Investig Magn Reson Imaging.  2021 Sep;25(3):156-163. 10.13104/imri.2021.25.3.156.

Convolutional Neural Network-Based Automatic Segmentation of Substantia Nigra on Nigrosome and Neuromelanin Sensitive MR Images

• Kang J
• Kim H
• Kim E
• Kim E
• Lee H
• Shin Ny
• Nam Y

• KMID: 2520439
• DOI: http://doi.org/10.13104/imri.2021.25.3.156

Abstract

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.

Keyword

Parkinson’s disease; Substantia nigra; Deep learning; Image segmentation; Nigrosome; neuromelanin