Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation
One of the most accurate and non-invasive prostate imaging methods is magnetic resonance imaging (MRI). Segmentation is needed to find the boundary of the prostate, either automatically or semi-automatically. Recently, fully convolutional neural networks (FCNN) are being used for this purpose. In th...
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doaj-d27d622011154a848af9c2fb61fc78a22021-03-29T22:12:53ZengIEEEIEEE Access2169-35362019-01-017367483676010.1109/ACCESS.2019.29032848666973Convolutional Neural Networks for Prostate Magnetic Resonance Image SegmentationTahereh Hassanzadeh0https://orcid.org/0000-0003-2611-8157Leonard G. C. Hamey1Kevin Ho-Shon2Department of Computing, Macquarie University, Sydney, NSW, AustraliaDepartment of Computing, Macquarie University, Sydney, NSW, AustraliaFaculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, AustraliaOne of the most accurate and non-invasive prostate imaging methods is magnetic resonance imaging (MRI). Segmentation is needed to find the boundary of the prostate, either automatically or semi-automatically. Recently, fully convolutional neural networks (FCNN) are being used for this purpose. In this paper, to improve the FCNN performance for prostate MRI segmentation, we analyze various structures of shortcut connections together with the size of a deep network and suggest eight different FCNNs-based deep 2D network structures for automatic MRI prostate segmentation. Our evaluations on the PROMISE12 dataset with ten-fold cross-validation indicate improved and competitive results. We analyze the results in detail, considering MRI slices, MRI volumes, test folds, and also the impact on prostate segmentation of using an EndoRectal Coil to capture the prostate MRI. Our best 2D network outperforms the state-of-the-art 3D FCNN-based methods for prostate MRI segmentation on publicly available data, without any further post-processing.https://ieeexplore.ieee.org/document/8666973/Automatic MRI segmentationfully convolutional neural networkprostate MRI segmentation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tahereh Hassanzadeh Leonard G. C. Hamey Kevin Ho-Shon |
spellingShingle |
Tahereh Hassanzadeh Leonard G. C. Hamey Kevin Ho-Shon Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation IEEE Access Automatic MRI segmentation fully convolutional neural network prostate MRI segmentation |
author_facet |
Tahereh Hassanzadeh Leonard G. C. Hamey Kevin Ho-Shon |
author_sort |
Tahereh Hassanzadeh |
title |
Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation |
title_short |
Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation |
title_full |
Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation |
title_fullStr |
Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation |
title_full_unstemmed |
Convolutional Neural Networks for Prostate Magnetic Resonance Image Segmentation |
title_sort |
convolutional neural networks for prostate magnetic resonance image segmentation |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
One of the most accurate and non-invasive prostate imaging methods is magnetic resonance imaging (MRI). Segmentation is needed to find the boundary of the prostate, either automatically or semi-automatically. Recently, fully convolutional neural networks (FCNN) are being used for this purpose. In this paper, to improve the FCNN performance for prostate MRI segmentation, we analyze various structures of shortcut connections together with the size of a deep network and suggest eight different FCNNs-based deep 2D network structures for automatic MRI prostate segmentation. Our evaluations on the PROMISE12 dataset with ten-fold cross-validation indicate improved and competitive results. We analyze the results in detail, considering MRI slices, MRI volumes, test folds, and also the impact on prostate segmentation of using an EndoRectal Coil to capture the prostate MRI. Our best 2D network outperforms the state-of-the-art 3D FCNN-based methods for prostate MRI segmentation on publicly available data, without any further post-processing. |
topic |
Automatic MRI segmentation fully convolutional neural network prostate MRI segmentation |
url |
https://ieeexplore.ieee.org/document/8666973/ |
work_keys_str_mv |
AT taherehhassanzadeh convolutionalneuralnetworksforprostatemagneticresonanceimagesegmentation AT leonardgchamey convolutionalneuralnetworksforprostatemagneticresonanceimagesegmentation AT kevinhoshon convolutionalneuralnetworksforprostatemagneticresonanceimagesegmentation |
_version_ |
1724192108490784768 |