Journal
COMPUTERS & ELECTRICAL ENGINEERING
Volume 101, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2022.108085
Keywords
Medical image; Deep learning; Multi-scale; Liver segmentation; Context extraction
Categories
Funding
- National Natural Science Foundation of China [61471079, 62002082]
- Guangxi Natural Science Foundation, China [2020GXNSFBA238014]
- Guangxi University Young and Middle-aged Teachers' Research Ability Improvement Project [2020KY05034]
- China Postdoctoral Science Foundation [2021M690753]
Ask authors/readers for more resources
In this study, a multi-scale context integration network called MCI-Net is proposed for liver image segmentation. Various techniques, such as a simplified residual module, a multi-scale context extraction module, an external attention mechanism, and a boundary correction block, are utilized to accurately segment the liver region from abdominal CT images.
Owing to the various object scales and high similarity with the surrounding organs (e.g., kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the abdominal computed tomography images. In this study, we propose a multi-scale context integration network called MCI-Net for liver image segmentation. Specifically, we first design a simplified residual module to prevent network degradation. Given the scale variability of objects, we propose a multi-scale context extraction module by combining four cascaded branches of hybrid dilated convolutions to capture broader and deeper features. In addition, we introduce an external attention mechanism based on two external, learnable and shared memory units, which helps to perceive the most discriminative information and suppress redundant features. Finally, we provide a boundary correction block to further improve the localization ability of boundary information. Extensive experiments on two liver CT image benchmark datasets qualitatively and quantitatively illustrate that our method is effective in improving liver segmentation accuracy and outperforms several state-of-the-art methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available