variations in cerebral arterial anatomy. The validity of the categorization performance of the model was tested, considering the control, ICAS, and control-blended stroke cohorts, using the area under the receiver operating characteristic (ROC) curve and precision-recall curve. The classification accuracy rarely fell outside each image's 90-99% scope, independent of cohort-dependent cerebrovascular structural variations. The classification ensemble was calibrated with high overall area rates under the ROC curve of 0.99-1.00 [0.97-1.00] in the test set across various study cohorts. Identifying an all-inclusive range of vessel components across controls, ICAS, and stroke patients, the accuracy rates of the prediction were: internal carotid arteries, 91-100%; middle cerebral arteries, 82-98%; anterior cerebral arteries, 88-100%; posterior cerebral arteries, 87-100%; and collections of superior, anterior inferior, and posterior inferior cerebellar arteries, 90-99% in the chunk-level classification. Using a voting algorithm on the queued classified vessel factors and anatomically post-processing the automatically classified results intensified quantitative prediction performance. We employed stochastic clustering and deep neural network ensembles. Ma-chine intelligence-assisted prediction of vessel structure allowed us to personalize quantitative predictions of various types of cerebral arterial structures, contributing to precise and efficient decisions regarding the cerebrovascular disease.
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Affiliation(s)
- Suk-Woo Hong
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
- Program in Brain Science, College of Natural Sciences, Seoul National University, Seoul, 08826, Korea
| | - Ha-Na Song
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Jong-Un Choi
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Irwon-dong, Gangnam-gu, Seoul, 06351, Korea
| | - Hwan-Ho Cho
- Department of Medical Artificial Intelligence, Konyang University, Daejeon, Korea
| | - In-Young Baek
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Ji-Eun Lee
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Yoon-Chul Kim
- Division of Digital Healthcare, Yonsei University Mirae Campus, Wonju, 26493, Korea
| | - Darda Chung
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Jong-Won Chung
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Oh-Young Bang
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Gyeong-Moon Kim
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea
| | - Hyun-Jin Park
- Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, Korea
| | - David S Liebeskind
- Department of Neurology and Comprehensive Stroke Center, UCLA, Los Angeles, CA, USA
| | - Woo-Keun Seo
- Department of Neurology and Stroke Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Korea.
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Irwon-dong, Gangnam-gu, Seoul, 06351, Korea.
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Dumais F, Caceres MP, Janelle F, Seifeldine K, Arès-Bruneau N, Gutierrez J, Bocti C, Whittingstall K. eICAB: A novel deep learning pipeline for Circle of Willis multiclass segmentation and analysis. Neuroimage 2022; 260:119425. [PMID: 35809887 DOI: 10.1016/j.neuroimage.2022.119425] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/22/2022] [Accepted: 06/29/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND The accurate segmentation, labeling and quantification of cerebral blood vessels on MR imaging is important for basic and clinical research, yet results are not generalizable, and often require user intervention. New methods are needed to automate this process. PURPOSE To automatically segment, label and quantify Circle of Willis (CW) arteries on Magnetic Resonance Angiography images using deep convolutional neural networks. MATERIALS AND METHODS MRA images were pooled from three public and private databases. A total of 116 subjects (mean age 56 years ± 21 [standard deviation]; 72 women) were used to make up the training set (N=101) and the testing set (N=15). In each image, fourteen arterial segments making up or surrounding the CW were manually annotated and validated by a clinical expert. Convolutional neural network (CNN) models were trained on a training set to be finally combined in an ensemble to develop eICAB. Model performances were evaluated using (1) quantitative analysis (dice score on test set) and (2) qualitative analysis (external datasets, N=121). The reliability was assessed using multiple MRAs of healthy participants (ICC of vessel diameters and volumes on test-retest). RESULTS Qualitative analysis showed that eICAB correctly predicted the large, medium and small arteries in 99±0.4%, 97±1% and 88±7% of all images, respectively. For quantitative assessment, the average dice score coefficients for the large (ICAs, BA), medium (ACAs, MCAs, PCAs-P2), and small (AComm, PComm, PCAs-P1) vessels were 0.76±0.07, 0.76±0.08 and 0.41±0.27, respectively. These results were similar and, in some cases, statistically better (p<0.05) than inter-expert annotation variability and robust to image SNR. Finally, test-retest analysis showed that the model yielded high diameter and volume reliability (ICC=0.99). CONCLUSION We have developed a quick and reliable open-source CNN-based method capable of accurately segmenting and labeling the CW in MRA images. This method is largely independent of image quality. In the future, we foresee this approach as a critical step towards fully automated analysis of MRA databases in basic and clinical research.
