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SC-Net: A New U-Net Network for Hippocampus Segmentation

Xinyi Xiao, Dongbo Pan*, Jianjun Yuan

College of Artificial Intelligence, Southwest University, Chongqing, 400715, China

* Corresponding Author: Dongbo Pan. Email: email

(This article belongs to the Special Issue: Cognitive Granular Computing Methods for Big Data Analysis)

Intelligent Automation & Soft Computing 2023, 37(3), 3179-3191.


Neurological disorders like Alzheimer’s disease have a significant impact on the lives and health of the elderly as the aging population continues to grow. Doctors can achieve effective prevention and treatment of Alzheimer’s disease according to the morphological volume of hippocampus. General segmentation techniques frequently fail to produce satisfactory results due to hippocampus’s small size, complex structure, and fuzzy edges. We develop a new SC-Net model using complete brain MRI images to achieve high-precision segmentation of hippocampal structures. The proposed network improves the accuracy of hippocampal structural segmentation by retaining the original location information of the hippocampus. Extensive experimental results demonstrate that the proposed SC-Net model is significantly better than other models, and reaches a Dice similarity coefficient of 0.885 on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset.


Cite This Article

APA Style
Xiao, X., Pan, D., Yuan, J. (2023). Sc-net: A new u-net network for hippocampus segmentation. Intelligent Automation & Soft Computing, 37(3), 3179-3191.
Vancouver Style
Xiao X, Pan D, Yuan J. Sc-net: A new u-net network for hippocampus segmentation. Intell Automat Soft Comput . 2023;37(3):3179-3191
IEEE Style
X. Xiao, D. Pan, and J. Yuan "SC-Net: A New U-Net Network for Hippocampus Segmentation," Intell. Automat. Soft Comput. , vol. 37, no. 3, pp. 3179-3191. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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