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Jain A, Laidlaw DH, Bajcsy P, Singh R. Memory-efficient semantic segmentation of large microscopy images using graph-based neural networks. Microscopy (Oxf) 2024; 73:275-286. [PMID: 37864808 DOI: 10.1093/jmicro/dfad049] [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: 03/13/2023] [Revised: 07/14/2023] [Accepted: 10/05/2023] [Indexed: 10/23/2023] Open
Abstract
We present a graph neural network (GNN)-based framework applied to large-scale microscopy image segmentation tasks. While deep learning models, like convolutional neural networks (CNNs), have become common for automating image segmentation tasks, they are limited by the image size that can fit in the memory of computational hardware. In a GNN framework, large-scale images are converted into graphs using superpixels (regions of pixels with similar color/intensity values), allowing us to input information from the entire image into the model. By converting images with hundreds of millions of pixels to graphs with thousands of nodes, we can segment large images using memory-limited computational resources. We compare the performance of GNN- and CNN-based segmentation in terms of accuracy, training time and required graphics processing unit memory. Based on our experiments with microscopy images of biological cells and cell colonies, GNN-based segmentation used one to three orders-of-magnitude fewer computational resources with only a change in accuracy of ‒2 % to +0.3 %. Furthermore, errors due to superpixel generation can be reduced by either using better superpixel generation algorithms or increasing the number of superpixels, thereby allowing for improvement in the GNN framework's accuracy. This trade-off between accuracy and computational cost over CNN models makes the GNN framework attractive for many large-scale microscopy image segmentation tasks in biology.
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Affiliation(s)
- Atishay Jain
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, Rhode Island 02906, USA
| | - David H Laidlaw
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, Rhode Island 02906, USA
| | - Peter Bajcsy
- Information Technology Laboratory, National Institute of Standards and Technology (NIST), 100 Bureau Drive, Gaithersburg, Maryland 20899, USA
| | - Ritambhara Singh
- Department of Computer Science, Brown University, 115 Waterman Street, Providence, Rhode Island 02906, USA
- Center for Computational Molecular Biology, Brown University, 164 Angell Street, Providence, Rhode Island 02906, USA
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Nan Y, Ser JD, Tang Z, Tang P, Xing X, Fang Y, Herrera F, Pedrycz W, Walsh S, Yang G. Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7391-7404. [PMID: 37204954 DOI: 10.1109/tnnls.2023.3269223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Airway segmentation is crucial for the examination, diagnosis, and prognosis of lung diseases, while its manual delineation is unduly burdensome. To alleviate this time-consuming and potentially subjective manual procedure, researchers have proposed methods to automatically segment airways from computerized tomography (CT) images. However, some small-sized airway branches (e.g., bronchus and terminal bronchioles) significantly aggravate the difficulty of automatic segmentation by machine learning models. In particular, the variance of voxel values and the severe data imbalance in airway branches make the computational module prone to discontinuous and false-negative predictions, especially for cohorts with different lung diseases. The attention mechanism has shown the capacity to segment complex structures, while fuzzy logic can reduce the uncertainty in feature representations. Therefore, the integration of deep attention networks and fuzzy theory, given by the fuzzy attention layer, should be an escalated solution for better generalization and robustness. This article presents an efficient method for airway segmentation, comprising a novel fuzzy attention neural network (FANN) and a comprehensive loss function to enhance the spatial continuity of airway segmentation. The deep fuzzy set is formulated by a set of voxels in the feature map and a learnable Gaussian membership function. Different from the existing attention mechanism, the proposed channel-specific fuzzy attention addresses the issue of heterogeneous features in different channels. Furthermore, a novel evaluation metric is proposed to assess both the continuity and completeness of airway structures. The efficiency, generalization, and robustness of the proposed method have been proved by training on normal lung disease while testing on datasets of lung cancer, COVID-19, and pulmonary fibrosis.
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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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De Rosa S, Bignami E, Bellini V, Battaglini D. The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management. Anesth Analg 2024:00000539-990000000-00808. [PMID: 38557728 DOI: 10.1213/ane.0000000000006969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices.
