Spinal Cord Segmentation in Ultrasound Medical Imagery

Abstract

In this paper, we study and evaluate the task of semantic segmentation of the spinal cord in ultrasound medical imagery. This task is useful for neurosurgeons to analyze the spinal cord movement during and after the laminectomy surgical operation. Laminectomy is performed on patients that suffer from an abnormal pressure made on the spinal cord. The surgeon operates by cutting the bones of the laminae and the intervening ligaments to relieve this pressure. During the surgery, ultrasound waves can pass through the laminectomy area to give real-time exploitable images of the spinal cord. The surgeon uses them to confirm spinal cord decompression or, occasionally, to assess a tumor adjacent to the spinal cord. The Freely pulsating spinal cord is a sign of adequate decompression. To evaluate the semantic segmentation approaches chosen in this study, we constructed two datasets using images collected from 10 different patients performing the laminectomy surgery. We found that the best solution for this task is Fully Convolutional DenseNets if the spinal cord is already in the train set. If the spinal cord does not exist in the train set, U-Net is the best. We also studied the effect of integrating inside both models some deep learning components like Atrous Spatial Pyramid Pooling (ASPP) and Depthwise Separable Convolution (DSC). We added a post-processing step and detailed the configurations to set for both models.