1
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Zhang Q, Li J, Nan X, Zhang X. Detail-sensitive 3D-UNet for pulmonary airway segmentation from CT images. Med Biol Eng Comput 2024:10.1007/s11517-024-03169-x. [PMID: 39017831 DOI: 10.1007/s11517-024-03169-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 07/06/2024] [Indexed: 07/18/2024]
Abstract
The segmentation of airway from computed tomography (CT) images plays a vital role in pulmonary disease diagnosis, evaluation, surgical planning, and treatment. Nevertheless, it is still challenging for current methods to handle distal thin and low-contrast airways, leading to mis-segmentation issues. This paper proposes a detail-sensitive 3D-UNet (DS-3D-UNet) that incorporates two new modules into 3D-UNet to segment airways accurately from CT images. The feature recalibration module is designed to give more attention to the foreground airway features through a new attention mechanism. The detail extractor module aims to restore multi-scale detailed features by fusion of features at different levels. Extensive experiments were conducted on the ATM'22 challenge dataset composed of 300 CT scans with airway annotations to evaluate its performance. Quantitative comparisons prove that the proposed model achieves the best performance in terms of Dice similarity coefficient (92.6%) and Intersection over Union (86.3%), outperforming other state-of-the-art methods. Qualitative comparisons further exhibit the superior performance of our method in segmenting thin and confused distal bronchi. The proposed model could provide important references for the diagnosis and treatment of pulmonary diseases, holding promising prospects in the field of digital medicine. Codes are available at https://github.com/nighlevil/DS-3D-UNet/tree/master .
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Affiliation(s)
- Qin Zhang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510000, China
| | - Jiajie Li
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510000, China
| | - Xiangling Nan
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510000, China
| | - Xiaodong Zhang
- Shenzhen Children's Hospital, Shenzhen, Guangdong, 518000, China.
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518000, China.
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2
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Yuan Y, Tan W, Xu L, Bao N, Zhu Q, Wang Z, Wang R. An end-to-end multi-scale airway segmentation framework based on pulmonary CT image. Phys Med Biol 2024; 69:115027. [PMID: 38657624 DOI: 10.1088/1361-6560/ad4300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 04/24/2024] [Indexed: 04/26/2024]
Abstract
Objective. Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D + 3D framework for multi-scale airway tree segmentation.Approach. In stage 1, we use a 2D full airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale atros spatial pyramid and Atros Residual Skip connection modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 asa prioriinformation. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added false positive losses and false negative losses to improve the segmentation performance of airway branches within the lungs.Main results. We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest dice similarity coefficient (DSC) of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other state-of-the-art methods to increase detected tree length rate by up to 46.33% and detected tree branch rate by up to 42.97%.Significance. The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales.
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Affiliation(s)
- Ye Yuan
- College of Computer Science and Engineering, Northeastern University, People's Republic of China
- Key Research Laboratory of Intelligent Computing of Medical Images, Ministry of Education, Northeastern University, People's Republic of China
| | - Wenjun Tan
- College of Computer Science and Engineering, Northeastern University, People's Republic of China
- Key Research Laboratory of Intelligent Computing of Medical Images, Ministry of Education, Northeastern University, People's Republic of China
| | - Lisheng Xu
- College of Medicine and Biological information Engineering, Northeastern University, People's Republic of China
| | - Nan Bao
- College of Medicine and Biological information Engineering, Northeastern University, People's Republic of China
| | - Quan Zhu
- The First Affiliated Hospital of Nanjing Medical University, People's Republic of China
| | - Zhe Wang
- Affiliated Zhongshan Hospital of Dalian University, People's Republic of China
| | - Ruoyu Wang
- Affiliated Zhongshan Hospital of Dalian University, People's Republic of China
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3
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Hu Y, Du S, Xu T, Lei Y. A novel computational fracture toughness model for soft tissue in needle insertion. J Mech Behav Biomed Mater 2023; 147:106129. [PMID: 37774443 DOI: 10.1016/j.jmbbm.2023.106129] [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: 03/21/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 10/01/2023]
Abstract
During the process of percutaneous puncture vascular intervention operation in endoscopic liver surgery, high precision needle manipulation requires the accurate needle tissue interaction model where the tissue fracture toughness is an important parameter to describe the tissue crack propagation, as well as to estimate tissue deformation and target displacement. However, the existing studies on fracture toughness estimation did not consider Young's modulus and the organ capsule structure. In this paper, a novel computational fracture toughness model is proposed considering insertion velocity, needle diameter and Young's modulus in insertion process, where the fracture toughness is determined by the tissue surface deformation, which was estimated through energy modeling using integrated shell element and three-dimensional solid element. The testbed is built to study the effect of different insertion velocities, needle diameters and Young's modulus on fracture toughness. The experiment result shows that the estimated result of computational fracture toughness model agrees well with the physical experimental data. In addition, the sensitivity analysis of different factors is conducted. Meanwhile, the model robustness analysis is investigated with different observation noises of Young's modulus and puncture displacement.
