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Fu H, Zhang J, Li B, Chen L, Zou J, Zhang Z, Zou H. Abdominal multi-organ segmentation in Multi-sequence MRIs based on visual attention guided network and knowledge distillation. Phys Med 2024; 122:103385. [PMID: 38810392 DOI: 10.1016/j.ejmp.2024.103385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 03/17/2024] [Accepted: 05/21/2024] [Indexed: 05/31/2024] Open
Abstract
PURPOSE The segmentation of abdominal organs in magnetic resonance imaging (MRI) plays a pivotal role in various therapeutic applications. Nevertheless, the application of deep-learning methods to abdominal organ segmentation encounters numerous challenges, especially in addressing blurred boundaries and regions characterized by low-contrast. METHODS In this study, a multi-scale visual attention-guided network (VAG-Net) was proposed for abdominal multi-organ segmentation based on unpaired multi-sequence MRI. A new visual attention-guided (VAG) mechanism was designed to enhance the extraction of contextual information, particularly at the edge of organs. Furthermore, a new loss function inspired by knowledge distillation was introduced to minimize the semantic disparity between different MRI sequences. RESULTS The proposed method was evaluated on the CHAOS 2019 Challenge dataset and compared with six state-of-the-art methods. The results demonstrated that our model outperformed these methods, achieving DSC values of 91.83 ± 0.24% and 94.09 ± 0.66% for abdominal multi-organ segmentation in T1-DUAL and T2-SPIR modality, respectively. CONCLUSION The experimental results show that our proposed method has superior performance in abdominal multi-organ segmentation, especially in the case of small organs such as the kidneys.
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Affiliation(s)
- Hao Fu
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Jian Zhang
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Bin Li
- Jiangnan University Medical Center, Wuxi No. 2 People's Hospital, Wu Xi, Jiangsu 214000, China
| | - Lanlan Chen
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Junzhong Zou
- Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.
| | - ZhuiYang Zhang
- Jiangnan University Medical Center, Wuxi No. 2 People's Hospital, Wu Xi, Jiangsu 214000, China.
| | - Hao Zou
- Center for Intelligent Medical Imaging and Health, Research Institute of Tsinghua University in Shenzhen, Shenzhen 518000, China.
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Chen W, Zhao L, Bian R, Li Q, Zhao X, Zhang M. Compensation of small data with large filters for accurate liver vessel segmentation from contrast-enhanced CT images. BMC Med Imaging 2024; 24:129. [PMID: 38822274 PMCID: PMC11143594 DOI: 10.1186/s12880-024-01309-1] [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: 02/14/2023] [Accepted: 05/27/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND Segmenting liver vessels from contrast-enhanced computed tomography images is essential for diagnosing liver diseases, planning surgeries and delivering radiotherapy. Nevertheless, identifying vessels is a challenging task due to the tiny cross-sectional areas occupied by vessels, which has posed great challenges for vessel segmentation, such as limited features to be learned and difficult to construct high-quality as well as large-volume data. METHODS We present an approach that only requires a few labeled vessels but delivers significantly improved results. Our model starts with vessel enhancement by fading out liver intensity and generates candidate vessels by a classifier fed with a large number of image filters. Afterwards, the initial segmentation is refined using Markov random fields. RESULTS In experiments on the well-known dataset 3D-IRCADb, the averaged Dice coefficient is lifted to 0.63, and the mean sensitivity is increased to 0.71. These results are significantly better than those obtained from existing machine-learning approaches and comparable to those generated from deep-learning models. CONCLUSION Sophisticated integration of a large number of filters is able to pinpoint effective features from liver images that are sufficient to distinguish vessels from other liver tissues under a scarcity of large-volume labeled data. The study can shed light on medical image segmentation, especially for those without sufficient data.
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Affiliation(s)
- Wen Chen
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
- Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Liang Zhao
- Taihe Hospital, Hubei University of Medicine, Shiyan, China.