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Affiliation(s)
- Silvia De Rosa
- From the Centre for Medical Sciences - CISMed, University of Trento, Trento, Italy
- Anesthesia and Intensive Care, Santa Chiara Regional Hospital, APSS Trento, Trento, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Denise Battaglini
- Anesthesia and Intensive Care, IRCCS Ospedale Policlinico San Martino, Genova, Italy
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Genkin D, Zanette B, Grzela P, Benkert T, Subbarao P, Moraes TJ, Katz S, Ratjen F, Santyr G, Kirby M. Semiautomated Segmentation and Analysis of Airway Lumen in Pediatric Patients Using Ultra Short Echo Time MRI. Acad Radiol 2024; 31:648-659. [PMID: 37550154 DOI: 10.1016/j.acra.2023.07.009] [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: 05/23/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 08/09/2023]
Abstract
RATIONALE AND OBJECTIVES Ultra short echo time (UTE) magnetic resonance imaging (MRI) pulse sequences have shown promise for airway assessment, but the feasibility and repeatability in the pediatric lung are unknown. The purpose of this work was to develop a semiautomated UTE MRI airway segmentation pipeline from the trachea-to-tertiary airways in pediatric participants and assess repeatability and lumen diameter correlations to lung function. MATERIALS AND METHODS A total of 29 participants (n = 7 healthy, n = 11 cystic fibrosis, n = 6 asthma, and n = 5 ex-preterm), aged 7-18 years, were imaged using a 3D stack-of-spirals UTE examination at 3 T. Two independent observers performed airway segmentations using a pipeline developed in-house; observer 1 repeated segmentations 1 month later. Segmentations were extracted using region-growing with leak detection, then manually edited if required. The airway trees were skeletonized, pruned, and labeled. Airway lumen diameter measurements were extracted using ray casting. Intra- and interobserver variability was assessed using the Sørensen-Dice coefficient (DSC) and intra-class correlation coefficient (ICC). Correlations between lumen diameter and pulmonary function were assessed using Spearman's correlation coefficient. RESULTS For airway segmentations and lumen diameter, intra- and interobserver DSCs were 0.88 and 0.80, while ICCs were 0.95 and 0.89, respectively. The variability increased from the trachea-to-tertiary airways for intra- (DSC: 0.91-0.64; ICC: 0.91-0.49) and interobserver (DSC: 0.84-0.51; ICC: 0.89-0.21) measurements. Lumen diameter was significantly correlated with forced expiratory volume in 1 second and forced vital capacity (P < .05). CONCLUSION UTE MRI airway segmentation from the trachea-to-tertiary airways in pediatric participants across a range of diseases is feasible. The UTE MRI-derived lumen measurements were repeatable and correlated with lung function.
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Affiliation(s)
- Daniel Genkin
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada (D.G.)
| | - Brandon Zanette
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.)
| | - Patrick Grzela
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.)
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany (T.B.)
| | - Padmaja Subbarao
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.); Department of Pediatrics, University of Toronto, Toronto, ON, Canada (P.S., T.J.M., F.R.)
| | - Theo J Moraes
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.); Department of Pediatrics, University of Toronto, Toronto, ON, Canada (P.S., T.J.M., F.R.)
| | - Sherri Katz
- Department of Pediatrics, University of Ottawa, Ottawa, ON, Canada (S.K.); Children's Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada (S.K.)
| | - Felix Ratjen
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.); Department of Pediatrics, University of Toronto, Toronto, ON, Canada (P.S., T.J.M., F.R.)
| | - Giles Santyr
- Program in Translational Medicine, The Hospital for Sick Children, Toronto, ON, Canada (B.Z., P.G., P.S., T.J.M., F.R., G.S.); Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada (G.S.)
| | - Miranda Kirby
- Department of Physics, Toronto Metropolitan University, Kerr Hall South Bldg., Room KHS-344, 350 Victoria St., Toronto, ON M5B 2K3, Canada (M.K.).
