Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components
Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, wi...
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oai:112.137.131.14:VNU_123-890992020-06-23T03:46:53Z Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components Hoang, Hong Son Pham, Cam Phuong Theo, van Walsum Luu, Manh Ha Liver segmentations CNNs Connected Components Post processing Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distance, mean surface distance, and false positive rate between the liver segmentation and the ground truth. The quantitative results demonstrate that the LCC algorithm statistically significantly improves results of the liver segmentation on non-contrast enhanced and low-dose images for all three CNNs. The combination with V-net shows the best performance in Dice score (higher than 90%), while the DRIU network achieves the smallest computation time (2 to 6 seconds) for a single segmentation on average. 2020-06-23T03:46:53Z 2020-06-23T03:46:53Z 2020 Article Hoang, H. S., et al. (2020). Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components. VNU Journal of Science: Comp. Science & Com. Eng, Vol. 36, No. 1 (2020) 25-37. 2588-1086 http://repository.vnu.edu.vn/handle/VNU_123/89099 https://doi.org/10.25073/2588-1086/vnucsce.241 en Computer Science and Communication Engineering; application/pdf H. : ĐHQGHN |
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Liver segmentations CNNs Connected Components Post processing Hoang, Hong Son Pham, Cam Phuong Theo, van Walsum Luu, Manh Ha Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components |
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Liver segmentation is relevant for several clinical applications. Automatic liver segmentation using convolutional neural networks (CNNs) has been recently investigated. In this paper, we propose a new approach of combining a largest connected component (LCC) algorithm, as a post-processing step, with CNN approaches to improve liver segmentation accuracy. Specifically, in this study, the algorithm is combined with three well-known CNNs for liver segmentation: FCN-CRF, DRIU and V-net. We perform the experiment on a variety of liver CT images, ranging from non-contrast enhanced CT images to low-dose contrast enhanced CT images. The methods are evaluated using Dice score, Haudorff distance, mean surface distance, and false positive rate between the liver segmentation and the ground truth. The quantitative results demonstrate that the LCC algorithm statistically significantly improves results of the liver segmentation on non-contrast enhanced and low-dose images for all three CNNs. The combination with V-net shows the best performance in Dice score (higher than 90%), while the DRIU network achieves the smallest computation time (2 to 6 seconds) for a single segmentation on average. |
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Article |
author |
Hoang, Hong Son Pham, Cam Phuong Theo, van Walsum Luu, Manh Ha |
author_facet |
Hoang, Hong Son Pham, Cam Phuong Theo, van Walsum Luu, Manh Ha |
author_sort |
Hoang, Hong Son |
title |
Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components |
title_short |
Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components |
title_full |
Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components |
title_fullStr |
Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components |
title_full_unstemmed |
Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components |
title_sort |
liver segmentation on a variety of computed tomography (ct) images based on convolutional neural networks combined with connected components |
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H. : ĐHQGHN |
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2020 |
url |
http://repository.vnu.edu.vn/handle/VNU_123/89099 https://doi.org/10.25073/2588-1086/vnucsce.241 |
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