This paper focuses on the segmentation of lumbar spine high-resolution magnetic resonance imaging. We designed an end-to-end deep learning-based model for automatic segmentation. Additionally, our method includes the automatic quantification of the herniated disc by incorporating acquisition parameters. We collected high-resolution magnetic resonance imaging from 17 patients with lumbar disc herniation. Based on deep learning techniques, we designed a convolutional neural network that accepts 3D inputs. The segmentation results of our model show high similarity to those obtained through manual segmentation, with the mean dice coefficient exceeding 0.8. By incorporating the acquisition parameters of magnetic resonance imaging, the automatic quantification method has an accuracy comparable to that of manual segmentation. Our research demonstrates that the designed deep learning model can reliably extract key features and reconstruct critical structures. Our method has the potential to be allowed for potential routine reporting in the clinical setting.