This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain. It was collected from four different hospitals and was divided into a training (179 patients) and validation (39 patients) set. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. We set up a continuous segmentation challenge to allow for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation, and improve the diagnostic value of lumbar spine MRI.

本文介绍了一个公开的包含多个研究中心的腰椎磁共振成像（MRI）数据集，其中包括底部疼痛病史的218名患者的447个MRI系列，并使用迭代数据标注方法训练了一个分割算法，该算法提供了基准算法和相似的nnU-Net引擎性能基准，并且可以用于比较不同的分割算法的公正性连续分割挑战。

MR图像中腰椎分割：数据集和公共基准