1
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Abdominal vessel segmentation using vessel model embedded fuzzy C-means and similarity from CT angiography. Med Biol Eng Comput 2022; 60:3325-3340. [DOI: 10.1007/s11517-022-02644-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 08/09/2022] [Indexed: 11/25/2022]
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2
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Jin L, Gao P, Wang K, Li J, Li M. Intraindividual evaluation of effects of image filter function on image quality in coronary computed tomography angiography. Front Cardiovasc Med 2022; 9:840735. [PMID: 36186969 PMCID: PMC9521173 DOI: 10.3389/fcvm.2022.840735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 08/12/2022] [Indexed: 11/18/2022] Open
Abstract
Objectives To evaluate whether applying image filters (smooth 3D+ and edge-2) improves image quality in coronary CT angiography (CCTA). Methods Ninety patients (routine group) with suspected coronary artery diseases based on 16-cm wide coverage detector CT findings were retrospectively enrolled at a chest pain center from December 2019 to September 2021. Two image filters, smooth 3D+ and edge-2 available on the Advantage Workstation (AW) were subsequently applied to the images to generate the research group (SE group). Quantitative parameters, including CT value, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), image sharpness and image quality score, and diagnostic accuracy were compared between the two groups. Results A total of 900 segments from 270 coronary arteries in 90 patients were analyzed. SNR, CNR, and image sharpness for vessels and image quality scores in the SE group were significantly better than those in the routine group (all p < 0.001). The SE group showed a slightly higher negative predictive value (NPV) on the left anterior descending artery and right coronary artery (RCA) stenosis evaluations, as well as total NPV. The SE group also showed slightly higher sensitivity and accuracy than the routine group on RCA stenosis evaluation. Conclusion The use of an image filter combining smooth 3D+ and edge-2 on an AW could improve the image quality of CCTA and increase radiologists' diagnostic confidence.
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Affiliation(s)
- Liang Jin
- Radiology Department, Huadong Hospital, Affiliated to Fudan University, Shanghai, China
| | - Pan Gao
- Radiology Department, Huadong Hospital, Affiliated to Fudan University, Shanghai, China
| | - Kun Wang
- Radiology Department, Huadong Hospital, Affiliated to Fudan University, Shanghai, China
| | - Jianying Li
- CT Research Center, GE Healthcare China, Beijing, China
| | - Ming Li
- Radiology Department, Huadong Hospital, Affiliated to Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
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3
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Huang Y, Yang J, Sun Q, Ma S, Yuan Y, Tan W, Cao P, Feng C. Vessel filtering and segmentation of coronary CT angiographic images. Int J Comput Assist Radiol Surg 2022; 17:1879-1890. [PMID: 35764765 DOI: 10.1007/s11548-022-02655-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 04/22/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Coronary artery segmentation in coronary computed tomography angiography (CTA) images plays a crucial role in diagnosing cardiovascular diseases. However, due to the complexity of coronary CTA images and coronary structure, it is difficult to automatically segment coronary arteries accurately and efficiently from numerous coronary CTA images. METHOD In this study, an automatic method based on symmetrical radiation filter (SRF) and D-means is presented. The SRF, which is applied to the three orthogonal planes, is designed to filter the suspicious vessel tissue according to the features of gradient changes on vascular boundaries to segment coronary arteries accurately and reduce computational cost. Additionally, the D-means local clustering is proposed to be embedded into vessel segmentation to eliminate noise impact in coronary CTA images. RESULTS The results of the proposed method were compared against the manual delineations in 210 coronary CTA data sets. The average values of true positive, false positive, Jaccard measure, and Dice coefficient were [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Moreover, comparing the delineated data sets and public data sets showed that the proposed method is better than the related methods. CONCLUSION The experimental results indicate that the proposed method can perform complete, robust, and accurate segmentation of coronary arteries with low computational cost. Therefore, the proposed method is proven effective in vessel segmentation of coronary CTA images without extensive training data and can meet clinical applications.
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Affiliation(s)
- Yan Huang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China. .,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
| | - Qi Sun
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Shuang Ma
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Yuliang Yuan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Peng Cao
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
| | - Chaolu Feng
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.,School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China
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4
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Mistelbauer G, Morar A, Schernthaner R, Strassl A, Fleischmann D, Moldoveanu F, Gröller ME. Semi-automatic vessel detection for challenging cases of peripheral arterial disease. Comput Biol Med 2021; 133:104344. [PMID: 33915360 DOI: 10.1016/j.compbiomed.2021.104344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 02/26/2021] [Accepted: 03/12/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVES Manual or semi-automated segmentation of the lower extremity arterial tree in patients with Peripheral arterial disease (PAD) remains a notoriously difficult and time-consuming task. The complex manifestations of the disease, including discontinuities of the vascular flow channels, the presence of calcified atherosclerotic plaque in close vicinity to adjacent bone, and the presence of metal or other imaging artifacts currently preclude fully automated vessel identification. New machine learning techniques may alleviate this challenge, but require large and reasonably well segmented training data. METHODS We propose a novel semi-automatic vessel tracking approach for peripheral arteries to facilitate and accelerate the creation of annotated training data by expert cardiovascular radiologists or technologists, while limiting the number of necessary manual interactions, and reducing processing time. After automatically classifying blood vessels, bones, and other tissue, the relevant vessels are tracked and organized in a tree-like structure for further visualization. RESULTS We conducted a pilot (N = 9) and a clinical study (N = 24) in which we assess the accuracy and required time for our approach to achieve sufficient quality for clinical application, with our current clinically established workflow as the standard of reference. Our approach enabled expert physicians to readily identify all clinically relevant lower extremity arteries, even in problematic cases, with an average sensitivity of 92.9%, and an average specificity and overall accuracy of 99.9%. CONCLUSIONS Compared to the clinical workflow in our collaborating hospitals (28:40 ± 7:45 [mm:ss]), our approach (17:24 ± 6:44 [mm:ss]) is on average 11:16 [mm:ss] (39%) faster.