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Chen Z, Wo BWB, Chan OL, Huang YH, Teng X, Zhang J, Dong Y, Xiao L, Ren G, Cai J. Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images. Quant Imaging Med Surg 2024; 14:1636-1651. [PMID: 38415134 PMCID: PMC10895116 DOI: 10.21037/qims-23-1251] [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: 09/01/2023] [Accepted: 11/23/2023] [Indexed: 02/29/2024]
Abstract
Background Pulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach. Methods The proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-developed) model; (III) segmentation of the airway using a combination of a differential geometric approach method and a BronchiNet (public access) model; (IV) labeling of the BT branches based on anatomical position; and (V) segmentation of the pulmonary segments based on the distance of each voxel to the labeled BT branches. This five-step process was applied to 14 high-resolution breath-hold CT images and compared against manual segmentations for evaluation. Results For the lung segmentation, the lung mask had a mean dice similarity coefficient (DSC) of 0.98±0.03. For the lobe segmentation, the V-Net model had a mean DSC of 0.94±0.06. For the airway segmentation, the average total length of the segmented airway trees per image scan was 1,902.8±502.1 mm, and the average number of the maximum airway tree generations was 8.5±1.3. For the segmentation of the pulmonary segments, the proposed method had a DSC of 0.73±0.11 and a mean surface distance of 6.1±2.9 mm. Conclusions This study demonstrated the feasibility of combining multiple deep-learning models for the auxiliary segmentation of pulmonary segments on CT images using a BT-based approach. The results highlighted the potential of the BT-based method for the semi-automatic segmentation of the pulmonary segment.
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Affiliation(s)
- Zhi Chen
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Bar Wai Barry Wo
- Department of Clinical Oncology, Tuen Mun Hospital, Hong Kong, China
| | - Oi Ling Chan
- Department of Radiology, Tuen Mun Hospital, Hong Kong, China
| | - Yu-Hua Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jiang Zhang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Yanjing Dong
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Li Xiao
- Department of Clinical Oncology, Tuen Mun Hospital, Hong Kong, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Zhang M, Wu Y, Zhang H, Qin Y, Zheng H, Tang W, Arnold C, Pei C, Yu P, Nan Y, Yang G, Walsh S, Marshall DC, Komorowski M, Wang P, Guo D, Jin D, Wu Y, Zhao S, Chang R, Zhang B, Lu X, Qayyum A, Mazher M, Su Q, Wu Y, Liu Y, Zhu Y, Yang J, Pakzad A, Rangelov B, Estepar RSJ, Espinosa CC, Sun J, Yang GZ, Gu Y. Multi-site, Multi-domain Airway Tree Modeling. Med Image Anal 2023; 90:102957. [PMID: 37716199 DOI: 10.1016/j.media.2023.102957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/07/2023] [Accepted: 09/04/2023] [Indexed: 09/18/2023]
Abstract
Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).
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Affiliation(s)
- Minghui Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yangqian Wu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hanxiao Zhang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yulei Qin
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Hao Zheng
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wen Tang
- InferVision Medical Technology Co., Ltd., Beijing, China
| | | | - Chenhao Pei
- InferVision Medical Technology Co., Ltd., Beijing, China
| | - Pengxin Yu
- InferVision Medical Technology Co., Ltd., Beijing, China
| | - Yang Nan
- Imperial College London, London, UK
| | | | | | | | | | - Puyang Wang
- Alibaba DAMO Academy, 969 West Wen Yi Road, Hangzhou, Zhejiang, China
| | - Dazhou Guo
- Alibaba DAMO Academy USA, 860 Washington Street, 8F, NY, USA
| | - Dakai Jin
- Alibaba DAMO Academy USA, 860 Washington Street, 8F, NY, USA
| | - Ya'nan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shuiqing Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Runsheng Chang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Boyu Zhang
- A.I R&D Center, Sanmed Biotech Inc., No. 266 Tongchang Road, Xiangzhou District, Zhuhai, Guangdong, China
| | - Xing Lu
- A.I R&D Center, Sanmed Biotech Inc., T220 Trade st. SanDiego, CA, USA
| | - Abdul Qayyum
- ENIB, UMR CNRS 6285 LabSTICC, Brest, 29238, France
| | - Moona Mazher
- Department of Computer Engineering and Mathematics, University Rovira I Virgili, Tarragona, Spain
| | - Qi Su
- Shanghai Jiao Tong University, Shanghai, China
| | - Yonghuang Wu
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Ying'ao Liu
- University of Science and Technology of China, Hefei, Anhui, China
| | | | - Jiancheng Yang
- Dianei Technology, Shanghai, China; EPFL, Lausanne, Switzerland
| | - Ashkan Pakzad
- Medical Physics and Biomedical Engineering Department, University College London, London, UK
| | - Bojidar Rangelov
- Center for Medical Image Computing, University College London, London, UK
| | | | | | - Jiayuan Sun
- Department of Respiratory and Critical Care Medicine, Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai, China.
