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Astley JR, Wild JM, Tahir BA. Deep learning in structural and functional lung image analysis. Br J Radiol 2022; 95:20201107. [PMID: 33877878 PMCID: PMC9153705 DOI: 10.1259/bjr.20201107] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The recent resurgence of deep learning (DL) has dramatically influenced the medical imaging field. Medical image analysis applications have been at the forefront of DL research efforts applied to multiple diseases and organs, including those of the lungs. The aims of this review are twofold: (i) to briefly overview DL theory as it relates to lung image analysis; (ii) to systematically review the DL research literature relating to the lung image analysis applications of segmentation, reconstruction, registration and synthesis. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. 479 studies were initially identified from the literature search with 82 studies meeting the eligibility criteria. Segmentation was the most common lung image analysis DL application (65.9% of papers reviewed). DL has shown impressive results when applied to segmentation of the whole lung and other pulmonary structures. DL has also shown great potential for applications in image registration, reconstruction and synthesis. However, the majority of published studies have been limited to structural lung imaging with only 12.9% of reviewed studies employing functional lung imaging modalities, thus highlighting significant opportunities for further research in this field. Although the field of DL in lung image analysis is rapidly expanding, concerns over inconsistent validation and evaluation strategies, intersite generalisability, transparency of methodological detail and interpretability need to be addressed before widespread adoption in clinical lung imaging workflow.
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Affiliation(s)
| | - Jim M Wild
- Department of Oncology and Metabolism, The University of Sheffield, Sheffield, United Kingdom
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Madore B, Belsley G, Cheng CC, Preiswerk F, Foley Kijewski M, Wu PH, Martell LB, Pluim JPW, Di Carli M, Moore SC. Ultrasound-based sensors for respiratory motion assessment in multimodality PET imaging. Phys Med Biol 2021; 67. [PMID: 34891142 DOI: 10.1088/1361-6560/ac4213] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 12/10/2021] [Indexed: 11/11/2022]
Abstract
Breathing motion can displace internal organs by up to several cm; as such, it is a primary factor limiting image quality in medical imaging. Motion can also complicate matters when trying to fuse images from different modalities, acquired at different locations and/or on different days. Currently available devices for monitoring breathing motion often do so indirectly, by detecting changes in the outline of the torso rather than the internal motion itself, and these devices are often fixed to floors, ceilings or walls, and thus cannot accompany patients from one location to another. We have developed small ultrasound-based sensors, referred to as 'organ configuration motion' (OCM) sensors, that attach to the skin and provide rich motion-sensitive information. In the present work we tested the ability of OCM sensors to enable respiratory gating during in vivo PET imaging. A motion phantom involving an FDG solution was assembled, and two cancer patients scheduled for a clinical PET/CT exam were recruited for this study. OCM signals were used to help reconstruct phantom and in vivo data into time series of motion-resolved images. As expected, the motion-resolved images captured the underlying motion. In Patient #1, a single large lesion proved to be mostly stationary through the breathing cycle. However, in Patient #2, several small lesions were mobile during breathing, and our proposed new approach captured their breathing-related displacements. In summary, a relatively inexpensive hardware solution was developed here for respiration monitoring. Because the proposed sensors attach to the skin, as opposed to walls or ceilings, they can accompany patients from one procedure to the next, potentially allowing data gathered in different places and at different times to be combined and compared in ways that account for breathing motion.
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Affiliation(s)
- Bruno Madore
- Harvard Medical School, Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, Massachusetts, 02115, UNITED STATES
| | - Gabriela Belsley
- Oxford Centre for Clinical Magnetic Resonance Research, Radcliffe Department of Medicine, University of Oxford, Oxford, Oxford, OX3 9DU, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Cheng-Chieh Cheng
- Computer Science and Engineering, National Sun Yat-sen University, 70 Lianhai Road, Kaohsiung, 804, TAIWAN
| | - Frank Preiswerk
- Amazon Robotics, Westborough, MA, USA, Amazon Robotics, 50 Otis St, Westborough, Massachusetts, 01581, UNITED STATES
| | - Marie Foley Kijewski
- Harvard Medical School, Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, Massachusetts, 02115, UNITED STATES
| | - Pei-Hsin Wu
- Electrical Engineering, National Sun Yat-sen University, 70 Lianhai Road, Kaohsiung, 804, TAIWAN
| | - Laurel B Martell
- Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, Massachusetts, 02115, UNITED STATES
| | - Josien P W Pluim
- Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, Eindhoven, PO Box 513, NETHERLANDS
| | - Marcelo Di Carli
- Harvard Medical School, Department of Radiology, Brigham and Women's Hospital, 75 Francis Street, Boston, Massachusetts, 02115, UNITED STATES
| | - Stephen C Moore
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, Pennsylvania, 19104, UNITED STATES
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Madore B, Preiswerk F, Bredfeldt JS, Zong S, Cheng CC. Ultrasound-based sensors to monitor physiological motion. Med Phys 2021; 48:3614-3622. [PMID: 33999423 DOI: 10.1002/mp.14949] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 12/28/2020] [Accepted: 05/01/2021] [Indexed: 12/25/2022] Open
Abstract
PURPOSE Medical procedures can be difficult to perform on anatomy that is constantly moving. Respiration displaces internal organs by up to several centimeters with respect to the surface of the body, and patients often have limited ability to hold their breath. Strategies to compensate for motion during diagnostic and therapeutic procedures require reliable information to be available. However, current devices often monitor respiration indirectly, through changes on the outline of the body, and they may be fixed to floors or ceilings, and thus unable to follow a given patient through different locations. Here we show that small ultrasound-based sensors referred to as "organ configuration motion" (OCM) sensors can be fixed to the abdomen and/or chest and provide information-rich, breathing-related signals. METHODS By design, the proposed sensors are relatively inexpensive. Breathing waveforms were obtained from tissues at varying depths and/or using different sensor placements. Validation was performed against breathing waveforms derived from magnetic resonance imaging (MRI) and optical tracking signals in five and eight volunteers, respectively. RESULTS Breathing waveforms from different modalities were scaled so they could be directly compared. Differences between waveforms were expressed in the form of a percentage, as compared to the amplitude of a typical breath. Expressed in this manner, for shallow tissues, OCM-derived waveforms on average differed from MRI and optical tracking results by 13.1% and 15.5%, respectively. CONCLUSION The present results suggest that the proposed sensors provide measurements that properly characterize breathing states. While OCM-based waveforms from shallow tissues proved similar in terms of information content to those derived from MRI or optical tracking, OCM further captured depth-dependent and position-dependent (i.e., chest and abdomen) information. In time, the richer information content of OCM-based waveforms may enable better respiratory gating to be performed, to allow diagnostic and therapeutic equipment to perform at their best.
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Affiliation(s)
- Bruno Madore
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Frank Preiswerk
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Amazon Robotics, North Reading, MA, USA
| | - Jeremy S Bredfeldt
- Department of Radiation Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shenyan Zong
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Cheng-Chieh Cheng
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan
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Azghadi MR, Lammie C, Eshraghian JK, Payvand M, Donati E, Linares-Barranco B, Indiveri G. Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2020; 14:1138-1159. [PMID: 33156792 DOI: 10.1109/tbcas.2020.3036081] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. The tutorial is augmented with case studies of the vast literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hardware platforms by performing a sensor fusion signal processing task combining electromyography (EMG) signals with computer vision. Comparisons are made between dedicated neuromorphic processors and embedded AI accelerators in terms of inference latency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that various accelerators and neuromorphic processors introduce to healthcare and biomedical domains.
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