167 related articles for article (PubMed ID: 37274226)
1. Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms.
Zhao T; Sun Z; Guo Y; Sun Y; Zhang Y; Wang X
Front Oncol; 2023; 13():1169922. PubMed ID: 37274226
[TBL] [Abstract][Full Text] [Related]
2. Ensemble U-net-based method for fully automated detection and segmentation of renal masses on computed tomography images.
Fatemeh Z; Nicola S; Satheesh K; Eranga U
Med Phys; 2020 Sep; 47(9):4032-4044. PubMed ID: 32329074
[TBL] [Abstract][Full Text] [Related]
3. Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network.
Lin Z; Cui Y; Liu J; Sun Z; Ma S; Zhang X; Wang X
Eur Radiol; 2021 Jul; 31(7):5021-5031. PubMed ID: 33439313
[TBL] [Abstract][Full Text] [Related]
4. Automated 3D U-net based segmentation of neonatal cerebral ventricles from 3D ultrasound images.
Szentimrey Z; de Ribaupierre S; Fenster A; Ukwatta E
Med Phys; 2022 Feb; 49(2):1034-1046. PubMed ID: 34958147
[TBL] [Abstract][Full Text] [Related]
5. Automated left ventricular myocardium segmentation using 3D deeply supervised attention U-net for coronary computed tomography angiography; CT myocardium segmentation.
Jun Guo B; He X; Lei Y; Harms J; Wang T; Curran WJ; Liu T; Jiang Zhang L; Yang X
Med Phys; 2020 Apr; 47(4):1775-1785. PubMed ID: 32017118
[TBL] [Abstract][Full Text] [Related]
6. Automated deep learning-based segmentation of COVID-19 lesions from chest computed tomography images.
Salehi M; Ardekani MA; Taramsari AB; Ghaffari H; Haghparast M
Pol J Radiol; 2022; 87():e478-e486. PubMed ID: 36091652
[TBL] [Abstract][Full Text] [Related]
7. Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images.
Anush A; Rohini G; Nicola S; WalaaEldin EM; Eranga U
J Med Imaging (Bellingham); 2023 Mar; 10(2):024501. PubMed ID: 36950139
[TBL] [Abstract][Full Text] [Related]
8. AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy.
Zhu W; Huang Y; Zeng L; Chen X; Liu Y; Qian Z; Du N; Fan W; Xie X
Med Phys; 2019 Feb; 46(2):576-589. PubMed ID: 30480818
[TBL] [Abstract][Full Text] [Related]
9. A deep learning-based self-adapting ensemble method for segmentation in gynecological brachytherapy.
Li Z; Zhu Q; Zhang L; Yang X; Li Z; Fu J
Radiat Oncol; 2022 Sep; 17(1):152. PubMed ID: 36064571
[TBL] [Abstract][Full Text] [Related]
10. Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.
Orlando N; Gillies DJ; Gyacskov I; Romagnoli C; D'Souza D; Fenster A
Med Phys; 2020 Jun; 47(6):2413-2426. PubMed ID: 32166768
[TBL] [Abstract][Full Text] [Related]
11. An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images.
Caballo M; Boone JM; Mann R; Sechopoulos I
Med Phys; 2018 Jun; 45(6):2542-2559. PubMed ID: 29676025
[TBL] [Abstract][Full Text] [Related]
12. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.
Panda A; Korfiatis P; Suman G; Garg SK; Polley EC; Singh DP; Chari ST; Goenka AH
Med Phys; 2021 May; 48(5):2468-2481. PubMed ID: 33595105
[TBL] [Abstract][Full Text] [Related]
13. Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion.
Zabihollahy F; Schieda N; Krishna S; Ukwatta E
Eur Radiol; 2020 Sep; 30(9):5183-5190. PubMed ID: 32350661
[TBL] [Abstract][Full Text] [Related]
14. Automatic segmentation of rectal tumor on diffusion-weighted images by deep learning with U-Net.
Zhu HT; Zhang XY; Shi YJ; Li XT; Sun YS
J Appl Clin Med Phys; 2021 Sep; 22(9):324-331. PubMed ID: 34343402
[TBL] [Abstract][Full Text] [Related]
15. Automated segmentation of vertebral cortex with 3D U-Net-based deep convolutional neural network.
Li Y; Yao Q; Yu H; Xie X; Shi Z; Li S; Qiu H; Li C; Qin J
Front Bioeng Biotechnol; 2022; 10():996723. PubMed ID: 36338129
[No Abstract] [Full Text] [Related]
16. Automatic segmentation of kidneys in computed tomography images using U-Net.
Khalal DM; Azizi H; Maalej N
Cancer Radiother; 2023 Apr; 27(2):109-114. PubMed ID: 36739197
[TBL] [Abstract][Full Text] [Related]
17. Fully automated bladder tumor segmentation from T2 MRI images using 3D U-Net algorithm.
Coroamă DM; Dioșan L; Telecan T; Andras I; Crișan N; Medan P; Andreica A; Caraiani C; Lebovici A; Boca B; Bálint Z
Front Oncol; 2023; 13():1096136. PubMed ID: 36969047
[TBL] [Abstract][Full Text] [Related]
18. A Deep Residual U-Net Algorithm for Automatic Detection and Quantification of Ascites on Abdominopelvic Computed Tomography Images Acquired in the Emergency Department: Model Development and Validation.
Ko H; Huh J; Kim KW; Chung H; Ko Y; Kim JK; Lee JH; Lee J
J Med Internet Res; 2022 Jan; 24(1):e34415. PubMed ID: 34982041
[TBL] [Abstract][Full Text] [Related]
19. Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen.
Meddeb A; Kossen T; Bressem KK; Hamm B; Nagel SN
Tomography; 2021 Dec; 7(4):950-960. PubMed ID: 34941650
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
