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Rajaraman S, Yang F, Zamzmi G, Xue Z, Antani S. Can Deep Adult Lung Segmentation Models Generalize to the Pediatric Population? EXPERT SYSTEMS WITH APPLICATIONS 2023; 229:120531. [PMID: 37397242 PMCID: PMC10310063 DOI: 10.1016/j.eswa.2023.120531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to (i) analyze the generalizability of deep adult lung segmentation models to the pediatric population and (ii) improve performance through a stage-wise, systematic approach consisting of CXR modality-specific weight initializations, stacked ensembles, and an ensemble of stacked ensembles. To evaluate segmentation performance and generalizability, novel evaluation metrics consisting of mean lung contour distance (MLCD) and average hash score (AHS) are proposed in addition to the multi-scale structural similarity index measure (MS-SSIM), the intersection of union (IoU), Dice score, 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD). Our results showed a significant improvement (p < 0.05) in cross-domain generalization through our approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.
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Affiliation(s)
- Sivaramakrishnan Rajaraman
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Feng Yang
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Ghada Zamzmi
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Zhiyun Xue
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Sameer Antani
- Computational Health Research Branch, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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Yao L, Shi F, Wang S, Zhang X, Xue Z, Cao X, Zhan Y, Chen L, Chen Y, Song B, Wang Q, Shen D. TaG-Net: Topology-Aware Graph Network for Centerline-Based Vessel Labeling. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3155-3166. [PMID: 37022246 DOI: 10.1109/tmi.2023.3240825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Anatomical labeling of head and neck vessels is a vital step for cerebrovascular disease diagnosis. However, it remains challenging to automatically and accurately label vessels in computed tomography angiography (CTA) since head and neck vessels are tortuous, branched, and often spatially close to nearby vasculature. To address these challenges, we propose a novel topology-aware graph network (TaG-Net) for vessel labeling. It combines the advantages of volumetric image segmentation in the voxel space and centerline labeling in the line space, wherein the voxel space provides detailed local appearance information, and line space offers high-level anatomical and topological information of vessels through the vascular graph constructed from centerlines. First, we extract centerlines from the initial vessel segmentation and construct a vascular graph from them. Then, we conduct vascular graph labeling using TaG-Net, in which techniques of topology-preserving sampling, topology-aware feature grouping, and multi-scale vascular graph are designed. After that, the labeled vascular graph is utilized to improve volumetric segmentation via vessel completion. Finally, the head and neck vessels of 18 segments are labeled by assigning centerline labels to the refined segmentation. We have conducted experiments on CTA images of 401 subjects, and experimental results show superior vessel segmentation and labeling of our method compared to other state-of-the-art methods.
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Black SM, Maclean C, Barrientos PH, Ritos K, Kazakidi A. Reconstruction and Validation of Arterial Geometries for Computational Fluid Dynamics Using Multiple Temporal Frames of 4D Flow-MRI Magnitude Images. Cardiovasc Eng Technol 2023; 14:655-676. [PMID: 37653353 PMCID: PMC10602980 DOI: 10.1007/s13239-023-00679-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/08/2023] [Indexed: 09/02/2023]
Abstract
PURPOSE Segmentation and reconstruction of arterial blood vessels is a fundamental step in the translation of computational fluid dynamics (CFD) to the clinical practice. Four-dimensional flow magnetic resonance imaging (4D Flow-MRI) can provide detailed information of blood flow but processing this information to elucidate the underlying anatomical structures is challenging. In this study, we present a novel approach to create high-contrast anatomical images from retrospective 4D Flow-MRI data. METHODS For healthy and clinical cases, the 3D instantaneous velocities at multiple cardiac time steps were superimposed directly onto the 4D Flow-MRI magnitude images and combined into a single composite frame. This new Composite Phase-Contrast Magnetic Resonance Angiogram (CPC-MRA) resulted in enhanced and uniform contrast within the lumen. These images were subsequently segmented and reconstructed to generate 3D arterial models for CFD. Using the time-dependent, 3D incompressible Reynolds-averaged Navier-Stokes equations, the transient aortic haemodynamics was computed within a rigid wall model of patient geometries. RESULTS Validation of these models against the gold standard CT-based approach showed no statistically significant inter-modality difference regarding vessel radius or curvature (p > 0.05), and a similar Dice Similarity Coefficient and Hausdorff Distance. CFD-derived near-wall hemodynamics indicated a significant inter-modality difference (p > 0.05), though these absolute errors were small. When compared to the in vivo data, CFD-derived velocities were qualitatively similar. CONCLUSION This proof-of-concept study demonstrated that functional 4D Flow-MRI information can be utilized to retrospectively generate anatomical information for CFD models in the absence of standard imaging datasets and intravenous contrast.
