496 related articles for article (PubMed ID: 32269918)
1. Fully automated lesion segmentation and visualization in automated whole breast ultrasound (ABUS) images.
Lee CY; Chang TF; Chou YH; Yang KC
Quant Imaging Med Surg; 2020 Mar; 10(3):568-584. PubMed ID: 32269918
[TBL] [Abstract][Full Text] [Related]
2. Deformable mapping technique to correlate lesions in digital breast tomosynthesis and automated breast ultrasound images.
Green CA; Goodsitt MM; Brock KK; Davis CL; Larson ED; Lau JH; Carson PL
Med Phys; 2018 Oct; 45(10):4402-4417. PubMed ID: 30066340
[TBL] [Abstract][Full Text] [Related]
3. Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis.
Agarwal R; Diaz O; Lladó X; Gubern-Mérida A; Vilanova JC; Martí R
Ultrason Imaging; 2018 Mar; 40(2):97-112. PubMed ID: 29182056
[TBL] [Abstract][Full Text] [Related]
4. Segmentation of malignant lesions in 3D breast ultrasound using a depth-dependent model.
Tan T; Gubern-Mérida A; Borelli C; Manniesing R; van Zelst J; Wang L; Zhang W; Platel B; Mann RM; Karssemeijer N
Med Phys; 2016 Jul; 43(7):4074. PubMed ID: 27370126
[TBL] [Abstract][Full Text] [Related]
5. Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN.
Lei Y; He X; Yao J; Wang T; Wang L; Li W; Curran WJ; Liu T; Xu D; Yang X
Med Phys; 2021 Jan; 48(1):204-214. PubMed ID: 33128230
[TBL] [Abstract][Full Text] [Related]
6. Dilated densely connected U-Net with uncertainty focus loss for 3D ABUS mass segmentation.
Cao X; Chen H; Li Y; Peng Y; Wang S; Cheng L
Comput Methods Programs Biomed; 2021 Sep; 209():106313. PubMed ID: 34364182
[TBL] [Abstract][Full Text] [Related]
7. A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging.
Bagci U; Foster B; Miller-Jaster K; Luna B; Dey B; Bishai WR; Jonsson CB; Jain S; Mollura DJ
EJNMMI Res; 2013 Jul; 3(1):55. PubMed ID: 23879987
[TBL] [Abstract][Full Text] [Related]
8. DSGMFFN: Deepest semantically guided multi-scale feature fusion network for automated lesion segmentation in ABUS images.
Cheng Z; Li Y; Chen H; Zhang Z; Pan P; Cheng L
Comput Methods Programs Biomed; 2022 Jun; 221():106891. PubMed ID: 35623209
[TBL] [Abstract][Full Text] [Related]
9. Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions.
Madabhushi A; Metaxas DN
IEEE Trans Med Imaging; 2003 Feb; 22(2):155-69. PubMed ID: 12715992
[TBL] [Abstract][Full Text] [Related]
10. Comparison of Otsu and an adapted Chan-Vese method to determine thyroid active volume using Monte Carlo generated SPECT images.
Högberg J; Andersén C; Rydén T; Lagerlöf JH
EJNMMI Phys; 2024 Jan; 11(1):6. PubMed ID: 38189877
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Intensity inhomogeneity correction for the breast sonogram: constrained fuzzy cell-based bipartitioning and polynomial surface modeling.
Lee CY; Chou YH; Huang CS; Chang YC; Tiu CM; Chen CM
Med Phys; 2010 Nov; 37(11):5645-54. PubMed ID: 21158276
[TBL] [Abstract][Full Text] [Related]
13. Malignant lesion segmentation in contrast-enhanced breast MR images based on the marker-controlled watershed.
Cui Y; Tan Y; Zhao B; Liberman L; Parbhu R; Kaplan J; Theodoulou M; Hudis C; Schwartz LH
Med Phys; 2009 Oct; 36(10):4359-69. PubMed ID: 19928066
[TBL] [Abstract][Full Text] [Related]
14. Carotid plaque segmentation from three-dimensional ultrasound images by direct three-dimensional sparse field level-set optimization.
Cheng J; Chen Y; Yu Y; Chiu B
Comput Biol Med; 2018 Mar; 94():27-40. PubMed ID: 29407996
[TBL] [Abstract][Full Text] [Related]
15. Auto-DenseUNet: Searchable neural network architecture for mass segmentation in 3D automated breast ultrasound.
Cao X; Chen H; Li Y; Peng Y; Zhou Y; Cheng L; Liu T; Shen D
Med Image Anal; 2022 Nov; 82():102589. PubMed ID: 36095905
[TBL] [Abstract][Full Text] [Related]
16. A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions.
Giri MG; Cavedon C; Mazzarotto R; Ferdeghini M
Med Phys; 2016 May; 43(5):2491. PubMed ID: 27147360
[TBL] [Abstract][Full Text] [Related]
17. Deep-Learning-Based Real-Time and Automatic Target-to-Background Ratio Calculation in Fluorescence Endoscopy for Cancer Detection and Localization.
Jiang Y; Chen J; Gong C; Wang TD; Seibel EJ
Diagnostics (Basel); 2022 Aug; 12(9):. PubMed ID: 36140433
[TBL] [Abstract][Full Text] [Related]
18. Models of breast lesions based on three-dimensional X-ray breast images.
Dukov N; Bliznakova K; Feradov F; Buliev I; Bosmans H; Mettivier G; Russo P; Cockmartin L; Bliznakov Z
Phys Med; 2019 Jan; 57():80-87. PubMed ID: 30738536
[TBL] [Abstract][Full Text] [Related]
19. Automated segmentation and measurement of the female pelvic floor from the mid-sagittal plane of 3D ultrasound volumes.
Szentimrey Z; Ameri G; Hong CX; Cheung RYK; Ukwatta E; Eltahawi A
Med Phys; 2023 Oct; 50(10):6215-6227. PubMed ID: 36964964
[TBL] [Abstract][Full Text] [Related]
20. Level set segmentation of breast masses in contrast-enhanced dedicated breast CT and evaluation of stopping criteria.
Kuo HC; Giger ML; Reiser I; Boone JM; Lindfors KK; Yang K; Edwards A
J Digit Imaging; 2014 Apr; 27(2):237-47. PubMed ID: 24162667
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
