École Centrale de Lyon, Lyon, France; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
Magn Reson Imaging. 2019 Dec;64:21-27. doi: 10.1016/j.mri.2019.04.009. Epub 2019 Apr 17.
This paper presents an open-source pipeline to train neural networks to segment structures of interest from MRI data. The pipeline is tailored towards homogeneous datasets and requires relatively low amounts of manual segmentations (few dozen, or less depending on the homogeneity of the dataset). Two use-case scenarios for segmenting the spinal cord white and grey matter are presented: one in marmosets with variable numbers of lesions, and the other in the publicly available human grey matter segmentation challenge [1]. The pipeline is freely available at: https://github.com/neuropoly/multiclass-segmentation.
本文提出了一个开源的流水线,用于训练神经网络从 MRI 数据中分割感兴趣的结构。该流水线针对同质数据集进行了定制,并且只需要相对较少的手动分割(根据数据集的同质性,几十个或更少)。本文提出了两种用于分割脊髓白质和灰质的应用案例:一种是在有不同数量病变的狨猴中,另一种是在公开的人类灰质分割挑战[1]中。该流水线可在以下网址免费获取:https://github.com/neuropoly/multiclass-segmentation。