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Affiliation(s)
- Gabriel Mistelbauer
- Department of Simulation and Graphics, Otto-von-Guericke University Magdeburg, Germany.
| | - Anca Morar
- Department of Computer Science, University Politehnica of Bucharest, Romania.
| | | | - Andreas Strassl
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria.
| | - Dominik Fleischmann
- Department of Radiology, Stanford University School of Medicine, Stanford, USA.
| | - Florica Moldoveanu
- Department of Computer Science, University Politehnica of Bucharest, Romania.
| | - M Eduard Gröller
- Institute of Visual Computing and Human-Centered Technology, TU Wien, Austria; VRVis Research Center, Austria.
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5
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Jia D, Zhuang X. Learning-based algorithms for vessel tracking: A review. Comput Med Imaging Graph 2021; 89:101840. [PMID: 33548822 DOI: 10.1016/j.compmedimag.2020.101840] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 10/07/2020] [Accepted: 12/03/2020] [Indexed: 11/24/2022]
Abstract
Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.
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Affiliation(s)
- Dengqiang Jia
- School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
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6
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Dong X, Zhao H, Li J, Tian Y, Zeng H, Ramos MA, Hu TS, Xu Q. Progress in Bioinspired Dry and Wet Gradient Materials from Design Principles to Engineering Applications. iScience 2020; 23:101749. [PMID: 33241197 PMCID: PMC7672307 DOI: 10.1016/j.isci.2020.101749] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Nature does nothing in vain. Through millions of years of revolution, living organisms have evolved hierarchical and anisotropic structures to maximize their survival in complex and dynamic environments. Many of these structures are intrinsically heterogeneous and often with functional gradient distributions. Understanding the convergent and divergent gradient designs in the natural material systems may lead to a new paradigm shift in the development of next-generation high-performance bio-/nano-materials and devices that are critically needed in energy, environmental remediation, and biomedical fields. Herein, we review the basic design principles and highlight some of the prominent examples of gradient biological materials/structures discovered over the past few decades. Interestingly, despite the anisotropic features in one direction (i.e., in terms of gradient compositions and properties), these natural structures retain certain levels of symmetry, including point symmetry, axial symmetry, mirror symmetry, and 3D symmetry. We further demonstrate the state-of-the-art fabrication techniques and procedures in making the biomimetic counterparts. Some prototypes showcase optimized properties surpassing those seen in the biological model systems. Finally, we summarize the latest applications of these synthetic functional gradient materials and structures in robotics, biomedical, energy, and environmental fields, along with their future perspectives. This review may stimulate scientists, engineers, and inventors to explore this emerging and disruptive research methodology and endeavors.
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Affiliation(s)
- Xiaoxiao Dong
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing, Beijing 102249, China
| | - Hong Zhao
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing, Beijing 102249, China
| | - Jiapeng Li
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing, Beijing 102249, China
| | - Yu Tian
- State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
| | - Hongbo Zeng
- Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Melvin A Ramos
- Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA
| | - Travis Shihao Hu
- Department of Mechanical Engineering, California State University, Los Angeles, CA 90032, USA
| | - Quan Xu
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum-Beijing, Beijing 102249, China
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Maher G, Parker D, Wilson N, Marsden A. Neural Network Vessel Lumen Regression for Automated Lumen Cross-Section Segmentation in Cardiovascular Image-Based Modeling. Cardiovasc Eng Technol 2020; 11:621-635. [PMID: 33179176 DOI: 10.1007/s13239-020-00497-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 10/15/2020] [Indexed: 11/29/2022]
Abstract
PURPOSE We accelerate a pathline-based cardiovascular model building method by training machine learning models to directly predict vessel lumen surface points from computed tomography (CT) and magnetic resonance (MR) medical image data. METHODS We formulate vessel lumen detection as a regression task using a polar coordiantes representation. RESULTS Neural networks trained with our regression formulation allow predictions to be made with significantly higher accuracy than existing methods that identify the vessel lumen through binary pixel classification. The regression formulation enables machine learning models to be trained end-to-end for vessel lumen detection without post-processing steps that reduce accuracy. CONCLUSION By employing our models in a pathline-based cardiovascular model building pipeline we substantially reduce the manual segmentation effort required to build accurate cardiovascular models, and reduce the overall time required to perform patient-specific cardiovascular simulations. While our method is applied here for cardiovascular model building it is generally applicable to segmentation of tree-like and tubular structures from image data.
