SepU-Net MRI Segmentation Algorithm Using Depthwise Separable Convolution and Pointwise Convolution Integrated U-Net
Tóm tắt
Từ khóa
#Depthwise separable convolution #Medical image segmentation #Light weight neural network #Computational efficiency #Brain tumor segmentation #Lightweight architectureTài liệu tham khảo
M. Havaei et al., “Brain tumor segmentation with deep neural networks,” Med. Image Anal., vol. 35, pp. 18–31, 2017,
doi: 10.1016/j.media.2016.05.004.
L. Zhao et al., “MM-UNet: A multimodality brain tumor segmentation network in MRI images,” Front. Oncol., vol. 12, Art. no. 950706, 2022, doi: 10.3389/fonc.2022.950706.
J. K. Ruffle et al., “Brain tumour segmentation with incomplete imaging data,” Brain Commun., vol. 5, no. 2, 2023,
doi: 10.1093/braincomms/fcad118.
P. Li et al., “mResU-Net: Multi-scale residual U-Net-based brain tumor segmentation from multimodal MRI,” Med. Biol. Eng. Comput., 2024, doi: 10.1007/s11517-023-02965-1.
L. Alzubaidi, J. Zhang, and A. J. Humaidi, “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,” J. Big Data, vol. 8, Art. no. 53, 2021, doi: 10.1186/s40537-021-00444-8.
B. Hou and S. Guan, “Brain tumor segmentation using deep learning: A review,” J. Comput. Electron. Inf. Manag., vol. 16, 2025,
doi: 10.54097/31ag9n29.
Y. Zhao and L. Lin, “A lightweight U-Net for medical image segmentation,” in Proc. PIERS, 2024, pp. 1–5,
doi: 10.1109/PIERS62282.2024.10618503.
L. Shen et al., “MBDRes-U-Net: Multi-scale lightweight brain tumor segmentation network,” arXiv preprint, arXiv:2411.01896, 2024, doi: 10.48550/arXiv.2411.01896.
G. E. S. Shahid et al., “LIU-NET: Lightweight inception U-Net for efficient brain tumor segmentation,” PeerJ Comput. Sci., 2025,
doi: 10.1109/CW58918.2023.00012.
A. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint, arXiv:1704.04861, 2017.
M. Sandler et al., “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. IEEE CVPR, 2018, pp. 4510–4520,
doi: 10.1109/CVPR.2018.00474.
K. Avazov et al., “Dynamic focus on tumor boundaries: A lightweight U-Net for MRI brain tumor segmentation,” Bioengineering, vol. 11, Art. no. 1302, 2024, doi: 10.3390/bioengineering11121302.
D. Liu et al., “SGEResU-Net for brain tumor segmentation,” Math. Biosci. Eng., 2022, doi: 10.3934/mbe.2022073.
Awsaf, “Brain tumor segmentation 2020 dataset,” Kaggle, 2020. [Online]. Available: https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation
S. Zabihi et al., “SepUNet: Depthwise separable convolution integrated U-Net,” in Proc. IEEE ICIP, 2021, pp. 2503–2507,
doi: 10.1109/ICIP42928.2021.9506283.
S. Gore, “Brain tumour segmentation and analysis using BraTS dataset with improvised 2D and 3D U-Net models,” Research Square, 2023, doi: 10.21203/rs.3.rs-2791706/v1.
S. Bakas et al., “Advancing TCGA glioma MRI collections with expert segmentation labels and radiomic features,” Sci. Data, vol. 4, Art. no. 170117, 2017, doi: 10.1038/sdata.2017.117.
S. Bakas et al., “Identifying the best machine learning algorithms for brain tumor segmentation,” arXiv preprint, arXiv:1811.02629, 2018.
A. M. Winkler, “The NIfTI file format,” 2012. [Online]. Available: https://brainder.org/2012/09/23/the-nifti-file-format/
M. Brett et al., “NiBabel: Access a cacophony of neuroimaging file formats,” 2012. [Online]. Available: https://nipy.org/nibabel/nifti_images.html
A. M. Winkler, “Brainder,” 2012. [Online]. Available: https://brainder.org/2012/09/23/
H. Dong et al., “Automatic brain tumor detection using U-Net,” in Med. Image Underst. Anal., Springer, 2017, pp. 506–517,
doi: 10.1007/978-3-319-60964-5_44.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proc. IEEE CVPR, 2017, pp. 1251–1258,
doi: 10.1109/CVPR.2017.195.
B. S. Hua, M. K. Tran, and S. K. Yeung, “Pointwise convolutional neural networks,” in Proc. IEEE/GVF, 2017.
D. Haase and M. A. Daniel, “Rethinking depthwise separable convolutions,” arXiv preprint, arXiv:2003.13549, 2020,
doi: 10.48550/arXiv.2003.13549.
J. Hu et al., “Squeeze-and-excitation networks,” in Proc. IEEE CVPR, 2018, pp. 7132–7141, doi: 10.1109/CVPR.2018.00745.
N. Klingler, “Squeeze-and-excitation networks: A performance upgrade,” 2024. [Online]. Available: https://viso.ai/deep-learning/squeeze-and-excite-networks/
S. Woo, J. Park, J. Y. Lee, and I. S. Kweon, “CBAM: Convolutional block attention module,” arXiv preprint, arXiv:1807.06521, 2018.
Q. Wang et al., “ECA-Net: Efficient channel attention for deep convolutional neural networks,” in Proc. IEEE CVPR, 2020, pp. 11534–11542, doi: 10.1109/CVPR42600.2020.01155.
H. Yang et al., “RS-YOLOX: A high-precision detector for object detection in satellite remote sensing images,” Appl. Sci., 2025,
doi: 10.3390/app12178707.
S. R. Dubey, S. K. Singh, and B. B. Chaudhuri, “Activation functions in deep learning: A comprehensive survey and benchmark,” arXiv preprint, arXiv:2109.14545, 2022, doi: 10.48550/arXiv.2109.14545.
N. Srivastava et al., “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res., vol. 15, pp. 1929–1958, 2014.
K. Matoba, N. Dimitriadis, and F. Fleuret, “The theoretical expressiveness of max pooling,” arXiv preprint, arXiv:2203.01016, 2022, doi: 10.48550/arXiv.2203.01016.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. MICCAI, 2015, pp. 234–241, doi: 10.1007/978-3-319-24574-4_28.
K. Machida, I. Nambu, and Y. Wada, “Transposed convolution as alternative preprocessor for brain–computer interface using EEG,” Appl. Sci., 2023, doi: 10.3390/app13063578.
R. Rastislav and K. Sayed, “3D MRI brain tumor segmentation using U-Net,” Kaggle, 2024. [Online]. Available: https://www.kaggle.com/code/khaledsayedaaaaa/3d-mri-brain-tumor-segmentation-u-net-acc-99
R. Preetha et al., “Brain tumor segmentation using multi-scale attention U-Net with EfficientNetB4 encoder for enhanced MRI analysis,” Sci. Rep., 2025, doi: 10.1038/s41598-025-94267-9.