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Kim JY, Kim HS, Kang SY, Moon BS, Kim BS, Yoon HJ. Value of Quantitative Salivary Gland SPECT/CT for the Detection of Saliva Leakage. Clin Nucl Med 2024; 49:e12-e13. [PMID: 37976525 DOI: 10.1097/rlu.0000000000004932] [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: 11/19/2023]
Abstract
ABSTRACT A 47-year-old woman presented to our emergency department with a 10-day history of pain, halitosis, and swelling below the left jaw. The patient was diagnosed with left sialadenitis and left submandibular abscess by tissue biopsy. An otolaryngologist performed transcervical incision and drainage of the abscess 1 day after admission. Postoperatively, the patient complained of a sensation of fluid leakage from the mouth, and a continuous purulent discharge was observed. One month postoperatively, a salivary gland scan and SPECT/CT were performed to investigate the sialorrhea and the cause of the discharge. Salivary gland SPECT/CT images localized the saliva leakage site.
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Affiliation(s)
| | - Han Su Kim
- Otorhinolaryngology-Head and Neck Surgery, Ewha Womans University, College of Medicine, Seoul, South Korea
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Weisman AJ, Huff DT, Govindan RM, Chen S, Perk TG. Multi-organ segmentation of CT via convolutional neural network: impact of training setting and scanner manufacturer. Biomed Phys Eng Express 2023; 9:065021. [PMID: 37725928 DOI: 10.1088/2057-1976/acfb06] [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: 07/06/2023] [Accepted: 09/19/2023] [Indexed: 09/21/2023]
Abstract
Objective. Automated organ segmentation on CT images can enable the clinical use of advanced quantitative software devices, but model performance sensitivities must be understood before widespread adoption can occur. The goal of this study was to investigate performance differences between Convolutional Neural Networks (CNNs) trained to segment one (single-class) versus multiple (multi-class) organs, and between CNNs trained on scans from a single manufacturer versus multiple manufacturers.Methods. The multi-class CNN was trained on CT images obtained from 455 whole-body PET/CT scans (413 for training, 42 for testing) taken with Siemens, GE, and Phillips PET/CT scanners where 16 organs were segmented. The multi-class CNN was compared to 16 smaller single-class CNNs trained using the same data, but with segmentations of only one organ per model. In addition, CNNs trained on Siemens-only (N = 186) and GE-only (N = 219) scans (manufacturer-specific) were compared with CNNs trained on data from both Siemens and GE scanners (manufacturer-mixed). Segmentation performance was quantified using five performance metrics, including the Dice Similarity Coefficient (DSC).Results. The multi-class CNN performed well compared to previous studies, even in organs usually considered difficult auto-segmentation targets (e.g., pancreas, bowel). Segmentations from the multi-class CNN were significantly superior to those from smaller single-class CNNs in most organs, and the 16 single-class models took, on average, six times longer to segment all 16 organs compared to the single multi-class model. The manufacturer-mixed approach achieved minimally higher performance over the manufacturer-specific approach.Significance. A CNN trained on contours of multiple organs and CT data from multiple manufacturers yielded high-quality segmentations. Such a model is an essential enabler of image processing in a software device that quantifies and analyzes such data to determine a patient's treatment response. To date, this activity of whole organ segmentation has not been adopted due to the intense manual workload and time required.
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Affiliation(s)
- Amy J Weisman
- AIQ Solutions, Madison, WI, United States of America
| | - Daniel T Huff
- AIQ Solutions, Madison, WI, United States of America
| | | | - Song Chen
- Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, People's Republic of China
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Park J, Kang SK, Hwang D, Choi H, Ha S, Seo JM, Eo JS, Lee JS. Automatic Lung Cancer Segmentation in [ 18F]FDG PET/CT Using a Two-Stage Deep Learning Approach. Nucl Med Mol Imaging 2023; 57:86-93. [PMID: 36998591 PMCID: PMC10043063 DOI: 10.1007/s13139-022-00745-7] [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: 09/23/2021] [Revised: 03/10/2022] [Accepted: 03/12/2022] [Indexed: 10/18/2022] Open
Abstract
Purpose Since accurate lung cancer segmentation is required to determine the functional volume of a tumor in [18F]FDG PET/CT, we propose a two-stage U-Net architecture to enhance the performance of lung cancer segmentation using [18F]FDG PET/CT. Methods The whole-body [18F]FDG PET/CT scan data of 887 patients with lung cancer were retrospectively used for network training and evaluation. The ground-truth tumor volume of interest was drawn using the LifeX software. The dataset was randomly partitioned into training, validation, and test sets. Among the 887 PET/CT and VOI datasets, 730 were used to train the proposed models, 81 were used as the validation set, and the remaining 76 were used to evaluate the model. In Stage 1, the global U-net receives 3D PET/CT volume as input and extracts the preliminary tumor area, generating a 3D binary volume as output. In Stage 2, the regional U-net receives eight consecutive PET/CT slices around the slice selected by the Global U-net in Stage 1 and generates a 2D binary image as the output. Results The proposed two-stage U-Net architecture outperformed the conventional one-stage 3D U-Net in primary lung cancer segmentation. The two-stage U-Net model successfully predicted the detailed margin of the tumors, which was determined by manually drawing spherical VOIs and applying an adaptive threshold. Quantitative analysis using the Dice similarity coefficient confirmed the advantages of the two-stage U-Net. Conclusion The proposed method will be useful for reducing the time and effort required for accurate lung cancer segmentation in [18F]FDG PET/CT.
