205 related articles for article (PubMed ID: 29320532)
1. Automatic outer and inner breast tissue segmentation using multi-parametric MRI images of breast tumor patients.
Thakran S; Chatterjee S; Singhal M; Gupta RK; Singh A
PLoS One; 2018; 13(1):e0190348. PubMed ID: 29320532
[TBL] [Abstract][Full Text] [Related]
2. An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U-nets.
Fashandi H; Kuling G; Lu Y; Wu H; Martel AL
Med Phys; 2019 Mar; 46(3):1230-1244. PubMed ID: 30609062
[TBL] [Abstract][Full Text] [Related]
3. Fully Automatic Assessment of Background Parenchymal Enhancement on Breast MRI Using Machine-Learning Models.
Nam Y; Park GE; Kang J; Kim SH
J Magn Reson Imaging; 2021 Mar; 53(3):818-826. PubMed ID: 33219624
[TBL] [Abstract][Full Text] [Related]
4. Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.
Jiao H; Jiang X; Pang Z; Lin X; Huang Y; Li L
Comput Math Methods Med; 2020; 2020():2413706. PubMed ID: 32454879
[TBL] [Abstract][Full Text] [Related]
5. Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?
Doran SJ; Hipwell JH; Denholm R; Eiben B; Busana M; Hawkes DJ; Leach MO; Silva IDS
Med Phys; 2017 Sep; 44(9):4573-4592. PubMed ID: 28477346
[TBL] [Abstract][Full Text] [Related]
6. Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images.
Jiang L; Hu X; Xiao Q; Gu Y; Li Q
Med Phys; 2017 Jun; 44(6):2400-2414. PubMed ID: 28375584
[TBL] [Abstract][Full Text] [Related]
7. Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics.
Saha A; Grimm LJ; Harowicz M; Ghate SV; Kim C; Walsh R; Mazurowski MA
Med Phys; 2016 Aug; 43(8):4558. PubMed ID: 27487872
[TBL] [Abstract][Full Text] [Related]
8. Segmentation of whole breast and fibroglandular tissue using nnU-Net in dynamic contrast enhanced MR images.
Huo L; Hu X; Xiao Q; Gu Y; Chu X; Jiang L
Magn Reson Imaging; 2021 Oct; 82():31-41. PubMed ID: 34147598
[TBL] [Abstract][Full Text] [Related]
9. Automatic segmentation of breast MR images through a Markov random field statistical model.
Ribes S; Didierlaurent D; Decoster N; Gonneau E; Risser L; Feillel V; Caselles O
IEEE Trans Med Imaging; 2014 Oct; 33(10):1986-96. PubMed ID: 24919158
[TBL] [Abstract][Full Text] [Related]
10. Using deep learning to segment breast and fibroglandular tissue in MRI volumes.
Dalmış MU; Litjens G; Holland K; Setio A; Mann R; Karssemeijer N; Gubern-Mérida A
Med Phys; 2017 Feb; 44(2):533-546. PubMed ID: 28035663
[TBL] [Abstract][Full Text] [Related]
11. Automatic human knee cartilage segmentation from 3D magnetic resonance images.
Dodin P; Pelletier JP; Martel-Pelletier J; Abram F
IEEE Trans Biomed Eng; 2010 Nov; 57(11):. PubMed ID: 20639173
[TBL] [Abstract][Full Text] [Related]
12. Automatic segmentation of subcutaneous mouse tumors by multiparametric MR analysis based on endogenous contrast.
Hectors SJ; Jacobs I; Strijkers GJ; Nicolay K
MAGMA; 2015 Aug; 28(4):363-75. PubMed ID: 25427885
[TBL] [Abstract][Full Text] [Related]
13. Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics.
Zhang J; Saha A; Zhu Z; Mazurowski MA
IEEE Trans Med Imaging; 2019 Feb; 38(2):435-447. PubMed ID: 30130181
[TBL] [Abstract][Full Text] [Related]
14. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates.
Pipitone J; Park MT; Winterburn J; Lett TA; Lerch JP; Pruessner JC; Lepage M; Voineskos AN; Chakravarty MM;
Neuroimage; 2014 Nov; 101():494-512. PubMed ID: 24784800
[TBL] [Abstract][Full Text] [Related]
15. Joint Dense Residual and Recurrent Attention Network for DCE-MRI Breast Tumor Segmentation.
Qin C; Lin J; Zeng J; Zhai Y; Tian L; Peng S; Li F
Comput Intell Neurosci; 2022; 2022():3470764. PubMed ID: 35498198
[TBL] [Abstract][Full Text] [Related]
16. Breast segmentation and density estimation in breast MRI: a fully automatic framework.
Gubern-Mérida A; Kallenberg M; Mann RM; Martí R; Karssemeijer N
IEEE J Biomed Health Inform; 2015 Jan; 19(1):349-57. PubMed ID: 25561456
[TBL] [Abstract][Full Text] [Related]
17. Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images.
Liu L; Li K; Qin W; Wen T; Li L; Wu J; Gu J
Med Biol Eng Comput; 2018 Feb; 56(2):183-199. PubMed ID: 29292471
[TBL] [Abstract][Full Text] [Related]
18. Automatic segmentation of MR brain images of preterm infants using supervised classification.
Moeskops P; Benders MJ; Chiţ SM; Kersbergen KJ; Groenendaal F; de Vries LS; Viergever MA; Išgum I
Neuroimage; 2015 Sep; 118():628-41. PubMed ID: 26057591
[TBL] [Abstract][Full Text] [Related]
19. A complete software application for automatic registration of x-ray mammography and magnetic resonance images.
Solves-Llorens JA; Rupérez MJ; Monserrat C; Feliu E; García M; Lloret M
Med Phys; 2014 Aug; 41(8):081903. PubMed ID: 25086534
[TBL] [Abstract][Full Text] [Related]
20. Breast fat volume measurement using wide-bore 3 T MRI: comparison of traditional mammographic density evaluation with MRI density measurements using automatic segmentation.
Petridou E; Kibiro M; Gladwell C; Malcolm P; Toms A; Juette A; Borga M; Dahlqvist Leinhard O; Romu T; Kasmai B; Denton E
Clin Radiol; 2017 Jul; 72(7):565-572. PubMed ID: 28363661
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]