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Affiliation(s)
- Yingda Hu
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, Zhejiang, China
| | - Shilun Du
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, Zhejiang, China
| | - Tian Xu
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, Zhejiang, China
| | - Yong Lei
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, Zhejiang, China.
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4
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Saha PK, Nadeem SA, Comellas AP. A Survey on Artificial Intelligence in Pulmonary Imaging. WILEY INTERDISCIPLINARY REVIEWS. DATA MINING AND KNOWLEDGE DISCOVERY 2023; 13:e1510. [PMID: 38249785 PMCID: PMC10796150 DOI: 10.1002/widm.1510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 06/21/2023] [Indexed: 01/23/2024]
Abstract
Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.
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Affiliation(s)
- Punam K Saha
- Departments of Radiology and Electrical and Computer Engineering, University of Iowa, Iowa City, IA, 52242
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5
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Wu Y, Zhao S, Qi S, Feng J, Pang H, Chang R, Bai L, Li M, Xia S, Qian W, Ren H. Two-stage contextual transformer-based convolutional neural network for airway extraction from CT images. Artif Intell Med 2023; 143:102637. [PMID: 37673569 DOI: 10.1016/j.artmed.2023.102637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 06/14/2023] [Accepted: 08/11/2023] [Indexed: 09/08/2023]
Abstract
Accurate airway segmentation from computed tomography (CT) images is critical for planning navigation bronchoscopy and realizing a quantitative assessment of airway-related chronic obstructive pulmonary disease (COPD). Existing methods face difficulty in airway segmentation, particularly for the small branches of the airway. These difficulties arise due to the constraints of limited labeling and failure to meet clinical use requirements in COPD. We propose a two-stage framework with a novel 3D contextual transformer for segmenting the overall airway and small airway branches using CT images. The method consists of two training stages sharing the same modified 3D U-Net network. The novel 3D contextual transformer block is integrated into both the encoder and decoder path of the network to effectively capture contextual and long-range information. In the first training stage, the proposed network segments the overall airway with the overall airway mask. To improve the performance of the segmentation result, we generate the intrapulmonary airway branch label, and train the network to focus on producing small airway branches in the second training stage. Extensive experiments were performed on in-house and multiple public datasets. Quantitative and qualitative analyses demonstrate that our proposed method extracts significantly more branches and longer lengths of the airway tree while accomplishing state-of-the-art airway segmentation performance. The code is available at https://github.com/zhaozsq/airway_segmentation.
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Affiliation(s)
- Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
| | - Shuiqing Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Jie Feng
- School of Chemical Equipment, Shenyang University of Technology, Liaoyang, China.
| | - Haowen Pang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Runsheng Chang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Long Bai
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
| | - Mengqi Li
- Department of Respiratory, the Second Affiliated Hospital of Dalian Medical University, Dalian, China.
| | - Shuyue Xia
- Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China.
| | - Wei Qian
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
| | - Hongliang Ren
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China.
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6
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Li H, Tang Z, Nan Y, Yang G. Human treelike tubular structure segmentation: A comprehensive review and future perspectives. Comput Biol Med 2022; 151:106241. [PMID: 36379190 DOI: 10.1016/j.compbiomed.2022.106241] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/16/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.
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Affiliation(s)
- Hao Li
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Zeyu Tang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Yang Nan
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Guang Yang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Royal Brompton Hospital, London, United Kingdom.
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7
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Wu Y, Zhang M, Yu W, Zheng H, Xu J, Gu Y. LTSP: long-term slice propagation for accurate airway segmentation. Int J Comput Assist Radiol Surg 2022; 17:857-865. [PMID: 35294715 PMCID: PMC8924579 DOI: 10.1007/s11548-022-02582-7] [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: 01/19/2022] [Accepted: 02/18/2022] [Indexed: 11/28/2022]
Abstract
Purpose: Bronchoscopic intervention is a widely used clinical technique for pulmonary diseases, which requires an accurate and topological complete airway map for its localization and guidance. The airway map could be extracted from chest computed tomography (CT) scans automatically by airway segmentation methods. Due to the complex tree-like structure of the airway, preserving its topology completeness while maintaining the segmentation accuracy is a challenging task. Methods: In this paper, a long-term slice propagation (LTSP) method is proposed for accurate airway segmentation from pathological CT scans. We also design a two-stage end-to-end segmentation framework utilizing the LTSP method in the decoding process. Stage 1 is used to generate a coarse feature map by an encoder–decoder architecture. Stage 2 is to adopt the proposed LTSP method for exploiting the continuity information and enhancing the weak airway features in the coarse feature map. The final segmentation result is predicted from the refined feature map. Results: Extensive experiments were conducted to evaluate the performance of the proposed method on 70 clinical CT scans. The results demonstrate the considerable improvements of the proposed method compared to some state-of-the-art methods as most breakages are eliminated and more tiny bronchi are detected. The ablation studies further confirm the effectiveness of the constituents of the proposed method and the efficacy of the framework design. Conclusion: Slice continuity information is beneficial to accurate airway segmentation. Furthermore, by propagating the long-term slice feature, the airway topology connectivity is preserved with overall segmentation accuracy maintained.