| | - Rongrong Bian
- Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Qingzhou Li
- Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Xueting Zhao
- Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
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Praschl C, Zopf LM, Kiemeyer E, Langthallner I, Ritzberger D, Slowak A, Weigl M, Blüml V, Nešić N, Stojmenović M, Kniewallner KM, Aigner L, Winkler S, Walter A. U-Net based vessel segmentation for murine brains with small micro-magnetic resonance imaging reference datasets. PLoS One 2023; 18:e0291946. [PMID: 37824474 PMCID: PMC10569551 DOI: 10.1371/journal.pone.0291946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 09/09/2023] [Indexed: 10/14/2023] Open
Abstract
Identification and quantitative segmentation of individual blood vessels in mice visualized with preclinical imaging techniques is a tedious, manual or semiautomated task that can require weeks of reviewing hundreds of levels of individual data sets. Preclinical imaging, such as micro-magnetic resonance imaging (μMRI) can produce tomographic datasets of murine vasculature across length scales and organs, which is of outmost importance to study tumor progression, angiogenesis, or vascular risk factors for diseases such as Alzheimer's. Training a neural network capable of accurate segmentation results requires a sufficiently large amount of labelled data, which takes a long time to compile. Recently, several reasonably automated approaches have emerged in the preclinical context but still require significant manual input and are less accurate than the deep learning approach presented in this paper-quantified by the Dice score. In this work, the implementation of a shallow, three-dimensional U-Net architecture for the segmentation of vessels in murine brains is presented, which is (1) open-source, (2) can be achieved with a small dataset (in this work only 8 μMRI imaging stacks of mouse brains were available), and (3) requires only a small subset of labelled training data. The presented model is evaluated together with two post-processing methodologies using a cross-validation, which results in an average Dice score of 61.34% in its best setup. The results show, that the methodology is able to detect blood vessels faster and more reliably compared to state-of-the-art vesselness filters with an average Dice score of 43.88% for the used dataset.
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Affiliation(s)
- Christoph Praschl
- Department of Medical and Bioinformatics, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg i. M., Austria
| | - Lydia M. Zopf
- Ludwig Boltzmann Institute for Experimental and Clinical Traumatology in the AUVA trauma research center, Austrian Cluster for Tissue Regeneration, Vienna, Austria
- Vienna BioCenter Core Facilities GmbH (VBCF), Vienna, Austria
| | - Emma Kiemeyer
- Department of Medical and Bioinformatics, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg i. M., Austria
| | - Ines Langthallner
- Department of Medical and Bioinformatics, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg i. M., Austria
| | - Daniel Ritzberger
- Department of Medical and Bioinformatics, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg i. M., Austria
| | - Adrian Slowak
- Department of Medical and Bioinformatics, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg i. M., Austria
| | - Martin Weigl
- Department of Medical and Bioinformatics, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg i. M., Austria
| | - Valentin Blüml
- Vienna BioCenter Core Facilities GmbH (VBCF), Vienna, Austria
| | - Nebojša Nešić
- Faculty of Informatics and Computation, Singidunum University, Belgrade, Serbia
| | - Miloš Stojmenović
- Faculty of Informatics and Computation, Singidunum University, Belgrade, Serbia
| | - Kathrin M. Kniewallner
- Institute of Molecular Regenerative Medicine, Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Ludwig Aigner
- Institute of Molecular Regenerative Medicine, Spinal Cord Injury and Tissue Regeneration Center Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Stephan Winkler
- Department of Medical and Bioinformatics, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Hagenberg i. M., Austria
| | - Andreas Walter
- Vienna BioCenter Core Facilities GmbH (VBCF), Vienna, Austria
- Centre of Optical Technologies, Aalen University, Aalen, Germany
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Radiya K, Joakimsen HL, Mikalsen KØ, Aahlin EK, Lindsetmo RO, Mortensen KE. Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review. Eur Radiol 2023; 33:6689-6717. [PMID: 37171491 PMCID: PMC10511359 DOI: 10.1007/s00330-023-09609-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 05/13/2023]
Abstract
OBJECTIVES Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging? METHODS A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography. RESULTS One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy. CONCLUSION Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this. KEY POINTS • ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.
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Affiliation(s)
- Keyur Radiya
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway.
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway.
| | - Henrik Lykke Joakimsen
- Institute of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
| | - Karl Øyvind Mikalsen
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Centre for Clinical Artificial Intelligence (SPKI), University Hospital of North Norway, Tromso, Norway
- UiT Machine Learning Group, Department of Physics and Technology, UiT the Arctic University of Norway, Tromso, Norway
| | - Eirik Kjus Aahlin
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
| | - Rolv-Ole Lindsetmo
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
- Head Clinic of Surgery, Oncology and Women Health, University Hospital of North Norway, Tromso, Norway
| | - Kim Erlend Mortensen
- Department of Gastroenterological Surgery at University Hospital of North Norway (UNN), Tromso, Norway
- Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway
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Alirr OI, Rahni AAA. Hepatic vessels segmentation using deep learning and preprocessing enhancement. J Appl Clin Med Phys 2023; 24:e13966. [PMID: 36933239 PMCID: PMC10161019 DOI: 10.1002/acm2.13966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 02/09/2023] [Accepted: 03/03/2023] [Indexed: 03/19/2023] Open
Abstract
PURPOSE Liver hepatic vessels segmentation is a crucial step for the diagnosis process in patients with hepatic diseases. Segmentation of liver vessels helps to study the liver internal segmental anatomy that helps in the preoperative planning of surgical treatment. METHODS Recently, the convolutional neural networks (CNN) have been proved to be efficient for the task of medical image segmentation. The paper proposes an automatic deep learning-based system for liver hepatic vessels segmentation of Computed Tomography (CT) datasets from different sources. The proposed work focuses on the combination of different steps; it starts by a preprocessing step to improve the vessels appearance within the liver region of interest in the CT scans. Coherence enhancing diffusion filtering (CED) and vesselness filtering methods are used to improve vessels contrast and intensity homogeneity. The proposed U-net based network architecture is implemented with modified residual block to include concatenation skip connection. The effect of enhancement using filtering step was studied. Also, the effect of data mismatch used in training and validation is studied. RESULTS The proposed method is evaluated using many CT datasets. Dice similarity coefficient (DSC) is used to evaluate the method. The average DSC score achieved a score 79%. CONCLUSIONS The proposed approach succeeded to segment liver vasculature from the liver envelope accurately, which makes it as potential tool for clinical preoperative planning.