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Affiliation(s)
- Junyoung Park
- Department of Electrical and Computer Engineering, Seoul National University College of Engineering, Seoul, 08826 Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
| | - Seung Kwan Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080 Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 Korea
- Brightonix Imaging Inc., Seoul, 03080 Korea
| | - Donghwi Hwang
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080 Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
| | - Seunggyun Ha
- Division of Nuclear Medicine, Department of Radiology, Seoul St Mary’s Hospital, The Catholic University of Korea, Seoul, 06591 Korea
| | - Jong Mo Seo
- Department of Electrical and Computer Engineering, Seoul National University College of Engineering, Seoul, 08826 Korea
| | - Jae Seon Eo
- Department of Nuclear Medicine, Korea University Guro Hospital, 148 Gurodong-ro, Guro-gu, Seoul, 08308 Korea
| | - Jae Sung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, 03080 Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, 08826 Korea
- Brightonix Imaging Inc., Seoul, 03080 Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University College of Medicine, Seoul, 03080 Korea
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Kwon K, Hwang D, Oh D, Kim JH, Yoo J, Lee JS, Lee WW. CT-free quantitative SPECT for automatic evaluation of %thyroid uptake based on deep-learning. EJNMMI Phys 2023; 10:20. [PMID: 36947267 PMCID: PMC10033819 DOI: 10.1186/s40658-023-00536-9] [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: 10/21/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023] Open
Abstract
PURPOSE Quantitative thyroid single-photon emission computed tomography/computed tomography (SPECT/CT) requires computed tomography (CT)-based attenuation correction and manual thyroid segmentation on CT for %thyroid uptake measurements. Here, we aimed to develop a deep-learning-based CT-free quantitative thyroid SPECT that can generate an attenuation map (μ-map) and automatically segment the thyroid. METHODS Quantitative thyroid SPECT/CT data (n = 650) were retrospectively analyzed. Typical 3D U-Nets were used for the μ-map generation and automatic thyroid segmentation. Primary emission and scattering SPECTs were inputted to generate a μ-map, and the original μ-map from CT was labeled (268 and 30 for training and validation, respectively). The generated μ-map and primary emission SPECT were inputted for the automatic thyroid segmentation, and the manual thyroid segmentation was labeled (280 and 36 for training and validation, respectively). Other thyroid SPECT/CT (n = 36) and salivary SPECT/CT (n = 29) were employed for verification. RESULTS The synthetic μ-map demonstrated a strong correlation (R2 = 0.972) and minimum error (mean square error = 0.936 × 10-4, %normalized mean absolute error = 0.999%) of attenuation coefficients when compared to the ground truth (n = 30). Compared to manual segmentation, the automatic thyroid segmentation was excellent with a Dice similarity coefficient of 0.767, minimal thyroid volume difference of - 0.72 mL, and a short 95% Hausdorff distance of 9.416 mm (n = 36). Additionally, %thyroid uptake by synthetic μ-map and automatic thyroid segmentation (CT-free SPECT) was similar to that by the original μ-map and manual thyroid segmentation (SPECT/CT) (3.772 ± 5.735% vs. 3.682 ± 5.516%, p = 0.1090) (n = 36). Furthermore, the synthetic μ-map generation and automatic thyroid segmentation were successfully performed in the salivary SPECT/CT using the deep-learning algorithms trained by thyroid SPECT/CT (n = 29). CONCLUSION CT-free quantitative SPECT for automatic evaluation of %thyroid uptake can be realized by deep-learning.
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Affiliation(s)
- Kyounghyoun Kwon
- Department of Health Science and Technology, The Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea
| | - Donghwi Hwang
- Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dongkyu Oh
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ji Hye Kim
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea
| | - Jihyung Yoo
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea
| | - Jae Sung Lee
- Department of Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea
| | - Won Woo Lee
- Department of Health Science and Technology, The Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam, Gyeonggi-do, 13620, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea.
