129 related articles for article (PubMed ID: 35588664)
1. A deep learning-based precision volume calculation approach for kidney and tumor segmentation on computed tomography images.
Hsiao CH; Sun TL; Lin PC; Peng TY; Chen YH; Cheng CY; Yang FJ; Yang SY; Wu CH; Lin FY; Huang Y
Comput Methods Programs Biomed; 2022 Jun; 221():106861. PubMed ID: 35588664
[TBL] [Abstract][Full Text] [Related]
2. Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches.
Zhou X
Adv Exp Med Biol; 2020; 1213():135-147. PubMed ID: 32030668
[TBL] [Abstract][Full Text] [Related]
3. A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in computed tomography images.
Hsiao CH; Lin PC; Chung LA; Lin FY; Yang FJ; Yang SY; Wu CH; Huang Y; Sun TL
Comput Methods Programs Biomed; 2022 Jun; 221():106854. PubMed ID: 35567864
[TBL] [Abstract][Full Text] [Related]
4. Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm.
Xia KJ; Yin HS; Zhang YD
J Med Syst; 2018 Nov; 43(1):2. PubMed ID: 30456668
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images.
Bevilacqua V; Brunetti A; Cascarano GD; Guerriero A; Pesce F; Moschetta M; Gesualdo L
BMC Med Inform Decis Mak; 2019 Dec; 19(Suppl 9):244. PubMed ID: 31830973
[TBL] [Abstract][Full Text] [Related]
7. [Study on the accuracy of automatic segmentation of knee CT images based on deep learning].
Song P; Fan Z; Zhi X; Cao Z; Min S; Liu X; Zhang Y; Kong X; Chai W
Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi; 2022 May; 36(5):534-539. PubMed ID: 35570625
[TBL] [Abstract][Full Text] [Related]
8. Kidney segmentation from computed tomography images using deep neural network.
da Cruz LB; Araújo JDL; Ferreira JL; Diniz JOB; Silva AC; de Almeida JDS; de Paiva AC; Gattass M
Comput Biol Med; 2020 Aug; 123():103906. PubMed ID: 32768047
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. CT2US: Cross-modal transfer learning for kidney segmentation in ultrasound images with synthesized data.
Song Y; Zheng J; Lei L; Ni Z; Zhao B; Hu Y
Ultrasonics; 2022 May; 122():106706. PubMed ID: 35149255
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography.
Allioui H; Mourdi Y; Sadgal M
Radiography (Lond); 2023 Jan; 29(1):109-118. PubMed ID: 36335787
[TBL] [Abstract][Full Text] [Related]
13. Fast interactive medical image segmentation with weakly supervised deep learning method.
Girum KB; Créhange G; Hussain R; Lalande A
Int J Comput Assist Radiol Surg; 2020 Sep; 15(9):1437-1444. PubMed ID: 32653985
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Intelligent Identification of Coal Crack in CT Images Based on Deep Learning.
Yu J; Wu C; Li Y; Zhang Y
Comput Intell Neurosci; 2022; 2022():7092436. PubMed ID: 36193183
[TBL] [Abstract][Full Text] [Related]
16. U-Net based deep learning bladder segmentation in CT urography.
Ma X; Hadjiiski LM; Wei J; Chan HP; Cha KH; Cohan RH; Caoili EM; Samala R; Zhou C; Lu Y
Med Phys; 2019 Apr; 46(4):1752-1765. PubMed ID: 30734932
[TBL] [Abstract][Full Text] [Related]
17. Multi-Scale deep learning framework for cochlea localization, segmentation and analysis on clinical ultra-high-resolution CT images.
Heutink F; Koch V; Verbist B; van der Woude WJ; Mylanus E; Huinck W; Sechopoulos I; Caballo M
Comput Methods Programs Biomed; 2020 Jul; 191():105387. PubMed ID: 32109685
[TBL] [Abstract][Full Text] [Related]
18. Automated pancreas segmentation and volumetry using deep neural network on computed tomography.
Lim SH; Kim YJ; Park YH; Kim D; Kim KG; Lee DH
Sci Rep; 2022 Mar; 12(1):4075. PubMed ID: 35260710
[TBL] [Abstract][Full Text] [Related]
19. Deep generative models for automated muscle segmentation in computed tomography scanning.
Nishiyama D; Iwasaki H; Taniguchi T; Fukui D; Yamanaka M; Harada T; Yamada H
PLoS One; 2021; 16(9):e0257371. PubMed ID: 34506602
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