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Affiliation(s)
- Omar Ibrahim Alirr
- College of Engineering and Technology, American University of the Middle East, Egaila, Kuwait
| | - Ashrani Aizzuddin Abd Rahni
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan, Bangi, Selangor, Malaysia
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6
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Wu R, Xin Y, Qian J, Dong Y. A multi-scale interactive U-Net for pulmonary vessel segmentation method based on transfer learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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7
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Robust deep 3-D architectures based on vascular patterns for liver vessel segmentation. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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8
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DV-Net: Accurate liver vessel segmentation via dense connection model with D-BCE loss function. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107471] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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9
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Alirr OI, Rahni AAA. Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters. J Digit Imaging 2021; 33:304-323. [PMID: 31428898 DOI: 10.1007/s10278-019-00262-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Preoperative planning for liver surgical treatments is an essential planning tool that aids in reducing the risks of surgical resection. Based on the computed tomography (CT) images, the resection can be planned before the actual tumour resection surgery. The computer-aided system provides an overview of the spatial relationships of the liver organ and its internal structures, tumours, and vasculature. It also allows for an accurate calculation of the remaining liver volume after resection. The aim of this paper was to review the main stages of the computer-aided system that helps to evaluate the risk of resection during liver cancer surgical treatments. The computer-aided system assists with surgical planning by enabling physicians to get volumetric measurements and visualise the liver, tumours, and surrounding vasculature. In this paper, it is concluded that for accurate planning of tumour resections, the liver organ and its internal structures should be segmented to understand the clear spatial relationship between them, thus allowing for a safer resection. This paper presents the main proposed segmentation techniques for each stage in the computer-aided system, namely the liver organ, tumours, and vessels. From the reviewed methods, it has been found that instead of relying on a single specific technique, a combination of a group of techniques would give more accurate segmentation results. The extracted masks from the segmentation algorithms are fused together to give the surgeons the 3D visualisation tool to study the spatial relationships of the liver and to calculate the required resection planning parameters.
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Affiliation(s)
- Omar Ibrahim Alirr
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia.
| | - Ashrani Aizzuddin Abd Rahni
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia
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10
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Xin M, Wen J, Wang Y, Yu W, Fang B, Hu J, Xu Y, Linghu C. Blood Vessel Segmentation Based on the 3D Residual U-Net. INT J PATTERN RECOGN 2021. [DOI: 10.1142/s021800142157007x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we propose blood vessel segmentation based on the 3D residual U-Net method. First, we integrate the residual block structure into the 3D U-Net. By exploring the influence of adding residual blocks at different positions in the 3D U-Net, we establish a novel and effective 3D residual U-Net. In addition, to address the challenges of pixel imbalance, vessel boundary segmentation, and small vessel segmentation, we develop a new weighted Dice loss function with a better effect than the weighted cross-entropy loss function. When training the model, we adopted a two-stage method from coarse-to-fine. In the fine stage, a local segmentation method of 3D sliding window is added. In the model testing phase, we used the 3D fixed-point method. Furthermore, we employ the 3D morphological closed operation to smooth the surfaces of vessels and volume analysis to remove noise blocks. To verify the accuracy and stability of our method, we compare our method with FCN, 3D DenseNet, and 3D U-Net. The experimental results indicate that our method has higher accuracy and better stability than the other studied methods and that the average Dice coefficients for hepatic veins and portal veins reach 71.7% and 76.5% in the coarse stage and 72.5% and 77.2% in the fine stage, respectively. In order to verify the robustness of the model, we conducted the same comparative experiment on the brain vessel datasets, and the average Dice coefficient reached 87.2%.