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SCHNYDER JASON D A, KRİSHNAN V, VİNAYACHANDRAN D. Intelligent systems for precision dental diagnosis and treatment planning – A review. CUMHURIYET DENTAL JOURNAL 2022. [DOI: 10.7126/cumudj.991480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Machines have changed the course of mankind. Simple machines were the basis of human civilization. Today with humongous technological development, machines are intelligent enough to carry out very complex nerve-racking tasks. The ability of a machine to learn from algorithms changed eventually into, the machine learning by itself, which constitutes artificial intelligence. Literature has plausible evidence for the use of intelligent systems in medical field. Artificial intelligence has been used in the multiple denominations of dentistry. These machines are used in the precision diagnosis, interpretation of medical images, accumulation of data, classification and compilation of records, determination of treatment and construction of a personalized treatment plan. Artificial intelligence can help in timely diagnosis of complex dental diseases which would ultimately aid in rapid commencement of treatment. Research helps us understand the effectiveness and challenges in the use of this technology. The apt use of intelligent systems could transform the entire medical system for the better.
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Affiliation(s)
| | - Vidya KRİSHNAN
- SRM Kattankulathur Dental College, SRM Institute of Science and Technology
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Lee WW, Ryu JS. KSNM 60 in General Nuclear Medicine: the Old Dream Comes True. Nucl Med Mol Imaging 2022; 56:71-79. [PMID: 35464669 PMCID: PMC8976866 DOI: 10.1007/s13139-021-00731-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/02/2021] [Accepted: 12/07/2021] [Indexed: 10/19/2022] Open
Abstract
Since the establishment of the Korean Society of Nuclear Medicine (KSNM) in 1961 by Professor Munho Lee, the KSNM has been progressing in various medical fields. Many papers have been published in the Korean Journal of Nuclear Medicine (KJNM), the official journal of KSNM, since 1967 and other domestic/international journals. Here, we tried to highlight the academic activities of KSNM members from the perspective of general nuclear medicine. After the introduction of the 99Mo/99mTc generator, general nuclear medicine has widened the field of clinical application with the advancement of imaging technology and emerging new radiopharmaceuticals; however, there have been many ups and downs. Treatment, as well as diagnosis, was a major concern in Korean nuclear medicine. With the recent advent of single-photon emission computed tomography/computed tomography, we hope that our old dream (diagnosis and treatment under the same principle of nuclear medicine) comes true.
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Murata T. [[SPECT] 5. Application of Artificial Intelligence in Nuclear Medicine for SPECT]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:1230-1236. [PMID: 36261360 DOI: 10.6009/jjrt.2022-2096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
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Lee JS, Kim KM, Choi Y, Kim HJ. A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea. Nucl Med Mol Imaging 2021; 55:265-284. [PMID: 34868376 DOI: 10.1007/s13139-021-00721-7] [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: 07/19/2021] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 10/19/2022] Open
Abstract
We review the history of nuclear medicine physics, instrumentation, and data sciences in Korea to commemorate the 60th anniversary of the Korean Society of Nuclear Medicine. In the 1970s and 1980s, the development of SPECT, nuclear stethoscope, and bone densitometry systems, as well as kidney and cardiac image analysis technology, marked the beginning of nuclear medicine physics and engineering in Korea. With the introduction of PET and cyclotron in Korea in 1994, nuclear medicine imaging research was further activated. With the support of large-scale government projects, the development of gamma camera, SPECT, and PET systems was carried out. Exploiting the use of PET scanners in conjunction with cyclotrons, extensive studies on myocardial blood flow quantification and brain image analysis were also actively pursued. In 2005, Korea's first domestic cyclotron succeeded in producing radioactive isotopes, and the cyclotron was provided to six universities and university hospitals, thereby facilitating the nationwide supply of PET radiopharmaceuticals. Since the late 2000s, research on PET/MRI has been actively conducted, and the advanced research results of Korean scientists in the fields of silicon photomultiplier PET and simultaneous PET/MRI have attracted significant attention from the academic community. Currently, Korean researchers are actively involved in endeavors to solve a variety of complex problems in nuclear medicine using artificial intelligence and deep learning technologies.
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Affiliation(s)
- Jae Sung Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080 Korea
| | - Kyeong Min Kim
- Department of Isotopic Drug Development, Korea Radioisotope Center for Pharmaceuticals, Korea Institute of Radiological and Medical Sciences, Seoul, Korea
| | - Yong Choi
- Department of Electronic Engineering, Sogang University, Seoul, Korea
| | - Hee-Joung Kim
- Department of Radiological Science, Yonsei University, Wonju, Korea
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