| | - Guang-Zhong Yang
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Yun Gu
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China.
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Dudurych I, Garcia-Uceda A, Petersen J, Du Y, Vliegenthart R, de Bruijne M. Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction. Eur Radiol 2023; 33:6718-6725. [PMID: 37071168 PMCID: PMC10511366 DOI: 10.1007/s00330-023-09615-y] [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: 07/26/2022] [Revised: 03/02/2023] [Accepted: 03/14/2023] [Indexed: 04/19/2023]
Abstract
OBJECTIVES Computed tomography (CT)-based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters. METHODS A deep-learning airway segmentation model was newly trained on 24 Imaging in Lifelines (ImaLife) low-dose chest CT scans. This model was combined with an optimal-surface graph-cut for airway wall segmentation. These tools were used to calculate bronchial parameters in CT scans of 188 ImaLife participants with two scans an average of 3 months apart. Bronchial parameters were compared for reproducibility assessment, assuming no change between scans. RESULTS Of 376 CT scans, 374 (99%) were successfully measured. Segmented airway trees contained a mean of 10 generations and 250 branches. The coefficient of determination (R2) for the luminal area (LA) ranged from 0.93 at the trachea to 0.68 at the 6th generation, decreasing to 0.51 at the 8th generation. Corresponding values for Wall Area Percentage (WAP) were 0.86, 0.67, and 0.42, respectively. Bland-Altman analysis of LA and WAP per generation demonstrated mean differences close to 0; limits of agreement (LoA) were narrow for WAP and Pi10 (± 3.7% of mean) and wider for LA (± 16.4-22.8% for 2-6th generations). From the 7th generation onwards, there was a sharp decrease in reproducibility and a widening LoA. CONCLUSION The outlined approach for automatic bronchial parameter measurement on low-dose chest CT scans is a reliable way to assess the airway tree down to the 6th generation. STATEMENT ON CLINICAL RELEVANCE This reliable and fully automatic pipeline for bronchial parameter measurement on low-dose CT scans has potential applications in screening for early disease and clinical tasks such as virtual bronchoscopy or surgical planning, while also enabling the exploration of bronchial parameters in large datasets. KEY POINTS • Deep learning combined with optimal-surface graph-cut provides accurate airway lumen and wall segmentations on low-dose CT scans. • Analysis of repeat scans showed that the automated tools had moderate-to-good reproducibility of bronchial measurements down to the 6th generation airway. • Automated measurement of bronchial parameters enables the assessment of large datasets with less man-hours.
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Affiliation(s)
- Ivan Dudurych
- Department of Radiology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Antonio Garcia-Uceda
- Department of Radiology and Nuclear Medicine, Erasmus MC, BIGR - Na 26-20, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Netherlands
- Department of Paediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, Rotterdam, Netherlands
| | - Jens Petersen
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Yihui Du
- Department of Epidemiology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
- Data Science in Health (DASH), University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Erasmus MC, BIGR - Na 26-20, Doctor Molewaterplein 40, 3015 GD, Rotterdam, Netherlands.
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.