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Affiliation(s)
- Mulin Xin
- College of Computer Science, Chongqing University, Chongqing 401331, P. R. China
| | - Jing Wen
- College of Computer Science, Chongqing University, Chongqing 401331, P. R. China
| | - Yi Wang
- College of Computer Science, Chongqing University, Chongqing 401331, P. R. China
| | - Wei Yu
- College of Computer Science, Chongqing University, Chongqing 401331, P. R. China
| | - Bin Fang
- College of Computer Science, Chongqing University, Chongqing 401331, P. R. China
| | - Jun Hu
- Southwest Hospital, Army Military Medical University, Chongqing 401331, P. R. China
| | - Yongmei Xu
- Southwest Hospital, Army Military Medical University, Chongqing 401331, P. R. China
| | - Chunhong Linghu
- Southwest Hospital, Army Military Medical University, Chongqing 401331, P. R. China
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Abstract
Accurate liver vessel segmentation is of crucial importance for the clinical diagnosis and treatment of many hepatic diseases. Recent state-of-the-art methods for liver vessel reconstruction mostly utilize deep learning methods, namely, the U-Net model and its variants. However, to the best of our knowledge, no comparative evaluation has been proposed to compare these approaches in the liver vessel segmentation task. Moreover, most research works do not consider the liver volume segmentation as a preprocessing step, in order to keep only inner hepatic vessels, for Couinaud representation for instance. For these reasons, in this work, we propose using accurate Dense U-Net liver segmentation and conducting a comparison between 3D U-Net models inside the obtained volumes. More precisely, 3D U-Net, Dense U-Net, and MultiRes U-Net are pitted against each other in the vessel segmentation task on the IRCAD dataset. For each model, three alternative setups that allow adapting the selected CNN architectures to volumetric data are tested, namely, full 3D, slab-based, and box-based setups are considered. The results showed that the most accurate setup is the full 3D process, providing the highest Dice for most of the considered models. However, concerning the particular models, the slab-based MultiRes U-Net provided the best score. With our accurate vessel segmentations, several medical applications can be investigated, such as automatic and personalized Couinaud zoning of the liver.
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12
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Ciecholewski M, Kassjański M. Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review. SENSORS 2021; 21:s21062027. [PMID: 33809361 PMCID: PMC7999381 DOI: 10.3390/s21062027] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 03/07/2021] [Accepted: 03/09/2021] [Indexed: 12/12/2022]
Abstract
The segmentation of liver blood vessels is of major importance as it is essential for formulating diagnoses, planning and delivering treatments, as well as evaluating the results of clinical procedures. Different imaging techniques are available for application in clinical practice, so the segmentation methods should take into account the characteristics of the imaging technique. Based on the literature, this review paper presents the most advanced and effective methods of liver vessel segmentation, as well as their performance according to the metrics used. This paper includes results available for four imaging methods, namely: computed tomography (CT), computed tomography angiography (CTA), magnetic resonance (MR), and ultrasonography (USG). The publicly available datasets used in research are also presented. This paper may help researchers gain better insight into the available materials and methods, making it easier to develop new, more effective solutions, as well as to improve existing approaches. This article analyzes in detail various segmentation methods, which can be divided into three groups: active contours, tracking-based, and machine learning techniques. For each group of methods, their theoretical and practical characteristics are discussed, and the pros and cons are highlighted. The most advanced and promising approaches are also suggested. However, we conclude that liver vasculature segmentation is still an open problem, because of the various deficiencies and constraints researchers need to address and try to eliminate from the solutions used.
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CARNet: Automatic Cerebral Aneurysm Classification in Time-of-Flight MR Angiography by Leveraging Recurrent Neural Networks. ARTIF INTELL 2021. [DOI: 10.1007/978-3-030-93046-2_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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14
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Perera-Bel E, Ceresa M, Torrents-Barrena J, Masoller N, Valenzuela-Alcaraz B, Gratacós E, Eixarch E, González Ballester MA. Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning. Int J Comput Assist Radiol Surg 2020; 15:1869-1879. [PMID: 32951100 DOI: 10.1007/s11548-020-02256-2] [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: 01/20/2020] [Accepted: 09/02/2020] [Indexed: 10/23/2022]
Abstract
PURPOSE Twin-to-twin transfusion syndrome (TTTS) is a serious condition that occurs in about 10-15% of monochorionic twin pregnancies. In most instances, the blood flow is unevenly distributed throughout the placenta anastomoses leading to the death of both fetuses if no surgical procedure is performed. Fetoscopic laser coagulation is the optimal therapy to considerably improve co-twin prognosis by clogging the abnormal anastomoses. Notwithstanding progress in recent years, TTTS surgery is highly risky. Computer-assisted planning of the intervention can thus improve the outcome. METHODS In this work, we implement a GPU-accelerated random walker (RW) algorithm to detect the placenta, both umbilical cord insertions and the placental vasculature from Doppler ultrasound (US). Placenta and background seeds are manually initialized in 10-20 slices (out of 245). Vessels are automatically initialized in the same slices by means of Otsu thresholding. The RW finds the boundaries of the placenta and reconstructs the vasculature. RESULTS We evaluate our semiautomatic method in 5 monochorionic and 24 singleton pregnancies. Although satisfactory performance is achieved on placenta segmentation (Dice ≥ 84.0%), some vascular connections are still neglected due to the presence of US reverberation artifacts (Dice ≥ 56.9%). We also compared inter-user variability and obtained Dice coefficients of ≥ 76.8% and ≥ 97.42% for placenta and vasculature, respectively. After a 3-min manual initialization, our GPU approach speeds the computation 10.6 times compared to the CPU. CONCLUSIONS Our semiautomatic method provides a near real-time user experience and requires short training without compromising the segmentation accuracy. A powerful approach is thus presented to rapidly plan the fetoscope insertion point ahead of TTTS surgery.