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Affiliation(s)
| | - Craig Maclean
- Research and Development, Terumo Aortic, Glasgow, UK
| | - Pauline Hall Barrientos
- Clinical Physics, Queen Elizabeth University Hospital, NHS Greater Glasgow & Clyde, Glasgow, UK
| | - Konstantinos Ritos
- Department of Mechanical and Aerospace Engineering, Glasgow, UK
- Department of Mechanical Engineering, University of Thessaly, Volos, Greece
| | - Asimina Kazakidi
- Department of Biomedical Engineering, University of Strathclyde, Glasgow, UK.
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Chen C, Zhou K, Wang Z, Zhang Q, Xiao R. All answers are in the images: A review of deep learning for cerebrovascular segmentation. Comput Med Imaging Graph 2023; 107:102229. [PMID: 37043879 DOI: 10.1016/j.compmedimag.2023.102229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 03/03/2023] [Accepted: 04/03/2023] [Indexed: 04/14/2023]
Abstract
Cerebrovascular imaging is a common examination. Its accurate cerebrovascular segmentation become an important auxiliary method for the diagnosis and treatment of cerebrovascular diseases, which has received extensive attention from researchers. Deep learning is a heuristic method that encourages researchers to derive answers from the images by driving datasets. With the continuous development of datasets and deep learning theory, it has achieved important success for cerebrovascular segmentation. Detailed survey is an important reference for researchers. To comprehensively analyze the newest cerebrovascular segmentation, we have organized and discussed researches centered on deep learning. This survey comprehensively reviews deep learning for cerebrovascular segmentation since 2015, it mainly includes sliding window based models, U-Net based models, other CNNs based models, small-sample based models, semi-supervised or unsupervised models, fusion based models, Transformer based models, and graphics based models. We organize the structures, improvement, and important parameters of these models, as well as analyze development trends and quantitative assessment. Finally, we have discussed the challenges and opportunities of possible research directions, hoping that our survey can provide researchers with convenient reference.
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Affiliation(s)
- Cheng Chen
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Kangneng Zhou
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhiliang Wang
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Qian Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
| | - Ruoxiu Xiao
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China; Shunde Innovation School, University of Science and Technology Beijing, Foshan 100024, China.
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Subramaniam P, Kossen T, Ritter K, Hennemuth A, Hildebrand K, Hilbert A, Sobesky J, Livne M, Galinovic I, Khalil AA, Fiebach JB, Frey D, Madai VI. Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks. Med Image Anal 2022; 78:102396. [DOI: 10.1016/j.media.2022.102396] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/28/2022] [Accepted: 02/17/2022] [Indexed: 02/01/2023]
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Pan L, Chen K, Zheng Z, Zhao Y, Yang P, Li Z, Wu S. Aging of Chinese bony orbit: automatic calculation based on UNet++ and connected component analysis. Surg Radiol Anat 2022; 44:749-758. [PMID: 35384466 PMCID: PMC8985397 DOI: 10.1007/s00276-022-02933-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/25/2022] [Indexed: 11/26/2022]
Abstract
Purpose Current research on the aging of bony orbit is usually done manually, which is inefficient and has a large error. In this paper, automatic segmentation of bony orbit based on deep learning and automatic calculation of the parameters of the segmented orbital contour (area and height of bony orbit) are presented. Methods The craniofacial CT scanning data of 595 Chinese were used to carry out three-dimensional reconstruction and output the craniofacial images. The orbital contour images are obtained automatically by UNet++ segmentation network, and then the bony orbital area and height were calculated automatically by connected component analysis. Results The automatic segmentation method has an Intersection of Union of 95.41% in craniofacial CT images. During the aging, the bony orbital area of males increased with age, while that of females decreased, and the area in male was larger than that in female (P < 0.05). The distance from equal points 10 and 40–90 to the supraorbital rim was significantly larger (P < 0.05). Except for the equal point 90, the distance from equal points to the inferior orbital rim was obviously larger (P < 0.05). In the females, the distance from equal points 50–70 to inferior orbital rim was significantly lower (P < 0.05). Conclusion The method proposed here can automatically and accurately study image dataset of large-scale bony orbital CT imaging. UNet++ can achieve high-precision segmentation of bony orbital contours. The bony orbital area of Chinese changes with aging, and the bony orbital height changes different between males and females, which may be caused by the different position and degree of orbital bone resorption of males and females in the process of aging.
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Affiliation(s)
- Lei Pan
- Plastic Surgery Center, Department of Plastic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Kunjian Chen
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China
| | - Zepei Zheng
- College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Ye Zhao
- Plastic Surgery Center, Department of Plastic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Panfeng Yang
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhu Li
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China.
| | - Sufan Wu
- Plastic Surgery Center, Department of Plastic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
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