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Affiliation(s)
- Yangqian Wu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.,Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - Minghui Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.,Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - Weihao Yu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.,Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - Hao Zheng
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.,Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - Jiasheng Xu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China.,Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - Yun Gu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China. .,Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China. .,Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China.
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8
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Garcia-Uceda A, Selvan R, Saghir Z, Tiddens HAWM, de Bruijne M. Automatic airway segmentation from computed tomography using robust and efficient 3-D convolutional neural networks. Sci Rep 2021; 11:16001. [PMID: 34362949 PMCID: PMC8346579 DOI: 10.1038/s41598-021-95364-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/21/2021] [Indexed: 12/11/2022] Open
Abstract
This paper presents a fully automatic and end-to-end optimised airway segmentation method for thoracic computed tomography, based on the U-Net architecture. We use a simple and low-memory 3D U-Net as backbone, which allows the method to process large 3D image patches, often comprising full lungs, in a single pass through the network. This makes the method simple, robust and efficient. We validated the proposed method on three datasets with very different characteristics and various airway abnormalities: (1) a dataset of pediatric patients including subjects with cystic fibrosis, (2) a subset of the Danish Lung Cancer Screening Trial, including subjects with chronic obstructive pulmonary disease, and (3) the EXACT'09 public dataset. We compared our method with other state-of-the-art airway segmentation methods, including relevant learning-based methods in the literature evaluated on the EXACT'09 data. We show that our method can extract highly complete airway trees with few false positive errors, on scans from both healthy and diseased subjects, and also that the method generalizes well across different datasets. On the EXACT'09 test set, our method achieved the second highest sensitivity score among all methods that reported good specificity.
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Affiliation(s)
- Antonio Garcia-Uceda
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 CE, Rotterdam, The Netherlands.
- Department of Pediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, 3015 CE, Rotterdam, The Netherlands.
| | - Raghavendra Selvan
- Department of Computer Science, University of Copenhagen, 2100, Copenhagen, Denmark
- Department of Neuroscience, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Zaigham Saghir
- Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Copenhagen University Hospital, 2900, Hellerup, Denmark
| | - Harm A W M Tiddens
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 CE, Rotterdam, The Netherlands
- Department of Pediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, 3015 CE, Rotterdam, The Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 CE, Rotterdam, The Netherlands.
- Department of Computer Science, University of Copenhagen, 2100, Copenhagen, Denmark.
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9
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Nadeem SA, Hoffman EA, Sieren JC, Comellas AP, Bhatt SP, Barjaktarevic IZ, Abtin F, Saha PK. A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:405-418. [PMID: 33021934 PMCID: PMC7772272 DOI: 10.1109/tmi.2020.3029013] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating intervention outcomes. Reliable, fully automated, and accurate segmentation of pulmonary airway trees is critical to such exploration. We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evaluated and compared with results from several state-of-the-art methods including an industry-standard one, where segmentation results were manually reviewed and corrected. Both new algorithms show a reproducibility of 95% or higher for total lung capacity (TLC) repeat CT scans. Experiments on TLC CT scans from different imaging sites at standard and low radiation dosages show that both new algorithms outperform the other methods in terms of leakages and branch-level accuracy. Considering the performance and execution times, the deep learning-based FG algorithm is a fully automated option for large multi-site studies.
