187 related articles for article (PubMed ID: 34873831)
1. A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images.
Gong Z; Guo C; Guo W; Zhao D; Tan W; Zhou W; Zhang G
J Appl Clin Med Phys; 2022 Jan; 23(1):e13482. PubMed ID: 34873831
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Fully automatic multi-organ segmentation for head and neck cancer radiotherapy using shape representation model constrained fully convolutional neural networks.
Tong N; Gou S; Yang S; Ruan D; Sheng K
Med Phys; 2018 Oct; 45(10):4558-4567. PubMed ID: 30136285
[TBL] [Abstract][Full Text] [Related]
5. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.
Liang S; Tang F; Huang X; Yang K; Zhong T; Hu R; Liu S; Yuan X; Zhang Y
Eur Radiol; 2019 Apr; 29(4):1961-1967. PubMed ID: 30302589
[TBL] [Abstract][Full Text] [Related]
6. Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.
Tang X; Jafargholi Rangraz E; Coudyzer W; Bertels J; Robben D; Schramm G; Deckers W; Maleux G; Baete K; Verslype C; Gooding MJ; Deroose CM; Nuyts J
Eur J Nucl Med Mol Imaging; 2020 Nov; 47(12):2742-2752. PubMed ID: 32314026
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation.
Qin W; Wu J; Han F; Yuan Y; Zhao W; Ibragimov B; Gu J; Xing L
Phys Med Biol; 2018 May; 63(9):095017. PubMed ID: 29633960
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Three-stage segmentation of lung region from CT images using deep neural networks.
Osadebey M; Andersen HK; Waaler D; Fossaa K; Martinsen ACT; Pedersen M
BMC Med Imaging; 2021 Jul; 21(1):112. PubMed ID: 34266391
[TBL] [Abstract][Full Text] [Related]
11. Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.
Ahn SH; Yeo AU; Kim KH; Kim C; Goh Y; Cho S; Lee SB; Lim YK; Kim H; Shin D; Kim T; Kim TH; Youn SH; Oh ES; Jeong JH
Radiat Oncol; 2019 Nov; 14(1):213. PubMed ID: 31775825
[TBL] [Abstract][Full Text] [Related]
12. Polycystic liver: automatic segmentation using deep learning on CT is faster and as accurate compared to manual segmentation.
Cayot B; Milot L; Nempont O; Vlachomitrou AS; Langlois-Jacques C; Dumortier J; Boillot O; Arnaud K; Barten TRM; Drenth JPH; Valette PJ
Eur Radiol; 2022 Jul; 32(7):4780-4790. PubMed ID: 35142898
[TBL] [Abstract][Full Text] [Related]
13. A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.
Gong H; Yu L; Leng S; Dilger SK; Ren L; Zhou W; Fletcher JG; McCollough CH
Med Phys; 2019 May; 46(5):2052-2063. PubMed ID: 30889282
[TBL] [Abstract][Full Text] [Related]
14. Automatic 3D CT liver segmentation based on fast global minimization of probabilistic active contour.
Jin R; Wang M; Xu L; Lu J; Song E; Ma G
Med Phys; 2023 Apr; 50(4):2100-2120. PubMed ID: 36413182
[TBL] [Abstract][Full Text] [Related]
15. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.
Ibragimov B; Xing L
Med Phys; 2017 Feb; 44(2):547-557. PubMed ID: 28205307
[TBL] [Abstract][Full Text] [Related]
16. Deep learning and level set approach for liver and tumor segmentation from CT scans.
Alirr OI
J Appl Clin Med Phys; 2020 Oct; 21(10):200-209. PubMed ID: 33113290
[TBL] [Abstract][Full Text] [Related]
17. Abdomen tissues segmentation from computed tomography images using deep learning and level set methods.
Gong Z; Song J; Guo W; Ju R; Zhao D; Tan W; Zhou W; Zhang G
Math Biosci Eng; 2022 Sep; 19(12):14074-14085. PubMed ID: 36654080
[TBL] [Abstract][Full Text] [Related]
18. Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN.
Xu X; Zhou F; Liu B
Int J Comput Assist Radiol Surg; 2018 Jul; 13(7):967-975. PubMed ID: 29556905
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. 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]
[Next] [New Search]
