175 related articles for article (PubMed ID: 35404812)
1. Learning From Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation.
Lyu F; Ye M; Ma AJ; Yip TC; Wong GL; Yuen PC
IEEE Trans Med Imaging; 2022 Sep; 41(9):2510-2520. PubMed ID: 35404812
[TBL] [Abstract][Full Text] [Related]
2. Weakly Supervised Liver Tumor Segmentation Using Couinaud Segment Annotation.
Lyu F; Ma AJ; Yip TC; Wong GL; Yuen PC
IEEE Trans Med Imaging; 2022 May; 41(5):1138-1149. PubMed ID: 34871168
[TBL] [Abstract][Full Text] [Related]
3. Towards annotation-efficient segmentation via image-to-image translation.
Vorontsov E; Molchanov P; Gazda M; Beckham C; Kautz J; Kadoury S
Med Image Anal; 2022 Nov; 82():102624. PubMed ID: 36208571
[TBL] [Abstract][Full Text] [Related]
4. CTumorGAN: a unified framework for automatic computed tomography tumor segmentation.
Pang S; Du A; Orgun MA; Yu Z; Wang Y; Wang Y; Liu G
Eur J Nucl Med Mol Imaging; 2020 Sep; 47(10):2248-2268. PubMed ID: 32222809
[TBL] [Abstract][Full Text] [Related]
5. Cascaded deep convolutional encoder-decoder neural networks for efficient liver tumor segmentation.
Budak Ü; Guo Y; Tanyildizi E; Şengür A
Med Hypotheses; 2020 Jan; 134():109431. PubMed ID: 31669758
[TBL] [Abstract][Full Text] [Related]
6. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images.
Wang J; Cheng Y; Guo C; Wang Y; Tamura S
Int J Comput Assist Radiol Surg; 2016 May; 11(5):817-26. PubMed ID: 26646416
[TBL] [Abstract][Full Text] [Related]
7. Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net.
Zhang G; Yang Z; Huo B; Chai S; Jiang S
Comput Methods Programs Biomed; 2021 Nov; 211():106419. PubMed ID: 34563895
[TBL] [Abstract][Full Text] [Related]
8. The effects of different levels of realism on the training of CNNs with only synthetic images for the semantic segmentation of robotic instruments in a head phantom.
Heredia Perez SA; Marques Marinho M; Harada K; Mitsuishi M
Int J Comput Assist Radiol Surg; 2020 Aug; 15(8):1257-1265. PubMed ID: 32445129
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. A medical unsupervised domain adaptation framework based on Fourier transform image translation and multi-model ensemble self-training strategy.
Jiang K; Gong T; Quan L
Int J Comput Assist Radiol Surg; 2023 Oct; 18(10):1885-1894. PubMed ID: 37010674
[TBL] [Abstract][Full Text] [Related]
11. Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks.
Sandfort V; Yan K; Pickhardt PJ; Summers RM
Sci Rep; 2019 Nov; 9(1):16884. PubMed ID: 31729403
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. Catheter segmentation in X-ray fluoroscopy using synthetic data and transfer learning with light U-nets.
Gherardini M; Mazomenos E; Menciassi A; Stoyanov D
Comput Methods Programs Biomed; 2020 Aug; 192():105420. PubMed ID: 32171151
[TBL] [Abstract][Full Text] [Related]
15. Image generation by GAN and style transfer for agar plate image segmentation.
Andreini P; Bonechi S; Bianchini M; Mecocci A; Scarselli F
Comput Methods Programs Biomed; 2020 Feb; 184():105268. PubMed ID: 31891902
[TBL] [Abstract][Full Text] [Related]
16. S-CUDA: Self-cleansing unsupervised domain adaptation for medical image segmentation.
Liu L; Zhang Z; Li S; Ma K; Zheng Y
Med Image Anal; 2021 Dec; 74():102214. PubMed ID: 34464837
[TBL] [Abstract][Full Text] [Related]
17. Unsupervised Domain Adaptation Using Fourier Phase Enhanced Training Images for Liver Tumors Detection in Multi-phase CT Images.
Jain RK; Sato T; El-Sayed AM; Watasue T; Nakagawa T; Iwamoto Y; Li Y; Han X; Lin L; Hu H; Ruan X; Chen YW
Annu Int Conf IEEE Eng Med Biol Soc; 2023 Jul; 2023():1-4. PubMed ID: 38082913
[TBL] [Abstract][Full Text] [Related]
18. Deep learning techniques for liver and liver tumor segmentation: A review.
Gul S; Khan MS; Bibi A; Khandakar A; Ayari MA; Chowdhury MEH
Comput Biol Med; 2022 Aug; 147():105620. PubMed ID: 35667155
[TBL] [Abstract][Full Text] [Related]
19. Simultaneous vessel segmentation and unenhanced prediction using self-supervised dual-task learning in 3D CTA (SVSUP).
Huang W; Gao W; Hou C; Zhang X; Wang X; Zhang J
Comput Methods Programs Biomed; 2022 Sep; 224():107001. PubMed ID: 35810508
[TBL] [Abstract][Full Text] [Related]
20. Tumor attention networks: Better feature selection, better tumor segmentation.
Pang S; Du A; Orgun MA; Wang Y; Yu Z
Neural Netw; 2021 Aug; 140():203-222. PubMed ID: 33780873
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]