1 |
RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2015: 234-241.
|
2 |
ZHOU Z W, SIDDIQUEE M R, TAJBAKHSH N, et al. UNet++: a nested U-Net architecture for medical image segmentation[M]. Berlin, Germany: Springer, 2018.
|
3 |
|
4 |
HUANG H M, LIN L F, TONG R F, et al. UNet 3+: a full-scale connected UNet for medical image segmentation[C]//Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. Washington D. C., USA: IEEE Press, 2020: 1055-1059.
|
5 |
VALANARASU J M J, PATEL V M. UNeXt: MLP-based rapid medical image segmentation network[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2022: 23-33.
|
6 |
|
7 |
JAEGER S, KARARGYRIS A, CANDEMIR S, et al. Automatic tuberculosis screening using chest radiographs. IEEE Transactions on Medical Imaging, 2014, 33(2): 233- 245.
doi: 10.1109/TMI.2013.2284099
|
8 |
CANDEMIR S, JAEGER S, PALANIAPPAN K, et al. Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Transactions on Medical Imaging, 2014, 33(2): 577- 590.
doi: 10.1109/TMI.2013.2290491
|
9 |
SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640- 651.
doi: 10.1109/TPAMI.2016.2572683
|
10 |
ALOM M Z, YAKOPCIC C, HASAN M, et al. Recurrent residual U-Net for medical image segmentation. Journal of Medical Imaging, 2019, 6(1): 014006.
|
11 |
林志洁, 郑秋岚, 梁涌, 等. 基于内卷U-Net的医学图像分割模型. 计算机工程, 2022, 48(8): 180- 186.
URL
|
|
LIN Z J, ZHENG Q L, LIANG Y, et al. Medical image segmentation model based on involution U-Net. Computer Engineering, 2022, 48(8): 180- 186.
URL
|
12 |
刘文, 亓文霞, 仲国强, 等. 基于Concat-UNet的食管癌肿瘤医学影像分割研究. 计算机工程, 2022, 48(12): 312- 320.
URL
|
|
LIU W, QI W X, ZHONG G Q, et al. Research on medical image segmentation for esophageal cancer tumors based on Concat-UNet. Computer Engineering, 2022, 48(12): 312- 320.
URL
|
13 |
LIN D Y, LI Y Q, NWE T L, et al. RefineU-Net: improved U-Net with progressive global feedbacks and residual attention guided local refinement for medical image segmentation. Pattern Recognition Letters, 2020, 138, 267- 275.
doi: 10.1016/j.patrec.2020.07.013
|
14 |
HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2017: 4700-4708.
|
15 |
AZAD R, ASADI-AGHBOLAGHI M, FATHY M, et al. Bi-directional ConvLSTM U-Net with densley connected convolutions[EB/OL]. [2023-05-11]. https://arxiv.org/abs/1909.00166.
|
16 |
HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2016: 770-778.
|
17 |
XU Q, MA Z C, HE N, et al. DCSAU-Net: a deeper and more compact split-attention U-Net for medical image segmentation. Computers in Biology and Medicine, 2023, 154, 106626.
doi: 10.1016/j.compbiomed.2023.106626
|
18 |
LIU H L, FENG Y, XU H, et al. MEA-Net: multilayer edge attention network for medical image segmentation. Scientific Reports, 2022, 12, 7868.
doi: 10.1038/s41598-022-11852-y
|
19 |
ZENG Z H, FAN C D, XIAO L Y, et al. DEA-UNet: a dense-edge-attention UNet architecture for medical image segmentation. Journal of Electronic Imaging, 2022, 31, 043032.
|
20 |
ZHANG Z, FU H, DAI H, et al. ET-Net: a generic edge-attention guidance network for medical image segmentation[C]//Proceedings of the 22nd International Conference Medical Image Computing and Computer Assisted Intervention. Berlin, Germany: Springer, 2019: 442-450.
|
21 |
HAO D C, LI H L. A graph-based edge attention gate medical image segmentation method. IET Image Processing, 2023, 17(7): 2142- 2157.
doi: 10.1049/ipr2.12780
|
22 |
孙军梅, 葛青青, 李秀梅, 等. 一种具有边缘增强特点的医学图像分割网络. 电子与信息学报, 2022, 44(5): 1643- 1652.
URL
|
|
SUN J M, GE Q Q, LI X M, et al. A medical image segmentation network with boundary enhancement. Journal of Electronics & Information Technology, 2022, 44(5): 1643- 1652.
URL
|
23 |
李翠云, 白静, 郑凉. 融合边缘增强注意力机制和U-Net网络的医学图像分割. 图学学报, 2022, 43(2): 273- 278.
URL
|
|
LI C Y, BAI J, ZHENG L. A U-Net based contour enhanced attention for medical image segmentation. Journal of Graphics, 2022, 43(2): 273- 278.
URL
|
24 |
WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 3-19.
|
25 |
王晓援, 王雪. 基于多尺度语义表征的医学图像分割网络. 吉林大学学报(理学版), 2022, 60(6): 1370- 1376.
URL
|
|
WANG X Y, WANG X. Medical image segmentation network based on multi-scale semantic representation. Journal of Jilin University(Science Edition), 2022, 60(6): 1370- 1376.
URL
|
26 |
ZHANG Z X, LIU Q J, WANG Y H. Road extraction by deep residual U-Net. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 749- 753.
|
27 |
GU Z W, CHENG J, FU H Z, et al. CE-Net: context encoder network for 2D medical image segmentation. IEEE Transactions on Medical Imaging, 2019, 38(10): 2281- 2292.
|
28 |
BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481- 2495.
|
29 |
CHEN J N, LU Y Y, YU Q H, et al. TransUNet: Transformers make strong encoders for medical image segmentation[EB/OL]. [2023-05-11]. https://arxiv.org/abs/2102.04306.
|
30 |
XU G P, WU X R, ZHANG X, et al. LeViT-UNet: make faster encoders with Transformer for medical image segmentation[EB/OL]. [2023-05-11]. https://arxiv.org/abs/2107.08623.
|