@inproceedings{970189f419054e6a9f4f5d71b4f7dcd9,
title = "Comparative Analysis of 3D-Extended Deep Learning Approaches for Precise Brain MRI Segmentation",
abstract = "Medical image segmentation is the key to disease diagnosis and treatment planning, and deep learning is widely used. For segmentation of brainstem and amygdala in human brain, five representative 2 D models (Fully Convolutional Networks (FCN), U-Net, DeepLab, UNet++, TransUNet) were extended to 3D and their performance was evaluated on OASIS dataset. The results showed that 3D U-net had the best performance, while the 3D variants of FCN, DeepLab and TransUnet were inferior to the Unet series. All models performed much better than the amygdala in the segmentation of brainstem. This study can provide a reference for constructing segmentation models of specific brain regions.",
keywords = "CNN, Deep learning, Medical image segmentation, Transformer, U-Net",
author = "Linghao Sun and Zhilin Zhang and Jinglong Wu and Lichang Yao and Youshan Ma and Ting Jiang and Ziqi Liu and Qi Dai and Xiujun Li",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 19th International Conference on Complex Medical Engineering, CME 2025 ; Conference date: 01-08-2025 Through 03-08-2025",
year = "2025",
doi = "10.1109/CME67420.2025.11239402",
language = "English",
series = "2025 19th International Conference on Complex Medical Engineering, CME 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "81--84",
booktitle = "2025 19th International Conference on Complex Medical Engineering, CME 2025",
address = "United States",
}