# Spinal Cord Segmentation in Ultrasound Medical Imagery
###### Abstract
It can also have other symptoms that can interfere with daily life. The surgical procedure creates more space for the spinal cord and nerve roots to relieve abnormal pressure on the spinal cord by removing the laminae and the intervening ligaments [3]. Figure 3 shows the laminectomy surgery and the spinal cord. In normal situations, the spinal cord could not be imaged using ultrasound imaging because ultrasonic waves do not pass well through bones [8]. In fact, the spinal cord is completely enclosed inside the bones of the spinal column. During laminectomy, ultrasonic waves can pass through the created bone defect to give real-time exploitable images of the spinal cord. Ultrasound imaging can demonstrate the spinal cord and the surrounding structures. This helps greatly to confirm the adequacy of the spinal cord decompression [8]. The ultrasound is also able to detect spinal cord pulsation and record it in a video format [8]. The created video can be saved for later use and analysis. It is believed that a decompressed spinal cord tends to have better pulsation. Spinal cord pulsation, however, is variable from one person to another [8]. Providing an automatic solution for detecting the exact boundaries of the spinal cord is helpful for automatic interpretation and analysis of spinal cord pulsation. This spinal cord pulsation values could be interpreted alongside the electrocardiogram (the electrical activity of the heart) to interpret if both signals are synchronized, or there is some latency to interpret [8]. But the great challenge here is the quality of the ultrasound image as boundaries could be obscured and some parts could be of foggy appearance [8]. This is due to the different attenuations applied to the sonographic waves while passing through the human body. This makes the automatic segmentation more difficult to achieve. But the service that gives this solution for neurosurgeons strengthens the interest for further examination to resolve this issue. Figure 4 gives a clear representation of how the spinal cord is represented in ultrasound imaging. Figure 3: The laminectomy surgery. Figure 2: The spinal cord inside the vertebral column. Since 2012 [9] deep learning based approaches have shown an attractive efficiency in object segmentation for multiple types of imaging (RGB imaging, aerial imaging, multi-spectral imaging etc.) [10; 11; 12; 13; 14]. This was a source of inspiration for the medical imaging community to move their interest towards a great adoption of these approaches for different medical imaging modalities (MRI, CT Scan, 2D Ultrasound, 3D Ultrasound etc.) [4]. Concerning the ultrasound medical segmentation, different works have been made in the detection of different parts of the body [4] (breast image segmentation, vessel image segmentation, heart image segmentation etc.). However, as demonstrated in Section 2, no one has treated the spinal cord image segmentation in 2D ultrasound medical imagery. In this study, we deduced, based on the current state of the art of image semantic segmentation, the best solution to adopt for our specific task. Our objectives in this paper were:  
* Treating, for the first time, the problem of spinal cord segmentation in ultrasound imaging. * Introducing, based on the state of the art in the area of image semantic segmentation, the best model for two case studies. The first case study is when the spinal cord is already in the train set of the model. The second case study is when the spinal cord does not belong to the train set of the model. We constructed a separate dataset for each scenario and selected the best model in each case study. * Studying the integration of some successful deep learning components like ASPP (Atrous Spatial Pyramid Pooling) and DSC (Depthwise Separable Convolution) inside the selected models. * Improving the performance of the chosen model by adding a post-processing step and selecting the right configuration setting for both models.