Jiantuan Duan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Jiantuan Duan has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Deguang Wang
School of Basic Medicine, Xuzhou Medical University, No 209. Tongshan Road, Xuzhou City, Jiangsu Province, China; School of Medical Imaging, Xuzhou Medical University, No 209. Tongshan Road, Xuzhou City, Jiangsu Province, China
1 shared publication - Hui Wang
School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
1 shared publication - Jiang Luo
School of Medical Imaging, Xuzhou Medical University, Xuzhou, China
1 shared publication - Jiawei Wu
School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, China
1 shared publication - Shengqiang Zhou
School of Economics and Finance, Xi’an Jiaotong University, Xi’an, China
1 shared publication - Songlin Zuo
School of the First Clinical Medical, Xuzhou Medical University, Xuzhou, China
1 shared publication
Latest Publications
- Open Access
- 15 October 2021
- Published by Springer Nature in BMC Medical Informatics and Decision Making
- Vol. 21 (1) , 1-12
U-Net combined with multi-scale attention mechanism for liver segmentation in CT images
Background: The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).
- 15 October 2021
- Published by Springer Nature in BMC Medical Informatics and Decision Making
- Vol. 21 (1) , 1-12
Open AccessU-Net combined with multi-scale attention mechanism for liver segmentation in CT images
Background: The liver is an important organ that undertakes the metabolic function of the human body. Liver cancer has become one of the cancers with the highest mortality. In clinic, it is an important work to extract the liver region accurately before the diagnosis and treatment of liver lesions. However, manual liver segmentation is a time-consuming and boring process. Not only that, but the segmentation results usually varies from person to person due to different work experience. In order to assist in clinical automatic liver segmentation, this paper proposes a U-shaped network with multi-scale attention mechanism for liver organ segmentation in CT images, which is called MSA-UNet. Our method makes a new design of U-Net encoder, decoder, skip connection, and context transition structure. These structures greatly enhance the feature extraction ability of encoder and the efficiency of decoder to recover spatial location information. We have designed many experiments on publicly available datasets to show the effectiveness of MSA-UNet. Compared with some other advanced segmentation methods, MSA-UNet finally achieved the best segmentation effect, reaching 98.00% dice similarity coefficient (DSC) and 96.08% intersection over union (IOU).