Liver vessel segmentation based on inter-scale V-Net

Jinzhu Yang; Meihan Fu; Ying Hu; Jinzhu Yang; Meihan Fu; Ying Hu

doi:10.3934/mbe.2021217

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 4327-4340. doi: 10.3934/mbe.2021217

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Liver vessel segmentation based on inter-scale V-Net

Jinzhu Yang ^{1
,
,},
Meihan Fu ²,
Ying Hu ^{2
,
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1.
Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education Northeastern University, Shenyang 110000, China
2.
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116000, China

Received: 15 March 2021 Accepted: 10 May 2021 Published: 18 May 2021

Segmentation and visualization of liver vessel is a key task in preoperative planning and computer-aided diagnosis of liver diseases. Due to the irregular structure of liver vessel, accurate liver vessel segmentation is difficult. This paper proposes a method of liver vessel segmentation based on an improved V-Net network. Firstly, a dilated convolution is introduced into the network to make the network can still enlarge the receptive field without reducing down-sampling and save detailed spatial information. Secondly, a 3D deep supervision mechanism is introduced into the network to speed up the convergence of the network and help the network learn semantic features better. Finally, inter-scale dense connections are designed in the decoder of the network to prevent the loss of high-level semantic information during the decoding process and effectively integrate multi-scale feature information. The public datasets 3Dircadb were used to perform liver vessel segmentation experiments. The average dice and sensitivity of the proposed method reached 71.6 and 75.4%, respectively, which are higher than those of the original network. The experimental results show that the improved V-Net network can automatically and accurately segment labeled or even other unlabeled liver vessels from the CT images.
- liver vessel,
- V-Net, dilated convolution,
- 3D deep supervision mechanism,
- inter-scale dense connections
Citation: Jinzhu Yang, Meihan Fu, Ying Hu. Liver vessel segmentation based on inter-scale V-Net[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4327-4340. doi: 10.3934/mbe.2021217

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Abstract

Segmentation and visualization of liver vessel is a key task in preoperative planning and computer-aided diagnosis of liver diseases. Due to the irregular structure of liver vessel, accurate liver vessel segmentation is difficult. This paper proposes a method of liver vessel segmentation based on an improved V-Net network. Firstly, a dilated convolution is introduced into the network to make the network can still enlarge the receptive field without reducing down-sampling and save detailed spatial information. Secondly, a 3D deep supervision mechanism is introduced into the network to speed up the convergence of the network and help the network learn semantic features better. Finally, inter-scale dense connections are designed in the decoder of the network to prevent the loss of high-level semantic information during the decoding process and effectively integrate multi-scale feature information. The public datasets 3Dircadb were used to perform liver vessel segmentation experiments. The average dice and sensitivity of the proposed method reached 71.6 and 75.4%, respectively, which are higher than those of the original network. The experimental results show that the improved V-Net network can automatically and accurately segment labeled or even other unlabeled liver vessels from the CT images.

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