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Spinal Cord Segmentation in Ultrasound Medical Imagery

Robotics and Internet of Things Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
Research Laboratory Smart Electricity & ICT, SEICT, LR18ES44, National Engineering School of Carthage, University of Carthage, Charguia II Tunis-Carthage 2035, Tunisia
Information System Department, College of Applied Computer Science, King Saud University, Riyadh 11543, Saudi Arabia
Departments of Neurosurgery, University of Calgary, Foothills Medical Center, Calgary, AB T2N 1N4, Canada
Division of Neurosurgery, Department of Surgery, College of Medicine, King Saud University, Riyadh 11472, Saudi Arabia
Raytheon Chair for Systems Engineering (RCSE Chair), Advanced Manufacturing Institute, King Saud University, Riyadh 11451, Saudi Arabia
Electrical Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(4), 1370;
Received: 9 December 2019 / Revised: 10 February 2020 / Accepted: 12 February 2020 / Published: 18 February 2020
(This article belongs to the Special Issue Image Processing Techniques for Biomedical Applications)
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. View Full-Text
Keywords: ultrasound; deep learning; laminectomy; spinal cord; spinal cord pulsation; Convolutional Neural Networks (CNN); Densenet; semantic segmentation; medical image segmentation ultrasound; deep learning; laminectomy; spinal cord; spinal cord pulsation; Convolutional Neural Networks (CNN); Densenet; semantic segmentation; medical image segmentation
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MDPI and ACS Style

Benjdira, B.; Ouni, K.; Al Rahhal, M.M.; Albakr, A.; Al-Habib, A.; Mahrous, E. Spinal Cord Segmentation in Ultrasound Medical Imagery. Appl. Sci. 2020, 10, 1370.

AMA Style

Benjdira B, Ouni K, Al Rahhal MM, Albakr A, Al-Habib A, Mahrous E. Spinal Cord Segmentation in Ultrasound Medical Imagery. Applied Sciences. 2020; 10(4):1370.

Chicago/Turabian Style

Benjdira, Bilel, Kais Ouni, Mohamad M. Al Rahhal, Abdulrahman Albakr, Amro Al-Habib, and Emad Mahrous. 2020. "Spinal Cord Segmentation in Ultrasound Medical Imagery" Applied Sciences 10, no. 4: 1370.

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