Paper Info
Title
FcTC-UNet: Fine-grained Combination of Transformer and CNN for Thoracic Organs Segmentation.
Abstract
Precise segmentation of organs at risk (OARs) in computed tomography (CT) images is an essential step for lung cancer radiotherapy. However, the manual delineation of OARs is time-consuming and subject to inter-observer variation. Although U-like architecture has achieved great success in medical image segmentation recently, it exhibits the limitations in modeling long-range dependencies. As an alternative structure, Transformers have emerged due to the outstanding capability of capturing the global contextual information provided by Self-Attention(SA) mechanism. However, Transformers need more computational cost than CNNs for introducing the SA module. In this paper, we propose a novel module named fine-grained combination of Transformer and CNN(FcTC). FcTC module is composed of dual-path extractor and fusing unit to effectively extract local information and model long-distance dependency. Then we build FcTC-UNet to automatically segment the OARs in thoracic CT images. The experiments results demonstrate that the proposed method achieves better performance over other state-of-the-art methods.
Year
DOI
Venue
2022
10.1109/EMBC48229.2022.9870880
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DocType
Volume
ISSN
Conference
2022
2694-0604
Citations 
PageRank 
References 
0
0.34
0
Authors
9
Name
Order
Citations
PageRank
Liang Qiao101.69
Qiang Liu22419.55
Jun Shi3113569.74
Minfan Zhao401.35
Hongyu Kan501.35
Zhaohui Wang677.58
Hong An75824.15
Chenguang Xiao800.34
Shuo Wang9158.67