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Affiliation(s)
- Enric Perera-Bel
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Mario Ceresa
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jordina Torrents-Barrena
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Narcís Masoller
- BCNatal, Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain
| | - Brenda Valenzuela-Alcaraz
- BCNatal, Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain
| | - Eduard Gratacós
- BCNatal, Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain
| | - Elisenda Eixarch
- BCNatal, Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), University of Barcelona, Barcelona, Spain.,Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centre for Biomedical Research on Rare Diseases (CIBER-ER), Madrid, Spain
| | - Miguel A González Ballester
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,ICREA, Barcelona, Spain
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Ibragimov B, Toesca DA, Chang DT, Yuan Y, Koong AC, Xing L. Automated hepatobiliary toxicity prediction after liver stereotactic body radiation therapy with deep learning-based portal vein segmentation. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2018.11.112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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16
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Şişik F, Sert E. Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware. Med Hypotheses 2019; 136:109507. [PMID: 31812927 DOI: 10.1016/j.mehy.2019.109507] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 11/11/2019] [Accepted: 11/16/2019] [Indexed: 01/20/2023]
Abstract
Automatic decision support systems have gained importance in health sector in recent years. In parallel with recent developments in the fields of artificial intelligence and image processing, embedded systems are also used in decision support systems for tumor diagnosis. Extreme learning machine (ELM), is a recently developed, quick and efficient algorithm which can quickly and flawlessly diagnose tumors using machine learning techniques. Similarly, significantly fast and robust fuzzy C-means clustering algorithm (FRFCM) is a novel and fast algorithm which can display a high performance. In the present study, a brain tumor segmentation approach is proposed based on extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms (BTS-ELM-FRFCM) running on Raspberry Pi (PRI) hardware. The present study mainly aims to introduce a new segmentation system hardware containing new algorithms and offering a high level of accuracy the health sector. PRI's are useful mobile devices due to their cost-effectiveness and satisfying hardware. 3200 training images were used to train ELM in the present study. 20 pieces of MRI images were used for testing process. Figure of merid (FOM), Jaccard similarity coefficient (JSC) and Dice indexes were used in order to evaluate the performance of the proposed approach. In addition, the proposed method was compared with brain tumor segmentation based on support vector machine (BTS-SVM), brain tumor segmentation based on fuzzy C-means (BTS-FCM) and brain tumor segmentation based on self-organizing maps and k-means (BTS-SOM). The statistical analysis on FOM, JSC and Dice results obtained using four different approaches indicated that BTS-ELM-FRFCM displayed the highest performance. Thus, it can be concluded that the embedded system designed in the present study can perform brain tumor segmentation with a high accuracy rate.
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Affiliation(s)
- Fatih Şişik
- Göksun Vocational School, Department of Computer Programming, Kahramanmaras Sutcu Imam University, K.Maras, Turkey
| | - Eser Sert
- Department of Computer Engineering, Engineering and Architecture Faculty, Kahramanmaras Sutcu Imam University, K.Maras, Turkey.
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3D Segmentation Algorithms for Computerized Tomographic Imaging: a Systematic Literature Review. J Digit Imaging 2019; 31:799-850. [PMID: 29915942 DOI: 10.1007/s10278-018-0101-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006-March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.