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Affiliation(s)
- Félix Dumais
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, Université de Sherbrooke, 3001 12e Avenue N, Sherbrooke, Québec J1H 5H3, Canada.
| | - Marco Perez Caceres
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, Université de Sherbrooke, 3001 12e Avenue N, Sherbrooke, Québec J1H 5H3, Canada
| | - Félix Janelle
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, Université de Sherbrooke, 3001 12e Avenue N, Sherbrooke, Québec J1H 5H3, Canada
| | - Kassem Seifeldine
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, Université de Sherbrooke, 3001 12e Avenue N, Sherbrooke, Québec J1H 5H3, Canada
| | - Noémie Arès-Bruneau
- Department of Medecine, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Jose Gutierrez
- Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Christian Bocti
- Department of Medecine, Université de Sherbrooke, Sherbrooke, Québec, Canada; Research Center on Aging, CIUSSS de l'Estrie - CHUS, Sherbrooke, Québec, Canada; Department of Neurology, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kevin Whittingstall
- Department of Radiology, Université de Sherbrooke, Sherbrooke, Québec, Canada
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Zhu Y, Qian P, Zhao Z, Zeng Z. Deep Feature Fusion via Graph Convolutional Network for Intracranial Artery Labeling. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:467-470. [PMID: 36086340 DOI: 10.1109/embc48229.2022.9871848] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. However, the task remains challenging because of the high complexity and variations of intracranial arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling. We introduce stacked graph convolutions in an encoder-core-decoder architecture, extracting high-level representations from graph nodes and their neighbors. Furthermore, we efficiently aggregate intermediate features from different hierarchies to enhance the proposed model's representation capability and labeling performance. We perform extensive experiments on public datasets, in which the results prove the superiority of our approach over baselines by a clear margin. Clinical relevance- The graph convolutions and feature fusion in our approach effectively extract graph information, which provides more accurate intracranial artery label predictions than existing methods and better facilitates medical research and disease diagnosis.
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Bayly PV, Alshareef A, Knutsen AK, Upadhyay K, Okamoto RJ, Carass A, Butman JA, Pham DL, Prince JL, Ramesh KT, Johnson CL. MR Imaging of Human Brain Mechanics In Vivo: New Measurements to Facilitate the Development of Computational Models of Brain Injury. Ann Biomed Eng 2021; 49:2677-2692. [PMID: 34212235 PMCID: PMC8516723 DOI: 10.1007/s10439-021-02820-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 06/22/2021] [Indexed: 01/04/2023]
Abstract
Computational models of the brain and its biomechanical response to skull accelerations are important tools for understanding and predicting traumatic brain injuries (TBIs). However, most models have been developed using experimental data collected on animal models and cadaveric specimens, both of which differ from the living human brain. Here we describe efforts to noninvasively measure the biomechanical response of the human brain with MRI-at non-injurious strain levels-and generate data that can be used to develop, calibrate, and evaluate computational brain biomechanics models. Specifically, this paper reports on a project supported by the National Institute of Neurological Disorders and Stroke to comprehensively image brain anatomy and geometry, mechanical properties, and brain deformations that arise from impulsive and harmonic skull loadings. The outcome of this work will be a publicly available dataset ( http://www.nitrc.org/projects/bbir ) that includes measurements on both males and females across an age range from adolescence to older adulthood. This article describes the rationale and approach for this study, the data available, and how these data may be used to develop new computational models and augment existing approaches; it will serve as a reference to researchers interested in using these data.
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Affiliation(s)
- Philip V Bayly
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA.
| | - Ahmed Alshareef
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew K Knutsen
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Kshitiz Upadhyay
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Ruth J Okamoto
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - John A Butman
- Clinical Center, National Institutes of Health, Bethesda, MD, USA
| | - Dzung L Pham
- Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - K T Ramesh
- Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Curtis L Johnson
- Department of Biomedical Engineering, University of Delaware, Newark, DE, USA.
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Wu D, Wang X, Bai J, Xu X, Ouyang B, Li Y, Zhang H, Song Q, Cao K, Yin Y. Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs. Int J Comput Assist Radiol Surg 2018; 14:271-280. [DOI: 10.1007/s11548-018-1884-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 11/05/2018] [Indexed: 10/27/2022]
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Kitasaka T, Kagajo M, Nimura Y, Hayashi Y, Oda M, Misawa K, Mori K. Automatic anatomical labeling of arteries and veins using conditional random fields. Int J Comput Assist Radiol Surg 2017; 12:1041-1048. [PMID: 28275889 DOI: 10.1007/s11548-017-1549-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Accepted: 02/27/2017] [Indexed: 11/26/2022]
Abstract
PURPOSE For safe and reliable laparoscopic surgery, it is important to determine individual differences of blood vessels such as the position, shape, and branching structures. Consequently, a computer-assisted laparoscopy that displays blood vessel structures with anatomical labels would be extremely beneficial. This paper details an automated anatomical labeling method for abdominal arteries and veins extracted from 3D CT volumes. METHODS The proposed method represents a blood vessel tree as a probabilistic graphical model by conditional random fields (CRFs). An adaptive gradient algorithm is adopted for structure learning. The anatomical labeling of blood vessel branches is performed by maximum a posteriori estimation. RESULTS We applied the proposed method to 50 cases of arterial and portal phase abdominal X-ray CT volumes. The experimental results showed that the F-measure of the proposed method for abdominal arteries and veins was 94.4 and 86.9%, respectively. CONCLUSION We developed an automated anatomical labeling method to annotate each blood vessel branches of abdominal arteries and veins using CRF. The proposed method outperformed a state-of-the-art method.
