TY - GEN
T1 - Spine Magnetic Resonance Image Segmentation Using Deep Learning Techniques
AU - Andrew, J.
AU - DIvyavarshini, Murathoti
AU - Barjo, Prerna
AU - Tigga, Irene
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - Spinal Malalignment is a chronic disease that is widespread across the world. It causes different diseases such as Stenosis, Scoliosis, Osteoporotic Fractures, Thoracolumbar fractures, Disc degeneration, etc. The diagnosis of such disease is generally done by analyzing the Magnetic Resonance Imaging (MRI) scan of the lumbar spine region. MRI analysis is done by well experienced medical professionals (radiologists and orthopedists). The flip side to this inspection is that it is time-consuming and may be subjected to a lack of accuracy. The manual segmentation of MRI scans from a large number of scan images is a tedious and time-consuming process. Thus, there is a need for automatic segmentation and analysis of spine MRI scans to improve clinical outputs and the accuracy of spinal measurements. In recent, the rise of deep learning technologies is making a revolution in medical systems. It is capable of analyzing a large amount of data and yield better accuracy. So, deep learning approaches can be efficiently used for the automatic segmentation of MRI scans. In this paper, an overview of spinal MRI segmentation using deep learning techniques is presented. The disease diagnosis from spine MRI is conferred. Then the state-of-art research in the automatic image segmentation using Convolutional Neural Network (CNN) is discussed. A comparative analysis is done on various deep learning techniques based on the performance metrics is presented. Finally, the evaluation metrics for automatic segmentation is provided along with the comparison of the state-of-art results.
AB - Spinal Malalignment is a chronic disease that is widespread across the world. It causes different diseases such as Stenosis, Scoliosis, Osteoporotic Fractures, Thoracolumbar fractures, Disc degeneration, etc. The diagnosis of such disease is generally done by analyzing the Magnetic Resonance Imaging (MRI) scan of the lumbar spine region. MRI analysis is done by well experienced medical professionals (radiologists and orthopedists). The flip side to this inspection is that it is time-consuming and may be subjected to a lack of accuracy. The manual segmentation of MRI scans from a large number of scan images is a tedious and time-consuming process. Thus, there is a need for automatic segmentation and analysis of spine MRI scans to improve clinical outputs and the accuracy of spinal measurements. In recent, the rise of deep learning technologies is making a revolution in medical systems. It is capable of analyzing a large amount of data and yield better accuracy. So, deep learning approaches can be efficiently used for the automatic segmentation of MRI scans. In this paper, an overview of spinal MRI segmentation using deep learning techniques is presented. The disease diagnosis from spine MRI is conferred. Then the state-of-art research in the automatic image segmentation using Convolutional Neural Network (CNN) is discussed. A comparative analysis is done on various deep learning techniques based on the performance metrics is presented. Finally, the evaluation metrics for automatic segmentation is provided along with the comparison of the state-of-art results.
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U2 - 10.1109/ICACCS48705.2020.9074218
DO - 10.1109/ICACCS48705.2020.9074218
M3 - Conference contribution
AN - SCOPUS:85084662171
T3 - 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020
SP - 945
EP - 950
BT - 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020
Y2 - 6 March 2020 through 7 March 2020
ER -