Deep Learning Framework for Fully Automated Intervertebral Disc Localization and Segmentation from Multi-modality MR Images

Yunhe Gao

Abstract: Intervertebral discs are joints that lie between vertebrae in the spinal column, which absorb shock between vertebrae during activities. There is a strong correlation between lower back pain and degeneration of intervertebral discs, which may have a great impact on peoples normal life. The precise segmentation of the intervertebral disc is of great significance for the diagnosis of disc degeneration. Currently clinical practice usually manually annotates the volumetric data, which is time-consuming, tedious, needs a lot of expertise and lacks of reproducibility. In this challenge, we developed a fully automated framework that can accurately segment and locate seven intervertebral discs. First, we delicately designed a powerful segmentation network which is a 2D fully convolutional neural network with densely connected atrous spatial pyramid pooling to capture and fuse multi-scale context information. Then we used a localization network and a robust post-process scheme to distinguish different IVD instance. Further more, we proposed a novel training strategy that can make the segmentation network focus on the spine region. The effectiveness of our algorithm is proven in the challenge, we achieved the mean segmentation Dice coefficient of 90.58% and a mean localization error of 0.78 mm.