Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images

Cerebrovascular segmentation is important in various clinical applications, such as surgical planning and computer-aided diagnosis. In order to achieve high segmentation performance, three challenging problems should be taken into consideration: (1) large variations in vascular anatomies and voxel i...

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Autores principales:	Bin Guo, Fugen Zhou, Bo Liu, Xiangzhi Bai
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	index terms-class imbalance image noise CNN GAN medical image segmentation Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/98a6599f25f1483da5e145fbba7a0f06
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spelling	oai:doaj.org-article:98a6599f25f1483da5e145fbba7a0f062021-11-16T07:39:22ZVoxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images1662-453X10.3389/fnins.2021.756536https://doaj.org/article/98a6599f25f1483da5e145fbba7a0f062021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.756536/fullhttps://doaj.org/toc/1662-453XCerebrovascular segmentation is important in various clinical applications, such as surgical planning and computer-aided diagnosis. In order to achieve high segmentation performance, three challenging problems should be taken into consideration: (1) large variations in vascular anatomies and voxel intensities; (2) severe class imbalance between foreground and background voxels; (3) image noise with different magnitudes. Limited accuracy was achieved without considering these challenges in deep learning-based methods for cerebrovascular segmentation. To overcome the limitations, we propose an end-to-end adversarial model called FiboNet-VANGAN. Specifically, our contributions can be summarized as follows: (1) to relieve the first problem mentioned above, a discriminator is proposed to regularize for voxel-wise distribution consistency between the segmentation results and the ground truth; (2) to mitigate the problem of class imbalance, we propose to use the addition of cross-entropy and Dice coefficient as the loss function of the generator. Focal loss is utilized as the loss function of the discriminator; (3) a new feature connection is proposed, based on which a generator called FiboNet is built. By incorporating Dice coefficient in the training of FiboNet, noise robustness can be improved by a large margin. We evaluate our method on a healthy magnetic resonance angiography (MRA) dataset to validate its effectiveness. A brain atrophy MRA dataset is also collected to test the performance of each method on abnormal cases. Results show that the three problems in cerebrovascular segmentation mentioned above can be alleviated and high segmentation accuracy can be achieved on both datasets using our method.Bin GuoFugen ZhouBo LiuXiangzhi BaiFrontiers Media S.A.articleindex terms-class imbalanceimage noiseCNNGANmedical image segmentationNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	index terms-class imbalance image noise CNN GAN medical image segmentation Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	index terms-class imbalance image noise CNN GAN medical image segmentation Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Bin Guo Fugen Zhou Bo Liu Xiangzhi Bai Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images
description	Cerebrovascular segmentation is important in various clinical applications, such as surgical planning and computer-aided diagnosis. In order to achieve high segmentation performance, three challenging problems should be taken into consideration: (1) large variations in vascular anatomies and voxel intensities; (2) severe class imbalance between foreground and background voxels; (3) image noise with different magnitudes. Limited accuracy was achieved without considering these challenges in deep learning-based methods for cerebrovascular segmentation. To overcome the limitations, we propose an end-to-end adversarial model called FiboNet-VANGAN. Specifically, our contributions can be summarized as follows: (1) to relieve the first problem mentioned above, a discriminator is proposed to regularize for voxel-wise distribution consistency between the segmentation results and the ground truth; (2) to mitigate the problem of class imbalance, we propose to use the addition of cross-entropy and Dice coefficient as the loss function of the generator. Focal loss is utilized as the loss function of the discriminator; (3) a new feature connection is proposed, based on which a generator called FiboNet is built. By incorporating Dice coefficient in the training of FiboNet, noise robustness can be improved by a large margin. We evaluate our method on a healthy magnetic resonance angiography (MRA) dataset to validate its effectiveness. A brain atrophy MRA dataset is also collected to test the performance of each method on abnormal cases. Results show that the three problems in cerebrovascular segmentation mentioned above can be alleviated and high segmentation accuracy can be achieved on both datasets using our method.
format	article
author	Bin Guo Fugen Zhou Bo Liu Xiangzhi Bai
author_facet	Bin Guo Fugen Zhou Bo Liu Xiangzhi Bai
author_sort	Bin Guo
title	Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images
title_short	Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images
title_full	Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images
title_fullStr	Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images
title_full_unstemmed	Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images
title_sort	voxel-wise adversarial fibonet for 3d cerebrovascular segmentation on magnetic resonance angiography images
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/98a6599f25f1483da5e145fbba7a0f06
work_keys_str_mv	AT binguo voxelwiseadversarialfibonetfor3dcerebrovascularsegmentationonmagneticresonanceangiographyimages AT fugenzhou voxelwiseadversarialfibonetfor3dcerebrovascularsegmentationonmagneticresonanceangiographyimages AT boliu voxelwiseadversarialfibonetfor3dcerebrovascularsegmentationonmagneticresonanceangiographyimages AT xiangzhibai voxelwiseadversarialfibonetfor3dcerebrovascularsegmentationonmagneticresonanceangiographyimages
_version_	1718426620287516672

Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images

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