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10
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Duan HH, Gong J, Sun XW, Nie SD. Region growing algorithm combined with morphology and skeleton analysis for segmenting airway tree in CT images. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2020; 28:311-331. [PMID: 32039883 DOI: 10.3233/xst-190627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
BACKGROUND Automatic segmentation of pulmonary airway tree is a challenging task in many clinical applications, including developing computer-aided detection and diagnosis schemes of lung diseases. OBJECTIVE To segment the pulmonary airway tree from the computed tomography (CT) chest images using a novel automatic method proposed in this study. METHODS This method combines a two-pass region growing algorithm with gray-scale morphological reconstruction and leakage elimination. The first-pass region growing is implemented to obtain a rough airway tree. The second-pass region growing and gray-scale morphological reconstruction are used to detect the distal airways. Finally, leakage detection is performed to remove leakage and refine the airway tree. RESULTS Our methods were compared with the gold standards. Forty-five clinical CT lung image scan cases were used in the experiments. Statistics on tree division order, branch number, and airway length were adopted for evaluation. The proposed method detected up to 12 generations of bronchi. On average, 148.85 branches were extracted with a false positive rate of 0.75%. CONCLUSIONS The results show that our method is accurate for pulmonary airway tree segmentation. The strategy of separating the leakage detection from the segmenting process is feasible and promising for ensuring a high branch detected rate with a low leakage volume.
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Affiliation(s)
- Hui-Hong Duan
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Jing Gong
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xi-Wen Sun
- Department of Radiology of Shanghai Pulmonary Hospital, ZhengMin Road, YangPu District, Shanghai, China
| | - Sheng-Dong Nie
- School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai, China
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11
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Shammi UA, Thomen RP. Role of New Imaging Capabilities with MRI and CT in the Evaluation of Bronchiectasis. CURRENT PULMONOLOGY REPORTS 2019. [DOI: 10.1007/s13665-019-00240-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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12
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Yun J, Park J, Yu D, Yi J, Lee M, Park HJ, Lee JG, Seo JB, Kim N. Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net. Med Image Anal 2018; 51:13-20. [PMID: 30388500 DOI: 10.1016/j.media.2018.10.006] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 10/08/2018] [Accepted: 10/18/2018] [Indexed: 12/01/2022]
Abstract
We propose a novel airway segmentation method in volumetric chest computed tomography (CT) and evaluate its performance on multiple datasets. The segmentation is performed voxel-by-voxel by a 2.5D convolutional neural net (2.5D CNN) trained in a supervised manner. To enhance the accuracy of the segmented airway tree, we simultaneously took three adjacent slices in each of the orthogonal directions including axial, sagittal, and coronal and fine-tuned the parameters that influence the tree length and the number of leakage. The gold standard of airway segmentation was generated by a semi-automated method using AVIEW™. The 2.5D CNN was trained and evaluated on a subset of inspiratory thoracic CT scans taken from the Korean obstructive lung disease study, which includes normal subjects and chronic obstructive pulmonary disease patients. The reliability and further practicality of our proposed method was demonstrated in multiple datasets. In eight test datasets collected by the same imaging protocol, the percentage detected tree length, false positive rate, and Dice similarity coefficient of our method were 92.16%, 7.74%, and 0.8997 ± 0.0892, respectively. In 20 test datasets of the EXACT'09 challenge, the percentage detected tree length was 60.1% and the false positive rate was 4.56%. Our fully automated (end-to-end) segmentation method could be applied in radiologic practice.
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Affiliation(s)
- Jihye Yun
- Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea
| | - Jinkon Park
- Coreline Soft, Co., Ltd., Sung-Myung B/D 5F, World Cup buk-ro 6-gil 49, Mapo-Gu, Seoul 03991, South Korea
| | - Donghoon Yu
- Coreline Soft, Co., Ltd., Sung-Myung B/D 5F, World Cup buk-ro 6-gil 49, Mapo-Gu, Seoul 03991, South Korea
| | - Jaeyoun Yi
- Coreline Soft, Co., Ltd., Sung-Myung B/D 5F, World Cup buk-ro 6-gil 49, Mapo-Gu, Seoul 03991, South Korea
| | - Minho Lee
- Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea
| | - Hee Jun Park
- Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea
| | - June-Goo Lee
- Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea
| | - Namkug Kim
- Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea.
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13
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Improving airway segmentation in computed tomography using leak detection with convolutional networks. Med Image Anal 2017; 36:52-60. [DOI: 10.1016/j.media.2016.11.001] [Citation(s) in RCA: 65] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 10/28/2016] [Accepted: 11/03/2016] [Indexed: 11/21/2022]
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14
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Karami E, Wang Y, Gaede S, Lee TY, Samani A. Anatomy-based algorithm for automatic segmentation of human diaphragm in noncontrast computed tomography images. J Med Imaging (Bellingham) 2016; 3:046004. [PMID: 27921072 DOI: 10.1117/1.jmi.3.4.046004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 10/28/2016] [Indexed: 11/14/2022] Open
Abstract
In-depth understanding of the diaphragm's anatomy and physiology has been of great interest to the medical community, as it is the most important muscle of the respiratory system. While noncontrast four-dimensional (4-D) computed tomography (CT) imaging provides an interesting opportunity for effective acquisition of anatomical and/or functional information from a single modality, segmenting the diaphragm in such images is very challenging not only because of the diaphragm's lack of image contrast with its surrounding organs but also because of respiration-induced motion artifacts in 4-D CT images. To account for such limitations, we present an automatic segmentation algorithm, which is based on a priori knowledge of diaphragm anatomy. The novelty of the algorithm lies in using the diaphragm's easy-to-segment contacting organs-including the lungs, heart, aorta, and ribcage-to guide the diaphragm's segmentation. Obtained results indicate that average mean distance to the closest point between diaphragms segmented using the proposed technique and corresponding manual segmentation is [Formula: see text], which is favorable. An important feature of the proposed technique is that it is the first algorithm to delineate the entire diaphragm. Such delineation facilitates applications, where the diaphragm boundary conditions are required such as biomechanical modeling for in-depth understanding of the diaphragm physiology.
