79 related articles for article (PubMed ID: 34891784)
1. C3D-UNET: A Comprehensive 3D Unet for Covid-19 Segmentation with Intact Encoding and Local Attention.
Bao Y; Zeng H; Zhou C; Liu C; Zhang L; Qian D; Wang J; Lu H
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():2592-2596. PubMed ID: 34891784
[TBL] [Abstract][Full Text] [Related]
2. MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT.
Yang J; Wu B; Li L; Cao P; Zaiane O
Comput Med Imaging Graph; 2021 Sep; 92():101957. PubMed ID: 34325225
[TBL] [Abstract][Full Text] [Related]
3. Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet.
Ma Y; Feng P; He P; Ren Y; Guo X; Yu X; Wei B
Biomed Phys Eng Express; 2021 May; 7(4):. PubMed ID: 33979791
[TBL] [Abstract][Full Text] [Related]
4. CARes-UNet: Content-aware residual UNet for lesion segmentation of COVID-19 from chest CT images.
Xu X; Wen Y; Zhao L; Zhang Y; Zhao Y; Tang Z; Yang Z; Chen CY
Med Phys; 2021 Nov; 48(11):7127-7140. PubMed ID: 34528263
[TBL] [Abstract][Full Text] [Related]
5. Dual attention fusion UNet for COVID-19 lesion segmentation from CT images.
Ma Y; Zhang Y; Chen L; Jiang Q; Wei B
J Xray Sci Technol; 2023; 31(4):713-729. PubMed ID: 37092210
[TBL] [Abstract][Full Text] [Related]
6. TDD-UNet:Transformer with double decoder UNet for COVID-19 lesions segmentation.
Huang X; Chen J; Chen M; Chen L; Wan Y
Comput Biol Med; 2022 Dec; 151(Pt A):106306. PubMed ID: 36403357
[TBL] [Abstract][Full Text] [Related]
7. Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT.
Wang J; Zhang X; Guo L; Shi C; Tamura S
Math Biosci Eng; 2023 Jan; 20(1):1297-1316. PubMed ID: 36650812
[TBL] [Abstract][Full Text] [Related]
8. PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans.
Bougourzi F; Distante C; Dornaika F; Taleb-Ahmed A
Med Image Anal; 2023 May; 86():102797. PubMed ID: 36966605
[TBL] [Abstract][Full Text] [Related]
9. Automatic Coronary Artery Segmentation of CCTA Images With an Efficient Feature-Fusion-and-Rectification 3D-UNet.
Song A; Xu L; Wang L; Wang B; Yang X; Xu B; Yang B; Greenwald SE
IEEE J Biomed Health Inform; 2022 Aug; 26(8):4044-4055. PubMed ID: 35446776
[TBL] [Abstract][Full Text] [Related]
10. Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks.
Zhang D; Yang Z; Jiang S; Zhou Z; Meng M; Wang W
J Appl Clin Med Phys; 2020 Oct; 21(10):158-169. PubMed ID: 32991783
[TBL] [Abstract][Full Text] [Related]
11. COVID-DAI: A novel framework for COVID-19 detection and infection growth estimation using computed tomography images.
Nazir T; Nawaz M; Javed A; Malik KM; Saudagar AKJ; Khan MB; Abul Hasanat MH; AlTameem A; AlKathami M
Microsc Res Tech; 2022 Jun; 85(6):2313-2330. PubMed ID: 35194866
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.
Buongiorno R; Del Corso G; Germanese D; Colligiani L; Python L; Romei C; Colantonio S
J Imaging; 2023 Dec; 9(12):. PubMed ID: 38132701
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. SAA-UNet: Spatial Attention and Attention Gate UNet for COVID-19 Pneumonia Segmentation from Computed Tomography.
Alshomrani S; Arif M; Al Ghamdi MA
Diagnostics (Basel); 2023 May; 13(9):. PubMed ID: 37175049
[TBL] [Abstract][Full Text] [Related]
16. Two-stage hybrid network for segmentation of COVID-19 pneumonia lesions in CT images: a multicenter study.
Shang Y; Wei Z; Hui H; Li X; Li L; Yu Y; Lu L; Li L; Li H; Yang Q; Wang M; Zhan M; Wang W; Zhang G; Wu X; Wang L; Liu J; Tian J; Zha Y
Med Biol Eng Comput; 2022 Sep; 60(9):2721-2736. PubMed ID: 35856130
[TBL] [Abstract][Full Text] [Related]
17. Pancreas segmentation with probabilistic map guided bi-directional recurrent UNet.
Li J; Lin X; Che H; Li H; Qian X
Phys Med Biol; 2021 May; 66(11):. PubMed ID: 33915526
[TBL] [Abstract][Full Text] [Related]
18. A Medical Image Segmentation Method Based on Improved UNet 3+ Network.
Xu Y; Hou S; Wang X; Li D; Lu L
Diagnostics (Basel); 2023 Feb; 13(3):. PubMed ID: 36766681
[TBL] [Abstract][Full Text] [Related]
19. Coarse-to-fine multiplanar D-SEA UNet for automatic 3D carotid segmentation in CTA images.
Wang J; Yu Y; Yan R; Liu J; Wu H; Geng D; Yu Z
Int J Comput Assist Radiol Surg; 2021 Oct; 16(10):1727-1736. PubMed ID: 34386900
[TBL] [Abstract][Full Text] [Related]
20. DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.
Chen C; Zhou J; Zhou K; Wang Z; Xiao R
Diagnostics (Basel); 2021 Oct; 11(11):. PubMed ID: 34829289
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]