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Zeng H, Zhou K, Ge S, Gao Y, Zhao J, Gao S, Zheng R. Anatomical Prior and Inter-Slice Consistency for Semi-Supervised Vertebral Structure Detection in 3D Ultrasound Volume. IEEE J Biomed Health Inform 2024; 28:2211-2222. [PMID: 38289848 DOI: 10.1109/jbhi.2024.3360102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Three-dimensional (3D) ultrasound imaging technique has been applied for scoliosis assessment, but the current assessment method only uses coronal projection images and cannot illustrate the 3D deformity and vertebra rotation. The vertebra detection is essential to reveal 3D spine information, but the detection task is challenging due to complex data and limited annotations. We propose VertMatch to detect vertebral structures in 3D ultrasound volume containing a detector and classifier. The detector network finds the potential positions of structures on transverse slice globally, and then the local patches are cropped based on detected positions. The classifier is used to distinguish whether the patches contain real vertebral structures and screen the predicted positions from the detector. VertMatch utilizes unlabeled data in a semi-supervised manner, and we develop two novel techniques for semi-supervised learning: 1) anatomical prior is used to acquire high-quality pseudo labels; 2) inter-slice consistency is used to utilize more unlabeled data by inputting multiple adjacent slices. Experimental results demonstrate that VertMatch can detect vertebra accurately in ultrasound volume and outperforms state-of-the-art methods. Moreover, VertMatch is also validated in automatic spinous process angle measurement on forty subjects with scoliosis, and the results illustrate that it can be a promising approach for the 3D assessment of scoliosis.
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Lee C, Kim C, Park B. Review of Three-Dimensional Handheld Photoacoustic and Ultrasound Imaging Systems and Their Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:8149. [PMID: 37836978 PMCID: PMC10575128 DOI: 10.3390/s23198149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/25/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023]
Abstract
Photoacoustic (PA) imaging is a non-invasive biomedical imaging technique that combines the benefits of optics and acoustics to provide high-resolution structural and functional information. This review highlights the emergence of three-dimensional handheld PA imaging systems as a promising approach for various biomedical applications. These systems are classified into four techniques: direct imaging with 2D ultrasound (US) arrays, mechanical-scanning-based imaging with 1D US arrays, mirror-scanning-based imaging, and freehand-scanning-based imaging. A comprehensive overview of recent research in each imaging technique is provided, and potential solutions for system limitations are discussed. This review will serve as a valuable resource for researchers and practitioners interested in advancements and opportunities in three-dimensional handheld PA imaging technology.
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Affiliation(s)
- Changyeop Lee
- Department of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering, Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Pohang University of Science and Technology, Pohang 37673, Republic of Korea;
| | - Chulhong Kim
- Department of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering, Graduate School of Artificial Intelligence, and Medical Device Innovation Center, Pohang University of Science and Technology, Pohang 37673, Republic of Korea;
| | - Byullee Park
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Republic of Korea
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Li R, Davoodi A, Cai Y, Niu K, Borghesan G, Cavalcanti N, Massalimova A, Carrillo F, Laux CJ, Farshad M, Fürnstahl P, Poorten EV. Robot-assisted ultrasound reconstruction for spine surgery: from bench-top to pre-clinical study. Int J Comput Assist Radiol Surg 2023; 18:1613-1623. [PMID: 37171662 DOI: 10.1007/s11548-023-02932-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/19/2023] [Indexed: 05/13/2023]
Abstract
PURPOSE Robot-assisted ultrasound (rUS) systems have already been used to provide non-radiative three-dimensional (3D) reconstructions that form the basis for guiding spine surgical procedures. Despite promising studies on this technology, there are few studies that offer insight into the robustness and generality of the approach by verifying performance in various testing scenarios. Therefore, this study aims at providing an assessment of a rUS system, with technical details from experiments starting at the bench-top to the pre-clinical study. METHODS A semi-automatic control strategy was proposed to ensure continuous and smooth robotic scanning. Next, a U-Net-based segmentation approach was developed to automatically process the anatomic features and derive a high-quality 3D US reconstruction. Experiments were conducted on synthetic phantoms and human cadavers to validate the proposed approach. RESULTS Average deviations of scanning force were found to be 2.84±0.45 N on synthetic phantoms and to be 5.64±1.10 N on human cadavers. The anatomic features could be reliably reconstructed at mean accuracy of 1.28±0.87 mm for the synthetic phantoms and of 1.74±0.89 mm for the human cadavers. CONCLUSION The results and experiments demonstrate the feasibility of the proposed system in a pre-clinical setting. This work is complementary to previous work, encouraging further exploration of the potential of this technology in in vivo studies.
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Affiliation(s)
- Ruixuan Li
- Robot-Assisted Surgery group, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium.