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Affiliation(s)
- Elham Karami
- Western University, Department of Medical Biophysics, Medical Sciences Building, London, Ontario N6A 5C1, Canada; Robarts Research Institute, Imaging Research Laboratories, 1151 Richmond Street, London, Ontario N6A 5B7, Canada
| | - Yong Wang
- Robarts Research Institute , Imaging Research Laboratories, 1151 Richmond Street, London, Ontario N6A 5B7, Canada
| | - Stewart Gaede
- Western University, Department of Medical Biophysics, Medical Sciences Building, London, Ontario N6A 5C1, Canada; London Regional Cancer Program, Department of Physics and Engineering, 800 Commissioners R E, London, Ontario N6A 5W9, Canada; Western University, Department of Oncology, 790 Commissioners Road East, London, Ontario N6A 4L6, Canada
| | - Ting-Yim Lee
- Western University, Department of Medical Biophysics, Medical Sciences Building, London, Ontario N6A 5C1, Canada; Robarts Research Institute, Imaging Research Laboratories, 1151 Richmond Street, London, Ontario N6A 5B7, Canada; Lawson Health Research Institute, Imaging Program, 268 Grosvenor Street, London, Ontario N6A 4V2, Canada; Western University, Department of Medical Imaging, London Health Sciences Centre, Victoria Hospital, London, Ontario N6A 5W9, Canada
| | - Abbas Samani
- Western University, Department of Medical Biophysics, Medical Sciences Building, London, Ontario N6A 5C1, Canada; Robarts Research Institute, Imaging Research Laboratories, 1151 Richmond Street, London, Ontario N6A 5B7, Canada; Western University, Department of Electrical and Computer Engineering, Thompson Engineering Building, London, Ontario N6A 5B9, Canada; Western University, Graduate Program in Biomedical Engineering, Claudette MacKay Lassonde Pavilion, London, Ontario N6A 5B9, Canada
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15
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Pu J, Jin C, Yu N, Qian Y, Wang X, Meng X, Guo Y. A "loop" shape descriptor and its application to automated segmentation of airways from CT scans. Med Phys 2015; 42:3076-84. [PMID: 26127059 DOI: 10.1118/1.4921139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
PURPOSE A novel shape descriptor is presented to aid an automated identification of the airways depicted on computed tomography (CT) images. METHODS Instead of simplifying the tubular characteristic of the airways as an ideal mathematical cylindrical or circular shape, the proposed "loop" shape descriptor exploits the fact that the cross sections of any tubular structure (regardless of its regularity) always appear as a loop. In implementation, the authors first reconstruct the anatomical structures in volumetric CT as a three-dimensional surface model using the classical marching cubes algorithm. Then, the loop descriptor is applied to locate the airways with a concave loop cross section. To deal with the variation of the airway walls in density as depicted on CT images, a multiple threshold strategy is proposed. A publicly available chest CT database consisting of 20 CT scans, which was designed specifically for evaluating an airway segmentation algorithm, was used for quantitative performance assessment. Measures, including length, branch count, and generations, were computed under the aid of a skeletonization operation. RESULTS For the test dataset, the airway length ranged from 64.6 to 429.8 cm, the generation ranged from 7 to 11, and the branch number ranged from 48 to 312. These results were comparable to the performance of the state-of-the-art algorithms validated on the same dataset. CONCLUSIONS The authors' quantitative experiment demonstrated the feasibility and reliability of the developed shape descriptor in identifying lung airways.
