A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis
Precise and reliable segmentation of vertebral column is crucial for accurate computer aided diagnostic system (CADx). It assists the radiologists and doctors in identifying various pathologies in the vertebrae with better visualizations. Accurate segmentation of vertebral disks and bones from the m...
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doaj-add2bb666eef4855bd2b4577e2dd8c4c2021-03-30T00:03:19ZengIEEEIEEE Access2169-35362019-01-01712038712039810.1109/ACCESS.2019.29364928807102A Robust Scheme of Vertebrae Segmentation for Medical DiagnosisFaisal Rehman0https://orcid.org/0000-0003-0453-4336Syed Irtiza Ali Shah1Naveed Riaz2Syed Omer Gilani3Department of Robotics and Intelligent Machine Engineering, National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Aerospace Engineering, National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Robotics and Intelligent Machine Engineering, National University of Sciences and Technology (NUST), Islamabad, PakistanPrecise and reliable segmentation of vertebral column is crucial for accurate computer aided diagnostic system (CADx). It assists the radiologists and doctors in identifying various pathologies in the vertebrae with better visualizations. Accurate segmentation of vertebral disks and bones from the medical images is a tough job and becomes more challenging when dealing with various deformities and pathologies. While remarkable success was achieved by deep convolutional neural networks (DCNNs) in medical image segmentation, it is still a difficult task for DCNNs to handle the medical image segmentation problems with various deformities and anatomical complexities. In this paper, we propose a novel and efficient framework to address the subject problem by integrating a parametric level set approach in deep convolutional neural networks. The proposed scheme utilizes the probability map of pre-trained deep network to initialize the level set and it refines the output iteratively under the action of various forces to fine-tune the training of deep network. Thus the learning of the network is improved and the network is able to accommodate high topological shape variations in the vertebrae. This proposed method was evaluated on two different datasets. The first one is 20 publically available 3D spine MRI dataset to perform disc segmentation and the second one is 173 computed tomography scans for thoracolumbar (thoracic and lumbar) vertebrae segmentation. The dice score was found to be 90.37 ± 0.9 percent for disks segmentation and 94.7 ± 1.1 with ASSD of 0.1 ± 0.04 mm for thoracolumbar vertebrae segmentation. The results reveal that our proposed method is robust over multiple segmentations and outperformed the recently published state of art methods.https://ieeexplore.ieee.org/document/8807102/Intervertebral diskthoracolumbar vertebraecomputed tomographyvertebrae segmentationdeep learningcomputer aided diagnosis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Faisal Rehman Syed Irtiza Ali Shah Naveed Riaz Syed Omer Gilani |
spellingShingle |
Faisal Rehman Syed Irtiza Ali Shah Naveed Riaz Syed Omer Gilani A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis IEEE Access Intervertebral disk thoracolumbar vertebrae computed tomography vertebrae segmentation deep learning computer aided diagnosis |
author_facet |
Faisal Rehman Syed Irtiza Ali Shah Naveed Riaz Syed Omer Gilani |
author_sort |
Faisal Rehman |
title |
A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis |
title_short |
A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis |
title_full |
A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis |
title_fullStr |
A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis |
title_full_unstemmed |
A Robust Scheme of Vertebrae Segmentation for Medical Diagnosis |
title_sort |
robust scheme of vertebrae segmentation for medical diagnosis |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Precise and reliable segmentation of vertebral column is crucial for accurate computer aided diagnostic system (CADx). It assists the radiologists and doctors in identifying various pathologies in the vertebrae with better visualizations. Accurate segmentation of vertebral disks and bones from the medical images is a tough job and becomes more challenging when dealing with various deformities and pathologies. While remarkable success was achieved by deep convolutional neural networks (DCNNs) in medical image segmentation, it is still a difficult task for DCNNs to handle the medical image segmentation problems with various deformities and anatomical complexities. In this paper, we propose a novel and efficient framework to address the subject problem by integrating a parametric level set approach in deep convolutional neural networks. The proposed scheme utilizes the probability map of pre-trained deep network to initialize the level set and it refines the output iteratively under the action of various forces to fine-tune the training of deep network. Thus the learning of the network is improved and the network is able to accommodate high topological shape variations in the vertebrae. This proposed method was evaluated on two different datasets. The first one is 20 publically available 3D spine MRI dataset to perform disc segmentation and the second one is 173 computed tomography scans for thoracolumbar (thoracic and lumbar) vertebrae segmentation. The dice score was found to be 90.37 ± 0.9 percent for disks segmentation and 94.7 ± 1.1 with ASSD of 0.1 ± 0.04 mm for thoracolumbar vertebrae segmentation. The results reveal that our proposed method is robust over multiple segmentations and outperformed the recently published state of art methods. |
topic |
Intervertebral disk thoracolumbar vertebrae computed tomography vertebrae segmentation deep learning computer aided diagnosis |
url |
https://ieeexplore.ieee.org/document/8807102/ |
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