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18
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Andrushia AD, Thangarajan R. RTS-ELM: an approach for saliency-directed image segmentation with ripplet transform. Pattern Anal Appl 2019. [DOI: 10.1007/s10044-019-00800-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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19
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Nishida N, Yamakawa M, Shiina T, Kudo M. Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology. Hepatol Int 2019; 13:416-421. [PMID: 30790230 DOI: 10.1007/s12072-019-09937-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 02/02/2019] [Indexed: 12/13/2022]
Abstract
An ultrasound (US) examination is a common noninvasive technique widely applied for diagnosis of a variety of diseases. Based on the rapid development of US equipment, many US images have been accumulated and are now available and ready for the preparation of a database for the development of computer-aided US diagnosis with deep learning technology. On the contrary, because of the unique characteristics of the US image, there could be some issues that need to be resolved for the establishment of computer-aided diagnosis (CAD) system in this field. For example, compared to the other modalities, the quality of a US image is, currently, highly operator dependent; the conditions of examination should also directly affect the quality of US images. So far, these factors have hampered the application of deep learning-based technology in the field of US diagnosis. However, the development of CAD and US technologies will contribute to an increase in diagnostic quality, facilitate the development of remote medicine, and reduce the costs in the national health care through the early diagnosis of diseases. From this point of view, it may have a large enough potential to induce a paradigm shift in the field of US imaging and diagnosis of liver diseases.
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Affiliation(s)
- Naoshi Nishida
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, 337-2 Ohno-higashi, Osaka-sayama, Osaka, 589-8511, Japan.
| | - Makoto Yamakawa
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tsuyoshi Shiina
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masatoshi Kudo
- Department of Gastroenterology and Hepatology, Faculty of Medicine, Kindai University, 337-2 Ohno-higashi, Osaka-sayama, Osaka, 589-8511, Japan
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20
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An Improved Fuzzy Connectedness Method for Automatic Three-Dimensional Liver Vessel Segmentation in CT Images. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:2376317. [PMID: 30510670 PMCID: PMC6231381 DOI: 10.1155/2018/2376317] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 09/22/2018] [Accepted: 10/04/2018] [Indexed: 01/04/2023]
Abstract
In this paper, an improved fuzzy connectedness (FC) method was proposed for automatic three-dimensional (3D) liver vessel segmentation in computed tomography (CT) images. The vessel-enhanced image (i.e., vesselness image) was incorporated into the fuzzy affinity function of FC, rather than the intensity image used by traditional FC. An improved vesselness filter was proposed by incorporating adaptive sigmoid filtering and a background-suppressing item. The fuzzy scene of FC was automatically initialized by using the Otsu segmentation algorithm and one single seed generated adaptively, while traditional FC required multiple seeds. The improved FC method was evaluated on 40 cases of clinical CT volumetric images from the 3Dircadb (n=20) and Sliver07 (n=20) datasets. Experimental results showed that the proposed liver vessel segmentation strategy could achieve better segmentation performance than traditional FC, region growing, and threshold level set. Average accuracy, sensitivity, specificity, and Dice coefficient of the improved FC method were, respectively, (96.4 ± 1.1)%, (73.7 ± 7.6)%, (97.4 ± 1.3)%, and (67.3 ± 5.7)% for the 3Dircadb dataset and (96.8 ± 0.6)%, (89.1 ± 6.8)%, (97.6 ± 1.1)%, and (71.4 ± 7.6)% for the Sliver07 dataset. It was concluded that the improved FC may be used as a new method for automatic 3D segmentation of liver vessel from CT images.
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21
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Huang Q, Sun J, Ding H, Wang X, Wang G. Robust liver vessel extraction using 3D U-Net with variant dice loss function. Comput Biol Med 2018; 101:153-162. [DOI: 10.1016/j.compbiomed.2018.08.018] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 08/17/2018] [Accepted: 08/17/2018] [Indexed: 10/28/2022]
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22
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Zeng YZ, Zhao YQ, Liao SH, Liao M, Chen Y, Liu XY. Liver vessel segmentation based on centerline constraint and intensity model. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.035] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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Zeng YZ, Liao SH, Tang P, Zhao YQ, Liao M, Chen Y, Liang YX. Automatic liver vessel segmentation using 3D region growing and hybrid active contour model. Comput Biol Med 2018; 97:63-73. [PMID: 29709715 DOI: 10.1016/j.compbiomed.2018.04.014] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Revised: 04/20/2018] [Accepted: 04/20/2018] [Indexed: 01/02/2023]
Abstract
This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56 mm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms.
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Affiliation(s)
- Ye-Zhan Zeng
- School of Information Science and Engineering, Central South University, Changsha, 410083, China; Department of Biomedical Engineering, Central South University, Changsha, 410083, China
| | - Sheng-Hui Liao
- School of Information Science and Engineering, Central South University, Changsha, 410083, China.
| | - Ping Tang
- School of Information Science and Engineering, Central South University, Changsha, 410083, China; Department of Biomedical Engineering, Central South University, Changsha, 410083, China
| | - Yu-Qian Zhao
- School of Information Science and Engineering, Central South University, Changsha, 410083, China; Department of Biomedical Engineering, Central South University, Changsha, 410083, China.
| | - Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
| | - Yan Chen
- Applied Vision Research Centre, Loughborough University, Loughborough, UK
| | - Yi-Xiong Liang
- School of Information Science and Engineering, Central South University, Changsha, 410083, China
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24
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Ibragimov B, Toesca D, Chang D, Koong A, Xing L. Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning. Phys Med Biol 2017; 62:8943-8958. [PMID: 28994665 DOI: 10.1088/1361-6560/aa9262] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Automated segmentation of the portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated segmentation of the PV from computed tomography (CT) images. We apply convolutional neural networks (CNNs) to learn the consistent appearance patterns of the PV using a training set of CT images with reference annotations and then enhance the PV in previously unseen CT images. Markov random fields (MRFs) were further used to smooth the results of the enhancement of the CNN enhancement and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled for liver stereotactic body radiation therapy. The obtained accuracy of the segmentation was [Formula: see text] 0.83 and [Formula: see text] 1.08 mm in terms of the median Dice coefficient and mean symmetric surface distance, respectively, when segmentation is encompassed into the PV region of interest. The obtained results indicate that CNNs and anatomical analysis can be used for the accurate segmentation of the PV and potentially integrated into liver radiation therapy planning.
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Affiliation(s)
- Bulat Ibragimov
- Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Palo Alto, CA 94305, United States of America
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25
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Zeng YZ, Zhao YQ, Tang P, Liao M, Liang YX, Liao SH, Zou BJ. Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 150:31-39. [PMID: 28859828 DOI: 10.1016/j.cmpb.2017.07.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 06/26/2017] [Accepted: 07/18/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method. METHODS Firstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein. RESULTS The experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction. CONCLUSIONS The proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein.
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Affiliation(s)
- Ye-Zhan Zeng
- School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China
| | - Yu-Qian Zhao
- School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China.
| | - Ping Tang
- School of Information Science and Engineering, Central South University, Changsha 410083, China; Department of Biomedical Engineering, Central South University, Changsha 410083, China
| | - Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Yi-Xiong Liang
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Sheng-Hui Liao
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Bei-Ji Zou
- School of Information Science and Engineering, Central South University, Changsha 410083, China
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26
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Gui L, Li C, Yang X. Medical image segmentation based on level set and isoperimetric constraint. Phys Med 2017; 42:162-173. [PMID: 29173911 DOI: 10.1016/j.ejmp.2017.09.123] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 08/16/2017] [Accepted: 09/13/2017] [Indexed: 12/16/2022] Open
Abstract
Level set based methods are being increasingly used in image segmentation. In these methods, various shape constraints can be incorporated into the energy functionals to obtain the desired shapes of the contours represented by their zero level sets of functions. Motivated by the isoperimetric inequality in differential geometry, we propose a segmentation method in which the isoperimetric constrain is integrated into a level set framework to penalize the ratio of its squared perimeter to its enclosed area of an active contour. The new model can ensure the compactness of segmenting objects and complete missing or/and blurred parts of their boundaries simultaneously. The isoperimetric shape constraint is free of explicit expressions of shapes and scale-invariant. As a result, the proposed method can handle various objects with different scales and does not need to estimate parameters of shapes. Our method can segment lesions with blurred or/and partially missing boundaries in ultrasound, Computed Tomography (CT) and Magnetic Resonance (MR) images efficiently. Quantitative evaluation also confirms that the proposed method can provide more accurate segmentation than two well-known level set methods. Therefore, our proposed method shows potential of accurate segmentation of lesions for applying in diagnoses and surgical planning.
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Affiliation(s)
- Luying Gui
- The Department of Mathematics, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China.
| | - Chunming Li
- The School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
| | - Xiaoping Yang
- The Department of Mathematics, Nanjing University, Nanjing, Jiangsu 210093, China.
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27
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Liao M, Zhao YQ, Liu XY, Zeng YZ, Zou BJ, Wang XF, Shih FY. Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 143:1-12. [PMID: 28391807 DOI: 10.1016/j.cmpb.2017.02.015] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Revised: 01/24/2017] [Accepted: 02/09/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Identifying liver regions from abdominal computed tomography (CT) volumes is an important task for computer-aided liver disease diagnosis and surgical planning. This paper presents a fully automatic method for liver segmentation from CT volumes based on graph cuts and border marching. METHODS An initial slice is segmented by density peak clustering. Based on pixel- and patch-wise features, an intensity model and a PCA-based regional appearance model are developed to enhance the contrast between liver and background. Then, these models as well as the location constraint estimated iteratively are integrated into graph cuts in order to segment the liver in each slice automatically. Finally, a vessel compensation method based on the border marching is used to increase the segmentation accuracy. RESULTS Experiments are conducted on a clinical data set we created and also on the MICCAI2007 Grand Challenge liver data. The results show that the proposed intensity, appearance models, and the location constraint are significantly effective for liver recognition, and the undersegmented vessels can be compensated by the border marching based method. The segmentation performances in terms of VOE, RVD, ASD, RMSD, and MSD as well as the average running time achieved by our method on the SLIVER07 public database are 5.8 ± 3.2%, -0.1 ± 4.1%, 1.0 ± 0.5mm, 2.0 ± 1.2mm, 21.2 ± 9.3mm, and 4.7 minutes, respectively, which are superior to those of existing methods. CONCLUSIONS The proposed method does not require time-consuming training process and statistical model construction, and is capable of dealing with complicated shapes and intensity variations successfully.