| | - Ayoob Davoodi
- Robot-Assisted Surgery group, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Yuyu Cai
- Robot-Assisted Surgery group, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
| | - Kenan Niu
- Robotics and Mechatronics, University of Twente, Enschede, The Netherlands
| | - Gianni Borghesan
- Robot-Assisted Surgery group, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
- Core Lab ROB, Flanders Make, Leuven, Belgium
| | - Nicola Cavalcanti
- Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Aidana Massalimova
- Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Fabio Carrillo
- Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Christoph J Laux
- University Spine Center Zurich, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Mazda Farshad
- University Spine Center Zurich, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Philipp Fürnstahl
- Research in Orthopedic Computer Science, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Emmanuel Vander Poorten
- Robot-Assisted Surgery group, Department of Mechanical Engineering, KU Leuven, Leuven, Belgium
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Meszaros-Beller L, Antico M, Fontanarosa D, Pivonka P. Assessment of thoracic spinal curvatures in static postures using spatially tracked 3D ultrasound volumes: a proof-of-concept study. Phys Eng Sci Med 2023; 46:197-208. [PMID: 36625994 PMCID: PMC10030537 DOI: 10.1007/s13246-022-01210-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 12/12/2022] [Indexed: 01/11/2023]
Abstract
The assessment of spinal posture is a difficult endeavour given the lack of identifiable bony landmarks for placement of skin markers. Moreover, potentially significant soft tissue artefacts along the spine further affect the accuracy of marker-based approaches. The objective of this proof-of-concept study was to develop an experimental framework to assess spinal postures by using three-dimensional (3D) ultrasound (US) imaging. A phantom spine model immersed in water was scanned using 3D US in a neutral and two curved postures mimicking a forward flexion in the sagittal plane while the US probe was localised by three electromagnetic tracking sensors attached to the probe head. The obtained anatomical 'coarse' registrations were further refined using an automatic registration algorithm and validated by an experienced sonographer. Spinal landmarks were selected in the US images and validated against magnetic resonance imaging data of the same phantom through image registration. Their position was then related to the location of the tracking sensors identified in the acquired US volumes, enabling the localisation of landmarks in the global coordinate system of the tracking device. Results of this study show that localised 3D US enables US-based anatomical reconstructions comparable to clinical standards and the identification of spinal landmarks in different postures of the spine. The accuracy in sensor identification was 0.49 mm on average while the intra- and inter-observer reliability in sensor identification was strongly correlated with a maximum deviation of 0.8 mm. Mapping of landmarks had a small relative distance error of 0.21 mm (SD = ± 0.16) on average. This study implies that localised 3D US holds the potential for the assessment of full spinal posture by accurately and non-invasively localising vertebrae in space.
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Affiliation(s)
- Laura Meszaros-Beller
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Australia.
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia.
| | - Maria Antico
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Queensland, Australia
- School of Clinical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Davide Fontanarosa
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia
- School of Clinical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Peter Pivonka
- School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Australia
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Australia
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Wang Y, Fu T, Wu C, Fan J, Song H, Xiao D, Lin Y, Liu F, Yang J. Adaptive tetrahedral interpolation for reconstruction of uneven freehand 3D ultrasound. Phys Med Biol 2023; 68. [PMID: 36731138 DOI: 10.1088/1361-6560/acb88c] [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: 04/20/2022] [Accepted: 02/02/2023] [Indexed: 02/04/2023]
Abstract
Objective.Freehand 3D ultrasound volume reconstruction has received considerable attention in medical research because it can freely perform spatial imaging at a low cost. However, the uneven distribution of the original ultrasound images in space reduces the reconstruction effect of the traditional method.Approach.An adaptive tetrahedral interpolation algorithm is proposed to reconstruct 3D ultrasound volume data. The algorithm adaptively divides the unevenly distributed images into numerous tetrahedrons and interpolates the voxel value in each tetrahedron to reconstruct 3D ultrasound volume data.Main results.Extensive experiments on simulated and clinical data confirm that the proposed method can achieve more accurate reconstruction than six benchmark methods. Specifically, the averaged interpolation error at the gray level can be reduced by 0.22-0.82, and the peak signal-to-noise ratio and the mean structure similarity can be improved by 0.32-1.83 dB and 0.01-0.05, respectively.Significance.With the parallel implementation of the algorithm, one 3D ultrasound volume data with size 279 × 279 × 276 can be reconstructed from 100 slices 2D ultrasound images with size 200 × 200 at 1.04 s. Such a quick and accurate approach has practical value in medical research.
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Affiliation(s)
- Yifan Wang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Tianyu Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Chan Wu
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Jingfan Fan
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Hong Song
- School of Software, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Deqiang Xiao
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Yucong Lin
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, People's Republic of China
| | - Fangyi Liu
- Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing 100853, People's Republic of China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, People's Republic of China
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Jin C, Wang S, Yang G, Li E, Liang Z. A Review of the Methods on Cobb Angle Measurements for Spinal Curvature. SENSORS 2022; 22:s22093258. [PMID: 35590951 PMCID: PMC9101880 DOI: 10.3390/s22093258] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/11/2022] [Accepted: 04/19/2022] [Indexed: 11/16/2022]
Abstract
Scoliosis is a common disease of the spine and requires regular monitoring due to its progressive properties. A preferred indicator to assess scoliosis is by the Cobb angle, which is currently measured either manually by the relevant medical staff or semi-automatically, aided by a computer. These methods are not only labor-intensive but also vary in precision by the inter-observer and intra-observer. Therefore, a reliable and convenient method is urgently needed. With the development of computer vision and deep learning, it is possible to automatically calculate the Cobb angles by processing X-ray or CT/MR/US images. In this paper, the research progress of Cobb angle measurement in recent years is reviewed from the perspectives of computer vision and deep learning. By comparing the measurement effects of typical methods, their advantages and disadvantages are analyzed. Finally, the key issues and their development trends are also discussed.
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Affiliation(s)
- Chen Jin
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (C.J.); (E.L.); (Z.L.)
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shengru Wang
- Peking Union Medical College Hospital, Beijing 100005, China;
| | - Guodong Yang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (C.J.); (E.L.); (Z.L.)
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: ; Tel.: +86-10-82544504
| | - En Li
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (C.J.); (E.L.); (Z.L.)
| | - Zize Liang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; (C.J.); (E.L.); (Z.L.)
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Banerjee S, Lyu J, Huang Z, Leung FH, Lee T, Yang D, Su S, Zheng Y, Ling SH. Ultrasound spine image segmentation using multi-scale feature fusion skip-inception U-Net (SIU-Net). Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.02.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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