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Affiliation(s)
- Gabriel Maher
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - David Parker
- Research Computing, Stanford University, Stanford, CA, USA
| | - Nathan Wilson
- Open Source Medical Software Corporation, Los Angeles, CA, USA
| | - Alison Marsden
- Pediatric Cardiology, Bioengineering, Stanford University, Stanford, CA, USA.
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Maher G, Wilson N, Marsden A. Accelerating cardiovascular model building with convolutional neural networks. Med Biol Eng Comput 2019; 57:2319-2335. [PMID: 31446517 PMCID: PMC7250144 DOI: 10.1007/s11517-019-02029-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 08/09/2019] [Indexed: 10/26/2022]
Abstract
The objective of this work is to reduce the user effort required for 2D segmentation when building patient-specific cardiovascular models using the SimVascular cardiovascular modeling software package. The proposed method uses a fully convolutional neural network (FCNN) to generate 2D cardiovascular segmentations. Given vessel pathlines, the neural network generates 2D vessel enhancement images along the pathlines. Thereafter, vessel segmentations are extracted using the marching-squares algorithm, which are then used to construct 3D cardiovascular models. The neural network is trained using a novel loss function, tailored for partially labeled segmentation data. An automated quality control method is also developed, allowing promising segmentations to be selected. Compared with a threshold and level set algorithm, the FCNN method improved 2D segmentation accuracy across several metrics. The proposed quality control approach further improved the average DICE score by 25.8%. In tests with users of SimVascular, when using quality control, users accepted 80% of segmentations produced by the best performing FCNN. The FCNN cardiovascular model building method reduces the amount of manual segmentation effort required for patient-specific model construction, by as much as 73%. This leads to reduced turnaround time for cardiovascular simulations. While the method was used for cardiovascular model building, it is applicable to general tubular structures. Graphical Abstract Proposed FCNN-based cardiovascular model building pipeline. a.) Image data and vessel pathline supplied by the user. b.) Path information is used to extract image pixel intensities in plane perpendicular to the vessel path. c.) 2D images extracted along vessel pathlines are input to the FCNN. d.) FCNN acts on the input images to compute local vessel enhancement images. e.) Vessel enhancement images computed by the FCNN, the pixel values are between 0 and 1 indicating vessel tissue likelihood. f.) The marching-squares algorithm is appliedto each enhanced image to extract the central vessel segmentation. g.) 2D extracted vessel surface points overlayed on original input images. h.) The 2D vessel surface points are transformed back to 3D space. i.) 3D crosssectional vessel surfaces are interpolated along the pathline to form the final vessel model.
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Affiliation(s)
- Gabriel Maher
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.
| | - Nathan Wilson
- Open Source Medical Software Corporation, Los Angeles, CA, USA
| | - Alison Marsden
- Pediatric Cardiology, Bioengineering, Stanford University, Stanford, CA, USA
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9
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Anisotropic diffusion filtering method with weighted directional structure tensor. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101590] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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10
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Directional fast-marching and multi-model strategy to extract coronary artery centerlines. Comput Biol Med 2019; 108:67-77. [DOI: 10.1016/j.compbiomed.2019.03.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 03/29/2019] [Accepted: 03/30/2019] [Indexed: 11/18/2022]
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11
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Moccia S, De Momi E, El Hadji S, Mattos LS. Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:71-91. [PMID: 29544791 DOI: 10.1016/j.cmpb.2018.02.001] [Citation(s) in RCA: 219] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 12/23/2017] [Accepted: 02/02/2018] [Indexed: 05/09/2023]
Abstract
BACKGROUND Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). OBJECTIVE This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. METHODS This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. DISCUSSION Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. CONCLUSION No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.
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Affiliation(s)
- Sara Moccia
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Sara El Hadji
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
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Merveille O, Talbot H, Najman L, Passat N. Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2018; 40:304-317. [PMID: 28237921 DOI: 10.1109/tpami.2017.2672972] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, non-linear operator, called RORPO (Ranking the Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. This operator, unlike most operators commonly used for 3D curvilinear structure analysis, is discrete, non-linear and non-local. From this new operator, two main curvilinear structure characteristics can be estimated: an intensity feature, that can be assimilated to a quantitative measure of curvilinearity; and a directional feature, providing a quantitative measure of the structure's orientation. We provide a full description of the structural and algorithmic details for computing these two features from RORPO, and we discuss computational issues. We experimentally assess RORPO by comparison with three of the most popular curvilinear structure analysis filters, namely Frangi Vesselness, Optimally Oriented Flux, and Hybrid Diffusion with Continuous Switch. In particular, we show that our method provides up to 8 percent more true positive and 50 percent less false positives than the next best method, on synthetic and real 3D images.
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13
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Zhang Q, Chung ACS. 3D Vessel Segmentation Using Random Walker with Oriented Flux Analysis and Direction Coherence. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-43775-0_25] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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