@Article{iasc.2023.041208,
AUTHOR = {Xinyi Xiao, Dongbo Pan, Jianjun Yuan},
TITLE = {SC-Net: A New U-Net Network for Hippocampus Segmentation},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {37},
YEAR = {2023},
NUMBER = {3},
PAGES = {3179--3191},
URL = {http://www.techscience.com/iasc/v37n3/54149},
ISSN = {2326-005X},
ABSTRACT = {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.},
DOI = {10.32604/iasc.2023.041208}
}