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Affiliation(s)
- Jiantao Pu
- Department of Radiology, First Affiliated Hospital of Medical College, Xi'an Jiaotong University, Shaanxi 710061, People's Republic of China, and Departments of Radiology and Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Chenwang Jin
- Department of Radiology, First Affiliated Hospital of Medical College, Xi'an Jiaotong University, Shaanxi 710061, People's Republic of China
| | - Nan Yu
- Department of Radiology, First Affiliated Hospital of Medical College, Xi'an Jiaotong University, Shaanxi 710061, People's Republic of China
| | - Yongqiang Qian
- Department of Radiology, First Affiliated Hospital of Medical College, Xi'an Jiaotong University, Shaanxi 710061, People's Republic of China
| | - Xiaohua Wang
- Third Affiliated Hospital, Peking University, Beijing, People's Republic of China, 100029
| | - Xin Meng
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15213
| | - Youmin Guo
- Department of Radiology, First Affiliated Hospital of Medical College, Xi'an Jiaotong University, Shaanxi 710061, People's Republic of China
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16
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Doel T, Gavaghan DJ, Grau V. Review of automatic pulmonary lobe segmentation methods from CT. Comput Med Imaging Graph 2015; 40:13-29. [PMID: 25467805 DOI: 10.1016/j.compmedimag.2014.10.008] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Revised: 10/11/2014] [Accepted: 10/15/2014] [Indexed: 11/17/2022]
Abstract
The computational detection of pulmonary lobes from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. Several authors have proposed automated algorithms and we present a methodological review. These algorithms share a number of common stages and we consider each stage in turn, comparing the methods applied by each author and discussing their relative strengths. No standard method has yet emerged and none of the published methods have been demonstrated across a full range of clinical pathologies and imaging protocols. We discuss how improved methods could be developed by combining different approaches, and we use this to propose a workflow for the development of new algorithms.
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Affiliation(s)
- Tom Doel
- Department of Computer Science, University of Oxford, Oxford, UK.
| | - David J Gavaghan
- Department of Computer Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering Science and Oxford e-Research Centre, University of Oxford, Oxford, UK
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17
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18
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Rosell J, Cabras P. A three-stage method for the 3D reconstruction of the tracheobronchial tree from CT scans. Comput Med Imaging Graph 2013; 37:430-7. [PMID: 23981684 DOI: 10.1016/j.compmedimag.2013.07.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2013] [Revised: 07/23/2013] [Accepted: 07/25/2013] [Indexed: 11/24/2022]
Abstract
This paper proposes a method for segmenting the airways from CT scans of the chest to obtain a 3D model that can be used in the virtual bronchoscopy for the exploration and the planning of paths to the lesions. The method is composed of 3 stages: a gross segmentation that reconstructs the main airway tree using adaptive region growing, a finer segmentation that identifies any potential airway region based on a 2D process that enhances bronchi walls using local information, and a final process to connect any isolated bronchus to the main airways using a morphologic reconstruction process and a path planning technique. The paper includes two examples for the evaluation and discussion of the proposal.
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Affiliation(s)
- Jan Rosell
- Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
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19
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Counter WB, Wang IQ, Farncombe TH, Labiris NR. Airway and pulmonary vascular measurements using contrast-enhanced micro-CT in rodents. Am J Physiol Lung Cell Mol Physiol 2013; 304:L831-43. [DOI: 10.1152/ajplung.00281.2012] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Preclinical imaging allows pulmonary researchers to study lung disease and pulmonary drug delivery noninvasively and longitudinally in small animals. However, anatomically localizing a pathology or drug deposition to a particular lung region is not easily done. Thus, a detailed knowledge of the anatomical structure of small animal lungs is necessary for understanding disease progression and in addition would facilitate the analysis of the imaging data, mapping drug deposition and relating function to structure. In this study, contrast-enhanced micro-computed tomography (CT) of the lung produced high-resolution images that allowed for the characterization of the rodent airway and pulmonary vasculature. Contrast-enhanced micro-CT was used to visualize the airways and pulmonary vasculature in Sprague-Dawley rats (200–225 g) and BALB/c mice (20–25 g) postmortem. Segmented volumes from these images were processed to yield automated measurements of the airways and pulmonary vasculature. The diameters, lengths, and branching angles of the airway, arterial, and venous trees were measured and analyzed as a function of generation number and vessel diameter to establish rules that could be applied at all levels of tree hierarchy. In the rat, airway, arterial, and venous tress were measured down to the 20th, 16th, and 14th generation, respectively. In the mouse, airway, arterial, and venous trees were measured down to the 16th, 8th, and 7th generation, respectively. This structural information, catalogued in a rodent database, will increase our understanding of lung structure and will aid in future studies of the relationship between structure and function in animal models of disease.
