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논문 기본 정보

자료유형
학술저널
저자정보
Junghwa Kang (Hankuk University of Foreign Studies) Hyeonha Kim (Sungkyunkwan University) Eunjin Kim (Sungkyunkwan University) Eunbi Kim (Hankuk University of Foreign Studies) Hyebin Lee (The Catholic University of Korea) Na-young Shin (The Catholic University of Korea) 남윤호 (한국외국어대학교)
저널정보
대한자기공명의과학회 Investigative Magnetic Resonance Imaging Investigative Magnetic Resonance Imaging 제25권 제3호
발행연도
2021.9
수록면
156 - 163 (8page)

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Recently, neuromelanin and nigrosome imaging techniques have been developed to evaluate the substantia nigra in Parkinson’s disease. Previous studies have shown potential benefits of quantitative analysis of neuromelanin and nigrosome images in the substantia nigra, although visual assessments have been performed to evaluate structures in most studies. In this study, we investigate the potential of using deep learning based automatic region segmentation techniques for quantitative analysis of the substantia nigra. The deep convolutional neural network was trained to automatically segment substantia nigra regions on 3D nigrosome and neuromelanin sensitive MR images obtained from 30 subjects. With a 5-fold cross-validation, the mean calculated dice similarity coefficient between manual and deep learning was 0.70 ± 0.11. Although calculated dice similarity coefficients were relatively low due to empirically drawn margins, selected slices were overlapped for more than two slices of all subjects. Our results demonstrate that deep convolutional neural network-based method could provide reliable localization of substantia nigra regions on neuromelanin and nigrosome sensitive MR images.

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