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Affiliation(s)
- Takayuki Kitasaka
- School of Information Science, Aichi Institute of Technology, Yachikusa, Yakusa-cho, Toyota, 470-0392, Japan.
| | - Mitsuru Kagajo
- Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Yukitaka Nimura
- Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Yuichiro Hayashi
- Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Masahiro Oda
- Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
| | - Kazunari Misawa
- Department of Gastroenterological Surgery, Aichi Cancer Center Hospital, Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan
| | - Kensaku Mori
- Information and Communications, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan
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Dunås T, Wåhlin A, Ambarki K, Zarrinkoob L, Birgander R, Malm J, Eklund A. Automatic labeling of cerebral arteries in magnetic resonance angiography. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2015; 29:39-47. [PMID: 26646523 DOI: 10.1007/s10334-015-0512-5] [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: 08/18/2015] [Revised: 11/04/2015] [Accepted: 11/10/2015] [Indexed: 12/25/2022]
Abstract
OBJECTIVES In order to introduce 4D flow magnetic resonance imaging (MRI) as a standard clinical instrument for studying the cerebrovascular system, new and faster postprocessing tools are necessary. The objective of this study was to construct and evaluate a method for automatic identification of individual cerebral arteries in a 4D flow MRI angiogram. MATERIALS AND METHODS Forty-six elderly individuals were investigated with 4D flow MRI. Fourteen main cerebral arteries were manually labeled and used to create a probabilistic atlas. An automatic atlas-based artery identification method (AAIM) was developed based on vascular-branch extraction and the atlas was used for identification. The method was evaluated by comparing automatic with manual identification in 4D flow MRI angiograms from 67 additional elderly individuals. RESULTS Overall accuracy was 93%, and internal carotid artery and middle cerebral artery labeling was 100% accurate. Smaller and more distal arteries had lower accuracy; for posterior communicating arteries and vertebral arteries, accuracy was 70 and 89%, respectively. CONCLUSION The AAIM enabled fast and fully automatic labeling of the main cerebral arteries. AAIM functionality provides the basis for creating an automatic and powerful method to analyze arterial cerebral blood flow in clinical routine.
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Affiliation(s)
- Tora Dunås
- Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden.
| | - Anders Wåhlin
- Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, S-901 87, Umeå, Sweden
| | - Khalid Ambarki
- Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden
- Centre for Biomedical Engineering and Physics, Umeå University, S-901 87, Umeå, Sweden
| | - Laleh Zarrinkoob
- Department of Clinical Neuroscience, Umeå University, S-901 87, Umeå, Sweden
| | - Richard Birgander
- Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden
| | - Jan Malm
- Department of Clinical Neuroscience, Umeå University, S-901 87, Umeå, Sweden
| | - Anders Eklund
- Department of Radiation Sciences, Umeå University, S-901 87, Umeå, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, S-901 87, Umeå, Sweden
- Centre for Biomedical Engineering and Physics, Umeå University, S-901 87, Umeå, Sweden
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Matsuzaki T, Oda M, Kitasaka T, Hayashi Y, Misawa K, Mori K. Automated anatomical labeling of abdominal arteries and hepatic portal system extracted from abdominal CT volumes. Med Image Anal 2014; 20:152-61. [PMID: 25484019 DOI: 10.1016/j.media.2014.11.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Revised: 11/07/2014] [Accepted: 11/07/2014] [Indexed: 11/28/2022]
Abstract
This paper proposes a method for automated anatomical labeling of abdominal arteries and a hepatic portal system. In abdominal surgeries, understanding blood vessel structure is critical since it is very complicated. The input of the proposed method is the blood vessel region extracted from the CT volume. The blood vessel region is expressed as a tree structure by applying a thinning process to it and compute the mapping from the branches in the tree structure to the anatomical names. First, several characteristic anatomical names are assigned by rule-based pre-processing. The branches assigned to these names are used as references. The remaining blood vessel names are assigned using a likelihood function trained by a machine-learning technique. Simple rule-based postprocessing can correct several blood vessel names. The output of the proposed method is a tree structure with anatomical names. In an experiment using 50 blood vessel regions manually extracted from abdominal CT volumes, the recall and precision rates of the abdominal arteries were 86.2% and 85.3%, and they were 86.5% and 79.5% for the hepatic portal system.
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Affiliation(s)
| | - Masahiro Oda
- Graduate School of Information Science, Nagoya University, Japan
| | - Takayuki Kitasaka
- Faculty of Information Science, Aichi Institute of Technology, Japan
| | | | | | - Kensaku Mori
- Information and Communications, Nagoya University, Japan; Graduate School of Information Science, Nagoya University, Japan
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