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9
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Chu G, Zhang R, He Y, Ng CH, Gu M, Leung YY, He H, Yang Y. Deep Learning Models for Automatic Upper Airway Segmentation and Minimum Cross-Sectional Area Localisation in Two-Dimensional Images. Bioengineering (Basel) 2023; 10:915. [PMID: 37627800 PMCID: PMC10451171 DOI: 10.3390/bioengineering10080915] [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: 06/05/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023] Open
Abstract
OBJECTIVE To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images. MATERIALS AND METHODS Two hundred and one 2D airway images acquired using cone-beam computed tomography (CBCT) scanning were randomly assigned to a test group (n = 161) to train artificial intelligence (AI) models and a validation group (n = 40) to evaluate the accuracy of AI processing. Four AI models, UNet18, UNet36, DeepLab50 and DeepLab101, were trained to automatically segment the upper airway 2D images in the test group. Precision, recall, Intersection over Union, the dice similarity coefficient and size difference were used to evaluate the performance of the AI-driven segmentation models. The CSAmin height in each image was manually determined using three-dimensional CBCT data. The nonlinear mathematical morphology technique was used to calculate the CSAmin level. Height errors were assessed to evaluate the CSAmin localisation accuracy in the validation group. The time consumed for airway segmentation and CSAmin localisation was compared between manual and AI processing methods. RESULTS The precision of all four segmentation models exceeded 90.0%. No significant differences were found in the accuracy of any AI models. The consistency of CSAmin localisation in specific segments between manual and AI processing was 0.944. AI processing was much more efficient than manual processing in terms of airway segmentation and CSAmin localisation. CONCLUSIONS We successfully developed and validated a fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images.
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Affiliation(s)
- Guang Chu
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
| | - Rongzhao Zhang
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Yingqing He
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | - Chun Hown Ng
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
| | - Min Gu
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
| | - Yiu Yan Leung
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Hong He
- Department of Orthodontics, The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST), Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan 430072, China
| | - Yanqi Yang
- Orthodontics, Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China; (G.C.)
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10
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Qiu Y, Jiang Z, Sun H, Xia Q, Liu X, Lei J, Li K. Computational fluid dynamics can detect changes in airway resistance for patients after COVID-19 infection. J Biomech 2023; 157:111713. [PMID: 37413823 DOI: 10.1016/j.jbiomech.2023.111713] [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: 12/05/2022] [Revised: 06/28/2023] [Accepted: 06/30/2023] [Indexed: 07/08/2023]
Abstract
Infection with COVID-19 can cause severe complication in the respiratory system, which may be related to increased respiratory resistance. Computational fluid dynamics(CFD) was used in this study to calculate the airway resistance based on the airway anatomy and a common air flowrate. The correlation between airway resistance and COVID-19 prognosis was then investigated. A total of 23 COVID-19 patients with 54 CT scans were grouped into the good prognosis and bad prognosis group based on whether the CT scan shows significant decrease in the pneumonia volume after one week treatment and retrospectively analyzed. A baseline group of 8 healthy people with the same age and gender ratio is enrolled for comparison. Results show that the airway resistance at admission is significantly higher for COVID-19 patients with poor prognosis than those with good prognosis and the baseline(0.063 ± 0.055 vs 0.029 ± 0.011 vs 0.017 ± 0.006 Pa/(ml/s),p = 0.01). In the left superior lobe (r = 0.3974,p = 0.01),left inferior lobe (r = 0.4843,p < 0.01), the right inferior lobe (r = 0.5298,p < 0.0001), the airway resistance was significantly correlated with the degree of pneumonia infection. It is concluded that for COVID-19 patients', airway resistance at admission is closely associated with their prognosis, and has the clinical potential to be used as an index for patients' diagnosis.
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Affiliation(s)
- Yue Qiu
- Department of Pulmonary and critical care medicine and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Med-X Center for Informatics Sichuan University, Chengdu, Sichuan, China; West China Hospital- SenseTime Joint Lab, Chengdu, Sichuan, China
| | - Zekun Jiang
- Department of Pulmonary and critical care medicine and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Med-X Center for Informatics Sichuan University, Chengdu, Sichuan, China; West China Hospital- SenseTime Joint Lab, Chengdu, Sichuan, China
| | - Hui Sun
- SenseTime Research, Beijing, China; West China Hospital- SenseTime Joint Lab, Chengdu, Sichuan, China
| | - Qing Xia
- SenseTime Research, Beijing, China; West China Hospital- SenseTime Joint Lab, Chengdu, Sichuan, China
| | | | - Jianguo Lei
- Med-X Center for Informatics Sichuan University, Chengdu, Sichuan, China
| | - Kang Li
- Department of Pulmonary and critical care medicine and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Med-X Center for Informatics Sichuan University, Chengdu, Sichuan, China; West China Hospital- SenseTime Joint Lab, Chengdu, Sichuan, China.
