Multitask Deep Learning for Segmentation and Lumbosacral Spine Inspection

Multi-task learning has achieved notable progress in many medical applications. In this paper, we propose a multi-task neural network, MRNet, for segmentation and spinal parameter inspection. It is developed based on the multi-path convolutional neural network for the robust detection of obscure regions on X-ray images. The proposed MRNet has two branches. One is for the segmentation of lumbar vertebrae, sacrum, and femoral heads. It shares the main features with the second branch for detection and classification by supervised learning. The output of the second branch is used to estimate the parameters for lumbosacral spine inspection. We conduct this research on our dataset collected and annotated by doctors for model training and performance evaluation. The datasets are used to train our MRNet as well as other networks for performance evaluation. Compared to the state-of-the-art techniques, the proposed MRNet is capable of X-ray image segmentation and parameter estimation with very limited training data. The results have demonstrated the feasibility of our MRNet for the segmentation of lumbar vertebrae, as well as the automated parameter prediction for lumbosacral spine inspection. Code is available at https://github.com/LuanTran07/BiLUnet-Lumbar-Spine.