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Affiliation(s)
- W. B. Counter
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Medical Physics, McMaster University, Hamilton, Ontario, Canada
| | - I. Q. Wang
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
| | - T. H. Farncombe
- Department of Radiology, McMaster University, Hamilton, Ontario, Canada; and
- Department of Nuclear Medicine, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - N. R. Labiris
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Department of Nuclear Medicine, Hamilton Health Sciences, McMaster University, Hamilton, Ontario, Canada
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20
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Mesanovic N, Huseinagic H, Mujagic S. 3D TRACHEOBRONCHIAL AIRWAY TREE SEGMENTATION FROM THORAX CT IMAGES. BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2013. [DOI: 10.4015/s1016237213500154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Segmentation of the lung structures is an important operation in the medical analysis. This paper is proposing a region growing algorithm for airway segmentation. The proposed method for the airway tree segmentation works fully in 3D and performs the measurements in the original gray-scale volume for increased accuracy and efficiency. This algorithm uses region growing and morphological operators. The airway segmentation algorithm is intended to serve qualitative and quantitative purposes, and additional three descriptors are being used for evaluation of the airway segmentation. The proposed method was evaluated using the database of 15 patients who underwent lung CT scans, with varying image quality and anatomical changes. Overlap measure is used to show the difference between measured volumes from the established gold standard and the proposed method. The student t-test and Pearson test showed high correlation of the results with the gold standard. Overall, the test results were satisfactory since accurate segmentation was achieved in 95% of the patients.
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Affiliation(s)
- Nihad Mesanovic
- IT Sector, University Clinical Centre, Trnovac bb, 75000 Tuzla, Bosnia and Herzegovina
| | - Haris Huseinagic
- Department of Radiology and Nuclear Medicine, University Clinical Centre, Trnovac bb, 75000 Tuzla, Bosnia and Herzegovina
| | - Svjetlana Mujagic
- Department of Radiology and Nuclear Medicine, University Clinical Centre, Trnovac bb, 75000 Tuzla, Bosnia and Herzegovina
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21
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Ji DX, Foong KWC, Ong SH. A two-stage rule-constrained seedless region growing approach for mandibular body segmentation in MRI. Int J Comput Assist Radiol Surg 2013; 8:723-32. [PMID: 23397281 DOI: 10.1007/s11548-012-0806-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2012] [Accepted: 12/13/2012] [Indexed: 11/28/2022]
Abstract
PURPOSE Extraction of the mandible from 3D volumetric images is frequently required for surgical planning and evaluation. Image segmentation from MRI is more complex than CT due to lower bony signal-to-noise. An automated method to extract the human mandible body shape from magnetic resonance (MR) images of the head was developed and tested. METHODS Anonymous MR images data sets of the head from 12 subjects were subjected to a two-stage rule-constrained region growing approach to derive the shape of the body of the human mandible. An initial thresholding technique was applied followed by a 3D seedless region growing algorithm to detect a large portion of the trabecular bone (TB) regions of the mandible. This stage is followed with a rule-constrained 2D segmentation of each MR axial slice to merge the remaining portions of the TB regions with lower intensity levels. The two-stage approach was replicated to detect the cortical bone (CB) regions of the mandibular body. The TB and CB regions detected from the preceding steps were merged and subjected to a series of morphological processes for completion of the mandibular body region definition. Comparisons of the accuracy of segmentation between the two-stage approach, conventional region growing method, 3D level set method, and manual segmentation were made with Jaccard index, Dice index, and mean surface distance (MSD). RESULTS The mean accuracy of the proposed method is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The mean accuracy of CRG is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The mean accuracy of the 3D level set method is [Formula: see text] for Jaccard index, [Formula: see text] for Dice index, and [Formula: see text] mm for MSD. The proposed method shows improvement in accuracy over CRG and 3D level set. CONCLUSION Accurate segmentation of the body of the human mandible from MR images is achieved with the proposed two-stage rule-constrained seedless region growing approach. The accuracy achieved with the two-stage approach is higher than CRG and 3D level set.
