1178 related articles for article (PubMed ID: 33950526)
1. PA-ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images.
Xu Y; Cai M; Lin L; Zhang Y; Hu H; Peng Z; Zhang Q; Chen Q; Mao X; Iwamoto Y; Han XH; Chen YW; Tong R
Med Phys; 2021 Jul; 48(7):3752-3766. PubMed ID: 33950526
[TBL] [Abstract][Full Text] [Related]
2. Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images.
Liu T; Liu J; Ma Y; He J; Han J; Ding X; Chen CT
Med Phys; 2021 Jan; 48(1):264-272. PubMed ID: 33159809
[TBL] [Abstract][Full Text] [Related]
3. PA-Net: A phase attention network fusing venous and arterial phase features of CT images for liver tumor segmentation.
Liu Z; Hou J; Pan X; Zhang R; Shi Z
Comput Methods Programs Biomed; 2024 Feb; 244():107997. PubMed ID: 38176329
[TBL] [Abstract][Full Text] [Related]
4. HFCF-Net: A hybrid-feature cross fusion network for COVID-19 lesion segmentation from CT volumetric images.
Wang Y; Yang Q; Tian L; Zhou X; Rekik I; Huang H
Med Phys; 2022 Jun; 49(6):3797-3815. PubMed ID: 35301729
[TBL] [Abstract][Full Text] [Related]
5. Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.
Ouhmich F; Agnus V; Noblet V; Heitz F; Pessaux P
Int J Comput Assist Radiol Surg; 2019 Aug; 14(8):1275-1284. PubMed ID: 31041697
[TBL] [Abstract][Full Text] [Related]
6. HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images.
Kushnure DT; Talbar SN
Comput Methods Programs Biomed; 2022 Jan; 213():106501. PubMed ID: 34752959
[TBL] [Abstract][Full Text] [Related]
7. An effective deep network for automatic segmentation of complex lung tumors in CT images.
Wang B; Chen K; Tian X; Yang Y; Zhang X
Med Phys; 2021 Sep; 48(9):5004-5016. PubMed ID: 34224147
[TBL] [Abstract][Full Text] [Related]
8. SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography.
Wang J; Lv P; Wang H; Shi C
Comput Methods Programs Biomed; 2021 Sep; 208():106268. PubMed ID: 34274611
[TBL] [Abstract][Full Text] [Related]
9. A Boundary-Enhanced Liver Segmentation Network for Multi-Phase CT Images with Unsupervised Domain Adaptation.
Ananda S; Jain RK; Li Y; Iwamoto Y; Han XH; Kanasaki S; Hu H; Chen YW
Bioengineering (Basel); 2023 Jul; 10(8):. PubMed ID: 37627784
[TBL] [Abstract][Full Text] [Related]
10. Automatic liver segmentation by integrating fully convolutional networks into active contour models.
Guo X; Schwartz LH; Zhao B
Med Phys; 2019 Oct; 46(10):4455-4469. PubMed ID: 31356688
[TBL] [Abstract][Full Text] [Related]
11. 3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor.
Li H; Liu B; Zhang Y; Fu C; Han X; Du L; Gao W; Chen Y; Liu X; Wang Y; Wang T; Ma G; Lei B
Front Oncol; 2021; 11():618496. PubMed ID: 34094903
[TBL] [Abstract][Full Text] [Related]
12. S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images.
Liu H; Yang J; Jiang C; He S; Fu Y; Zhang S; Hu X; Fang J; Ji W
Comput Biol Med; 2024 May; 174():108400. PubMed ID: 38613888
[TBL] [Abstract][Full Text] [Related]
13. Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images.
Seo H; Huang C; Bassenne M; Xiao R; Xing L
IEEE Trans Med Imaging; 2020 May; 39(5):1316-1325. PubMed ID: 31634827
[TBL] [Abstract][Full Text] [Related]
14. Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning.
Zhu Y; Hu P; Li X; Tian Y; Bai X; Liang T; Li J
Med Phys; 2022 Sep; 49(9):5799-5818. PubMed ID: 35833617
[TBL] [Abstract][Full Text] [Related]
15. Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.
Sun C; Guo S; Zhang H; Li J; Chen M; Ma S; Jin L; Liu X; Li X; Qian X
Artif Intell Med; 2017 Nov; 83():58-66. PubMed ID: 28347562
[TBL] [Abstract][Full Text] [Related]
16. RMAU-Net: Residual Multi-Scale Attention U-Net For liver and tumor segmentation in CT images.
Jiang L; Ou J; Liu R; Zou Y; Xie T; Xiao H; Bai T
Comput Biol Med; 2023 May; 158():106838. PubMed ID: 37030263
[TBL] [Abstract][Full Text] [Related]
17. Progressive attention module for segmentation of volumetric medical images.
Zhang M; Pan H; Zhu Y; Gu Y
Med Phys; 2022 Jan; 49(1):295-308. PubMed ID: 34796528
[TBL] [Abstract][Full Text] [Related]
18. Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging.
Qayyum A; Lalande A; Meriaudeau F
Comput Biol Med; 2020 Dec; 127():104097. PubMed ID: 33142142
[TBL] [Abstract][Full Text] [Related]
19. Domain-Adversarial Transformer Network for Multiphase Liver Tumor Segmentation.
Ni Y; Chen G; Feng Z; Cui H; Metaxas D; Zhang S; Zhu W
Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38083011
[TBL] [Abstract][Full Text] [Related]
20. Liver tumor segmentation in CT volumes using an adversarial densely connected network.
Chen L; Song H; Wang C; Cui Y; Yang J; Hu X; Zhang L
BMC Bioinformatics; 2019 Dec; 20(Suppl 16):587. PubMed ID: 31787071
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]