2.5D cascaded context-based network for liver and tumor segmentation from CT images-Reference-Cited by-同舟云学术

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

Published:2023 Issue:8 Volume:31 Page:4324-4345
ISSN:2688-1594
Container-title:Electronic Research Archive
language:
Short-container-title:era

Author:

Bi Rongrong¹,Guo Liang²,Yang Botao¹,Wang Jinke¹²,Shi Changfa³

Affiliation:

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

Abstract

<abstract> <p>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.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

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