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11
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Nadeem SA, Comellas AP, Hoffman EA, Saha PK. Airway Detection in COPD at Low-Dose CT Using Deep Learning and Multiparametric Freeze and Grow. Radiol Cardiothorac Imaging 2022; 4:e210311. [PMID: 36601453 PMCID: PMC9806731 DOI: 10.1148/ryct.210311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 09/27/2022] [Accepted: 10/27/2022] [Indexed: 06/17/2023]
Abstract
PURPOSE To present and validate a fully automated airway detection method at low-dose CT in patients with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS In this retrospective study, deep learning (DL) and freeze-and-grow (FG) methods were optimized and applied to automatically detect airways at low-dose CT. Four data sets were used: two data sets consisting of matching standard- and low-dose CT scans from the Genetic Epidemiology of COPD (COPDGene) phase II (2014-2017) cohort (n = 2 × 236; mean age ± SD, 70 years ± 9; 123 women); one data set consisting of low-dose CT scans from the COPDGene phase III (2018-2020) cohort (n = 335; mean age ± SD, 73 years ± 8; 173 women); and one data set consisting of low-dose, anonymized CT scans from the 2003 Dutch-Belgian Randomized Lung Cancer Screening trial (n = 55) acquired by using different CT scanners. Performance measures for different methods were computed and compared by using the Wilcoxon signed rank test. RESULTS At low-dose CT, 56 294 of 62 480 (90.1%) airways of the reference total airway count (TAC) and 32 109 of 37 864 (84.8%) airways of the peripheral TAC (TACp), detected at standard-dose CT, were detected. Significant losses (P < .001) of 14 526 of 76 453 (19.0%) airways and 884 of 6908 (12.8%) airways in the TAC and 12 256 of 43 462 (28.2%) airways and 699 of 3882 (18.0%) airways in the TACp were observed, respectively, for the multiprotocol and multiscanner data without retraining. When using the automated low-dose CT method, TAC values of 347, 342, 323, and 266 and TACp values of 205, 202, 289, and 141 were observed for those who have never smoked and participants at Global Initiative for Chronic Obstructive Lung Disease stages 0, 1, and 2, respectively, which were superior to the respective values previously reported for matching groups when using a semiautomated method at standard-dose CT. CONCLUSION A low-cost, automated CT-based airway detection method was suitable for investigation of airway phenotypes at low-dose CT.Keywords: Airway, Airway Count, Airway Detection, Chronic Obstructive Pulmonary Disease, CT, Deep Learning, Generalizability, Low-Dose CT, Segmentation, Thorax, LungClinical trial registration no. NCT00608764 Supplemental material is available for this article. © RSNA, 2022.