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Affiliation(s)
- Dong Xu Ji
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore,
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22
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Pu J, Leader JK, Meng X, Whiting B, Wilson D, Sciurba FC, Reilly JJ, Bigbee WL, Siegfried J, Gur D. Three-dimensional airway tree architecture and pulmonary function. Acad Radiol 2012; 19:1395-401. [PMID: 22884402 DOI: 10.1016/j.acra.2012.06.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2012] [Revised: 06/22/2012] [Accepted: 06/23/2012] [Indexed: 11/18/2022]
Abstract
RATIONALE AND OBJECTIVES The airway tree is a primary conductive structure, and airways' morphologic characteristics, or variations thereof, may have an impact on airflow, thereby affecting pulmonary function. The objective of this study was to investigate the correlation between airway tree architecture, as depicted on computed tomography, and pulmonary function. MATERIALS AND METHODS A total of 548 chest computed tomographic examinations acquired on different patients at full inspiration were included in this study. The patients were enrolled in a study of chronic obstructive pulmonary disease (Specialized Center for Clinically Oriented Research) and underwent pulmonary function testing in addition to computed tomographic examinations. A fully automated airway tree segmentation algorithm was used to extract the three-dimensional airway tree from each examination. Using a skeletonization algorithm, airway tree volume-normalized architectural measures, including total airway length, branch count, and trachea length, were computed. Correlations between airway tree measurements with pulmonary function testing parameters and chronic obstructive pulmonary disease severity in terms of the Global Initiative for Obstructive Lung Disease classification were computed using Spearman's rank correlations. RESULTS Non-normalized total airway volume and trachea length were associated (P < .01) with lung capacity measures (ie, functional residual capacity, total lung capacity, inspiratory capacity, vital capacity, residual volume, and forced expiratory vital capacity). Spearman's correlation coefficients ranged from 0.27 to 0.55 (P < .01). With the exception of trachea length, all normalized architecture-based measures (ie, total airway volume, total airway length, and total branch count) had statistically significant associations with the lung function measures (forced expiratory volume in 1 second and the ratio of forced expiratory volume in 1 second to forced expiratory vital capacity), and adjusted volume was associated with all three respiratory impedance measures (lung reactance at 5 Hz, lung resistance at 5 Hz, and lung resistance at 20 Hz), and adjusted branch count was associated with all respiratory impedance measures but lung resistance at 20 Hz. When normalized for lung volume, all airway architectural measures were statistically significantly associated with chronic obstructive pulmonary disease severity, with Spearman's correlation coefficients ranging from -0.338 to -0.546 (P < .01). CONCLUSIONS Despite the large variability in anatomic characteristics of the airway tree across subjects, architecture-based measures demonstrated statistically significant associations (P < .01) with nearly all pulmonary function testing measures, as well as with disease severity.
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Affiliation(s)
- Jiantao Pu
- University of Pittsburgh, Department of Radiology, PA 15213, USA.
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23
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Lo P, van Ginneken B, Reinhardt JM, Yavarna T, de Jong PA, Irving B, Fetita C, Ortner M, Pinho R, Sijbers J, Feuerstein M, Fabijańska A, Bauer C, Beichel R, Mendoza CS, Wiemker R, Lee J, Reeves AP, Born S, Weinheimer O, van Rikxoort EM, Tschirren J, Mori K, Odry B, Naidich DP, Hartmann I, Hoffman EA, Prokop M, Pedersen JH, de Bruijne M. Extraction of airways from CT (EXACT'09). IEEE TRANSACTIONS ON MEDICAL IMAGING 2012; 31:2093-2107. [PMID: 22855226 DOI: 10.1109/tmi.2012.2209674] [Citation(s) in RCA: 99] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.
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Pu J, Gu S, Liu S, Zhu S, Wilson D, Siegfried JM, Gur D. CT based computerized identification and analysis of human airways: a review. Med Phys 2012; 39:2603-16. [PMID: 22559631 DOI: 10.1118/1.4703901] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
As one of the most prevalent chronic disorders, airway disease is a major cause of morbidity and mortality worldwide. In order to understand its underlying mechanisms and to enable assessment of therapeutic efficacy of a variety of possible interventions, noninvasive investigation of the airways in a large number of subjects is of great research interest. Due to its high resolution in temporal and spatial domains, computed tomography (CT) has been widely used in clinical practices for studying the normal and abnormal manifestations of lung diseases, albeit there is a need to clearly demonstrate the benefits in light of the cost and radiation dose associated with CT examinations performed for the purpose of airway analysis. Whereas a single CT examination consists of a large number of images, manually identifying airway morphological characteristics and computing features to enable thorough investigations of airway and other lung diseases is very time-consuming and susceptible to errors. Hence, automated and semiautomated computerized analysis of human airways is becoming an important research area in medical imaging. A number of computerized techniques have been developed to date for the analysis of lung airways. In this review, we present a summary of the primary methods developed for computerized analysis of human airways, including airway segmentation, airway labeling, and airway morphometry, as well as a number of computer-aided clinical applications, such as virtual bronchoscopy. Both successes and underlying limitations of these approaches are discussed, while highlighting areas that may require additional work.
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Affiliation(s)
- Jiantao Pu
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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Salito C, Barazzetti L, Woods JC, Aliverti A. 3D Airway Tree Reconstruction in Healthy Subjects and Emphysema. Lung 2011; 189:287-93. [DOI: 10.1007/s00408-011-9305-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2011] [Accepted: 05/20/2011] [Indexed: 10/18/2022]
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