지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 61 - 67 (7page)
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초록· 키워드
오류제보하기
Objectives: Back pain, especially lower back pain, is experienced in 60% to 80% of adults at some points during their lives. Various studies have found that lower back pain is a very common problem among adolescents, and the highest incidence rates are for adults in their 30s. There has been a remarkable increase in using computer-aided diagnosis to assist doctors in the interpretation of medical images. Spine segmentation in computed tomography (CT) scans using algorithmic methods allows improved diagnosis of back pain. Methods: In this study, we developed a web-based automatic spine segmentation method using deep learning and obtained the dice coefficient by comparison with the predicted image. Our method is based on convolutional neural networks for segmentation. More specifically, we train a hierarchical data format file using U-Net architecture and then insert the test data label to perform segmentation. Thus, we obtained more specific and detailed results. A total of 344 CT images were used in the experiment. Of these, 330 were used for learning, and the remaining 14 for testing. Results: Our method achieved an average dice coefficient of 90.4%, a precision of 96.81%, and an F1-score of 91.64%. Conclusions: The proposed web-based deep learning approach can be very practical and accurate for spine segmentation as a diagnostic method.
목차
등록된 정보가 없습니다.
참고문헌 (17)
참고문헌 신청
Mayo Clinic / Back pain
Abu-Naser SS / 2016 / Lower back pain expert system diagnosis and treatment / J Multidiscip Eng Sci Stud 2(4):441~446
National Academies of Sciences / 2015 / Engineering, and Medicine. Improving diagnosis in health care / National Academies Press
Shiraishi J / 2011 / Computer-aided diagnosis and artificial intelligence in clinical imaging / Semin Nucl Med 41(6):449~462
Vania M / Automatic spine segmentation using convolutional neural network via redundant generation of class labels for 3D spine modeling
Abu-Naser SS / 2016 / Lower back pain expert system diagnosis and treatment / J Multidiscip Eng Sci Stud 2(4):441~446
National Academies of Sciences / 2015 / Engineering, and Medicine. Improving diagnosis in health care / National Academies Press
Shiraishi J / 2011 / Computer-aided diagnosis and artificial intelligence in clinical imaging / Semin Nucl Med 41(6):449~462
Vania M / Automatic spine segmentation using convolutional neural network via redundant generation of class labels for 3D spine modeling
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