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Affiliation(s)
- Miao Liao
- School of Information Science and Engineering, Central South University, Changsha 410083, China; School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
| | - Yu-Qian Zhao
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Xi-Yao Liu
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Ye-Zhan Zeng
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Bei-Ji Zou
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Xiao-Fang Wang
- Department of Mathematics and Computer Science, École centrale de Lyon, Écully, France
| | - Frank Y Shih
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
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28
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Xiao R, Ding H, Zhai F, Zhao T, Zhou W, Wang G. Vascular segmentation of head phase-contrast magnetic resonance angiograms using grayscale and shape features. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 142:157-166. [PMID: 28325443 DOI: 10.1016/j.cmpb.2017.02.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 01/24/2017] [Accepted: 02/09/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE In neurosurgery planning, vascular structures must be predetermined, which can guarantee the security of the operation carried out in the case of avoiding blood vessels. In this paper, an automatic algorithm of vascular segmentation, which combined the grayscale and shape features of the blood vessels, is proposed to extract 3D vascular structures from head phase-contrast magnetic resonance angiography dataset. METHODS First, a cost function of mis-segmentation is introduced on the basis of traditional Bayesian statistical classification, and the blood vessel of weak grayscale that tended to be misclassified into background will be preserved. Second, enhanced vesselness image is obtained according to the shape-based multiscale vascular enhancement filter. Third, a new reconstructed vascular image is established according to the fusion of vascular grayscale and shape features using Dempster-Shafer evidence theory; subsequently, the corresponding segmentation structures are obtained. Finally, according to the noise distribution characteristic of the data, segmentation ratio coefficient, which increased linearly from top to bottom, is proposed to control the segmentation result, thereby preventing over-segmentation. RESULTS Experiment results show that, through the proposed method, vascular structures can be detected not only when both grayscale and shape features are strong, but also when either of them is strong. Compared with traditional grayscale feature- and shape feature-based methods, it is better in the evaluation of testing in segmentation accuracy, and over-segmentation and under-segmentation ratios. CONCLUSIONS The proposed grayscale and shape features combined vascular segmentation is not only effective but also accurate. It may be used for diagnosis of vascular diseases and planning of neurosurgery.
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Affiliation(s)
- Ruoxiu Xiao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China
| | - Hui Ding
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China
| | - Fangwen Zhai
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China
| | - Tong Zhao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China
| | - Wenjing Zhou
- Tsinghua University Yuquan Hospital, No. 5, Shijingshan Road, Shijingshan District, Beijing, 100049, China
| | - Guangzhi Wang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Room C249, Beijing 100084, China.
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29
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Liao M, Zhao YQ, Wang W, Zeng YZ, Yang Q, Shih FY, Zou BJ. Efficient liver segmentation in CT images based on graph cuts and bottleneck detection. Phys Med 2016; 32:1383-1396. [PMID: 27771278 DOI: 10.1016/j.ejmp.2016.10.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Revised: 10/05/2016] [Accepted: 10/05/2016] [Indexed: 12/20/2022] Open
Abstract
Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes. An intensity model is combined with a principal component analysis (PCA) based appearance model to exclude complex background and highlight liver region. They are then integrated with location information from neighboring slices into graph cuts to segment the liver in each slice automatically. Finally, a boundary refinement method based on bottleneck detection is used to increase the segmentation accuracy. Our method does not require heavy training process or statistical model construction, and is capable of dealing with complicated shape and intensity variations. We apply the proposed method on XHCSU14 and SLIVER07 databases, and evaluate it by MICCAI criteria and Dice similarity coefficient. Experimental results show our method outperforms several existing methods on liver segmentation.
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Affiliation(s)
- Miao Liao
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Yu-Qian Zhao
- School of Information Science and Engineering, Central South University, Changsha 410083, China.
| | - Wei Wang
- The Third Xiangya Hospital, Central South University, Changsha 410083, China.
| | - Ye-Zhan Zeng
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Qing Yang
- School of Information Science and Engineering, Central South University, Changsha 410083, China
| | - Frank Y Shih
- College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Bei-Ji Zou
- School of Information Science and Engineering, Central South University, Changsha 410083, China
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