2.5D cascaded context-based network for liver and tumor segmentation from CT images

Rongrong Bi; Liang Guo; Botao Yang; Jinke Wang; Changfa Shi; Rongrong Bi; Liang Guo; Botao Yang; Jinke Wang; Changfa Shi

doi:10.3934/era.2023221

Electronic Research Archive

2023, Volume 31, Issue 8: 4324-4345. doi: 10.3934/era.2023221

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2.5D cascaded context-based network for liver and tumor segmentation from CT images

1.
Department of Software Engineering, Harbin University of Science and Technology, Rongcheng 264300, China
2.
School of Automation, Harbin University of Science and Technology, Harbin 150080, China
3.
Mobile E-business Collaborative Innovation Center of Hunan Province, Hunan University of Technology and Business, Changsha 410205, China

Received: 09 May 2023 Revised: 28 May 2023 Accepted: 30 May 2023 Published: 07 June 2023

The existing 2D/3D strategies still have limitations in human liver and tumor segmentation efficiency. Therefore, this paper proposes a 2.5D network combing cascaded context module (CCM) and Ladder Atrous Spatial Pyramid Pooling (L-ASPP), named CCLNet, for automatic liver and tumor segmentation from CT. First, we utilize the 2.5D mode to improve the training efficiency; Second, we employ the ResNet-34 as the encoder to enhance the segmentation accuracy. Third, the L-ASPP module is used to enlarge the receptive field. Finally, the CCM captures more local and global feature information. We experimented on the LiTS17 and 3DIRCADb datasets. Experimental results prove that the method skillfully balances accuracy and cost, thus having good prospects in liver and liver segmentation in clinical assistance.
- liver and tumor,
- segmentation,
- cascaded context,
- ASPP,
- deep learning
Citation: Rongrong Bi, Liang Guo, Botao Yang, Jinke Wang, Changfa Shi. 2.5D cascaded context-based network for liver and tumor segmentation from CT images[J]. Electronic Research Archive, 2023, 31(8): 4324-4345. doi: 10.3934/era.2023221

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Abstract

The existing 2D/3D strategies still have limitations in human liver and tumor segmentation efficiency. Therefore, this paper proposes a 2.5D network combing cascaded context module (CCM) and Ladder Atrous Spatial Pyramid Pooling (L-ASPP), named CCLNet, for automatic liver and tumor segmentation from CT. First, we utilize the 2.5D mode to improve the training efficiency; Second, we employ the ResNet-34 as the encoder to enhance the segmentation accuracy. Third, the L-ASPP module is used to enlarge the receptive field. Finally, the CCM captures more local and global feature information. We experimented on the LiTS17 and 3DIRCADb datasets. Experimental results prove that the method skillfully balances accuracy and cost, thus having good prospects in liver and liver segmentation in clinical assistance.

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