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12
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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: 7] [Impact Index Per Article: 3.5] [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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13
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Weikert T, Friebe L, Wilder-Smith A, Yang S, Sperl JI, Neumann D, Balachandran A, Bremerich J, Sauter AW. Automated quantification of airway wall thickness on chest CT using retina U-Nets - Performance evaluation and application to a large cohort of chest CTs of COPD patients. Eur J Radiol 2022; 155:110460. [PMID: 35963191 DOI: 10.1016/j.ejrad.2022.110460] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 07/17/2022] [Accepted: 07/31/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Airway wall thickening is a consequence of chronic inflammatory processes and usually only qualitatively described in CT radiology reports. The purpose of this study is to automatically quantify airway wall thickness in multiple airway generations and assess the diagnostic potential of this parameter in a large cohort of patients with Chronic Obstructive Pulmonary Disease (COPD). MATERIALS AND METHODS This retrospective, single-center study included a series of unenhanced chest CTs. Inclusion criteria were the mentioning of an explicit COPD GOLD stage in the written radiology report and time period (01/2019-12/2021). A control group included chest CTs with completely unremarkable lungs according to the report. The DICOM images of all cases (axial orientation; slice-thickness: 1 mm; soft-tissue kernel) were processed by an AI algorithm pipeline consisting of (A) a 3D-U-Net for det detection and tracing of the bronchial tree centerlines (B) extraction of image patches perpendicular to the centerlines of the bronchi, and (C) a 2D U-Net for segmentation of airway walls on those patches. The performance of centerline detection and wall segmentation was assessed. The imaging parameter average wall thickness was calculated for bronchus generations 3-8 (AWT3-8) across the lungs. Mean AWT3-8 was compared between five groups (control, COPD Gold I-IV) using non-parametric statistics. Furthermore, the established emphysema score %LAV-950 was calculated and used to classify scans (normal vs. COPD) alone and in combination with AWT3-8. RESULTS: A total of 575 chest CTs were processed. Algorithm performance was very good (airway centerline detection sensitivity: 86.9%; airway wall segmentation Dice score: 0.86). AWT3-8 was statistically significantly greater in COPD patients compared to controls (2.03 vs. 1.87 mm, p < 0.001) and increased with COPD stage. The classifier that combined %LAV-950 and AWT3-8 was superior to the classifier using only %LAV-950 (AUC = 0.92 vs. 0.79). CONCLUSION Airway wall thickness increases in patients suffering from COPD and is automatically quantifiable. AWT3-8 could become a CT imaging parameter in COPD complementing the established emphysema biomarker %LAV-950. CLINICAL RELEVANCE STATEMENT Quantitative measurements considering the complete visible bronchial tree instead of qualitative description could enhance radiology reports, allow for precise monitoring of disease progression and diagnosis of early stages of disease.
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Affiliation(s)
- Thomas Weikert
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.
| | - Liene Friebe
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Adrian Wilder-Smith
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Shan Yang
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
| | | | - Dominik Neumann
- Siemens Healthineers, Henkestrasse 127, 91052 Erlangen, Germany
| | | | - Jens Bremerich
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
| | - Alexander W Sauter
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland
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14
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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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15
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Dudurych I, Garcia-Uceda A, Saghir Z, Tiddens HAWM, Vliegenthart R, de Bruijne M. Creating a training set for artificial intelligence from initial segmentations of airways. Eur Radiol Exp 2021; 5:54. [PMID: 34841480 PMCID: PMC8627914 DOI: 10.1186/s41747-021-00247-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 10/04/2021] [Indexed: 12/02/2022] Open
Abstract
Airways segmentation is important for research about pulmonary disease but require a large amount of time by trained specialists. We used an openly available software to improve airways segmentations obtained from an artificial intelligence (AI) tool and retrained the tool to get a better performance. Fifteen initial airway segmentations from low-dose chest computed tomography scans were obtained with a 3D-Unet AI tool previously trained on Danish Lung Cancer Screening Trial and Erasmus-MC Sophia datasets. Segmentations were manually corrected in 3D Slicer. The corrected airway segmentations were used to retrain the 3D-Unet. Airway measurements were automatically obtained and included count, airway length and luminal diameter per generation from the segmentations. Correcting segmentations required 2–4 h per scan. Manually corrected segmentations had more branches (p < 0.001), longer airways (p < 0.001) and smaller luminal diameters (p = 0.004) than initial segmentations. Segmentations from retrained 3D-Unets trended towards more branches and longer airways compared to the initial segmentations. The largest changes were seen in airways from 6th generation onwards. Manual correction results in significantly improved segmentations and is potentially a useful and time-efficient method to improve the AI tool performance on a specific hospital or research dataset.
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Affiliation(s)
- Ivan Dudurych
- Department of Radiology, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands.
| | - Antonio Garcia-Uceda
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.,Department of Paediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, Rotterdam, Netherlands
| | - Zaigham Saghir
- Department of Medicine, Section of Pulmonary Medicine, Herlev-Gentofte Hospital, Hellerup, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Harm A W M Tiddens
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.,Department of Paediatric Pulmonology and Allergology, Erasmus MC-Sophia Children Hospital, Rotterdam, Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Centre Groningen, Groningen, Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
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