Paper Info
Title
HMOE-Net - Hybrid Multi-scale Object Equalization Network for Intracerebral Hemorrhage Segmentation in CT Images.
Abstract
In this paper, we propose a novel Hybrid Multi-scale Object Equalization Network (HMOE-Net) to segment intracerebral hemorrhage (ICH) regions. In particular, we design a shallow feature extraction network (SFENet) and a deep feature extraction network (DFENet) to solve the problem of equalization learning of hybrid multi-scale object features. The multi-level feature extraction (MLFE) blocks are presented in DFENet to explore multi-level semantic features more effectively. Furthermore, we adopt a progressive feature extraction strategy combining SFENet and DFENet to further consider the differences of various ICH regions and achieve the equalization feature learning of multi-scale objects. To verify the effectiveness of HMOE-Net, we collect a clinical ICH dataset with a total of 500 CT cases from three hospitals for the evaluation. The experimental results show that HMOE-Net is superior to six state-of-the-art methods and achieves accurate segmentation for multi-scale ICH regions.
Year
DOI
Venue
2020
10.1109/BIBM49941.2020.9313439
BIBM
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Xizhi He100.34
Kai Chen25914.81
Kai Hu3468.62
Zhineng Chen419225.29
Xuanya Li5169.22
Xieping Gao610024.43