Review of Deep Learning Models for Spine Segmentation

Abstract

Medical image segmentation has been a long-standing chal- lenge due to the limitation in labeled datasets and the exis- tence of noise and artifacts. In recent years, deep learning has shown its capability in achieving successive progress in this field, making its automatic segmentation performance gradually catch up with that of manual segmentation. In this paper, we select twelve state-of-the-art models and compare their performance in the spine MRI segmentation task. We divide them into two categories. One of them is the U-Net family, including U-Net, Attention U-Net, ResUNet++, TransUNet, and MiniSeg. The architectures of these models often ultimately include the encoder-decoder structure, and their innovation generally lies in the way of better fusing low-level and high-level information. Models in the other category, named Models Using Backbone often use ResNet, Res2Net, or other pre-trained models on ImageNet as the backbone to extract information. These models pay more attention capturing multi-scale and rich contextual information. All models are trained and tested on the open-source spine M- RI dataset with 20 labels and no pre-training. Through the comparison, the models using backbone exceed U-Net family, and DeepLabv3+ works best. We suppose it is also necessary to extract multi-scale information in a multi-label medical segmentation task.