LSS-UNET: Lumbar spinal stenosis semantic segmentation using deep learning

The most important information to be noted about LSS not a hernia. While a hernia occurs with a rupture in the disc, LSS occurs as a result of calcification due to deformation of the bone in the following years. In addition, the correct interpretation and diagnosis of biomedical images requires serious expertise, making the diagnosis of LSS difficult. Looking at the literature, the U-Net method can perform semantic segmentation with high success. In recent years, it has been seen in the literature that the success of the classical U-Net has increased when the architecture of different deep learning methods has been applied. In order to segment the LSS region, semantic segmentation was performed on lumbar spine MR images with 3 different deep learning methods. The success of these methods was calculated by Dice and IoU scores. The highest segmentation success among 1560 images was obtained in the ResUNet model with 0.93 DICE score. LSS treatment, which negatively affects human life, is very important because of the difficulty of interpreting MR images and the confusion of LSS with lumbar hernia. Today, expert decision support systems have become essential for correct diagnosis, which is the most important feature of starting a treatment/surgical operation. Especially the high success of classification/segmentation obtained by deep learning methods has also been demonstrated in LSS segmentation, which is the subject of our study.