257 related articles for article (PubMed ID: 33680934)
1. Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer.
Jin J; Zhu H; Zhang J; Ai Y; Zhang J; Teng Y; Xie C; Jin X
Front Oncol; 2020; 10():614201. PubMed ID: 33680934
[TBL] [Abstract][Full Text] [Related]
2. The Accuracy and Radiomics Feature Effects of Multiple U-net-Based Automatic Segmentation Models for Transvaginal Ultrasound Images of Cervical Cancer.
Jin J; Zhu H; Teng Y; Ai Y; Xie C; Jin X
J Digit Imaging; 2022 Aug; 35(4):983-992. PubMed ID: 35355160
[TBL] [Abstract][Full Text] [Related]
3. The Effects of Automatic Segmentations on Preoperative Lymph Node Status Prediction Models With Ultrasound Radiomics for Patients With Early Stage Cervical Cancer.
Teng Y; Ai Y; Liang T; Yu B; Jin J; Xie C; Jin X
Technol Cancer Res Treat; 2022; 21():15330338221099396. PubMed ID: 35522305
[No Abstract] [Full Text] [Related]
4. CNN-based automatic segmentations and radiomics feature reliability on contrast-enhanced ultrasound images for renal tumors.
Yang Y; Chen F; Liang H; Bai Y; Wang Z; Zhao L; Ma S; Niu Q; Li F; Xie T; Cai Y
Front Oncol; 2023; 13():1166988. PubMed ID: 37333811
[TBL] [Abstract][Full Text] [Related]
5. Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.
Lin YC; Lin G; Pandey S; Yeh CH; Wang JJ; Lin CY; Ho TY; Ko SF; Ng SH
Eur Radiol; 2023 Sep; 33(9):6548-6556. PubMed ID: 37338554
[TBL] [Abstract][Full Text] [Related]
6. CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features.
Wang Y; Wang M; Cao P; Wong EMF; Ho G; Lam TPW; Han L; Lee EYP
Quant Imaging Med Surg; 2023 Aug; 13(8):5218-5229. PubMed ID: 37581064
[TBL] [Abstract][Full Text] [Related]
7. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer.
Lin YC; Lin CH; Lu HY; Chiang HJ; Wang HK; Huang YT; Ng SH; Hong JH; Yen TC; Lai CH; Lin G
Eur Radiol; 2020 Mar; 30(3):1297-1305. PubMed ID: 31712961
[TBL] [Abstract][Full Text] [Related]
8. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms.
Altazi BA; Zhang GG; Fernandez DC; Montejo ME; Hunt D; Werner J; Biagioli MC; Moros EG
J Appl Clin Med Phys; 2017 Nov; 18(6):32-48. PubMed ID: 28891217
[TBL] [Abstract][Full Text] [Related]
9. Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study.
Moribata Y; Kurata Y; Nishio M; Kido A; Otani S; Himoto Y; Nishio N; Furuta A; Onishi H; Masui K; Kobayashi T; Nakamoto Y
Sci Rep; 2023 Jan; 13(1):628. PubMed ID: 36635425
[TBL] [Abstract][Full Text] [Related]
10. Deep learning-based automatic segmentation of meningioma from multiparametric MRI for preoperative meningioma differentiation using radiomic features: a multicentre study.
Chen H; Li S; Zhang Y; Liu L; Lv X; Yi Y; Ruan G; Ke C; Feng Y
Eur Radiol; 2022 Oct; 32(10):7248-7259. PubMed ID: 35420299
[TBL] [Abstract][Full Text] [Related]
11. Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer.
Owens CA; Peterson CB; Tang C; Koay EJ; Yu W; Mackin DS; Li J; Salehpour MR; Fuentes DT; Court LE; Yang J
PLoS One; 2018; 13(10):e0205003. PubMed ID: 30286184
[TBL] [Abstract][Full Text] [Related]
12. Diffusion and perfusion MRI radiomics obtained from deep learning segmentation provides reproducible and comparable diagnostic model to human in post-treatment glioblastoma.
Park JE; Ham S; Kim HS; Park SY; Yun J; Lee H; Choi SH; Kim N
Eur Radiol; 2021 May; 31(5):3127-3137. PubMed ID: 33128598
[TBL] [Abstract][Full Text] [Related]
13. Application of nnU-Net for Automatic Segmentation of Lung Lesions on CT Images and Its Implication for Radiomic Models.
Ferrante M; Rinaldi L; Botta F; Hu X; Dolp A; Minotti M; De Piano F; Funicelli G; Volpe S; Bellerba F; De Marco P; Raimondi S; Rizzo S; Shi K; Cremonesi M; Jereczek-Fossa BA; Spaggiari L; De Marinis F; Orecchia R; Origgi D
J Clin Med; 2022 Dec; 11(24):. PubMed ID: 36555950
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Interobserver Agreement in Automatic Segmentation Annotation of Prostate Magnetic Resonance Imaging.
Jin L; Ma Z; Li H; Gao F; Gao P; Yang N; Li D; Li M; Geng D
Bioengineering (Basel); 2023 Nov; 10(12):. PubMed ID: 38135930
[TBL] [Abstract][Full Text] [Related]
16. RefineNet-based 2D and 3D automatic segmentations for clinical target volume and organs at risks for patients with cervical cancer in postoperative radiotherapy.
Xiao C; Jin J; Yi J; Han C; Zhou Y; Ai Y; Xie C; Jin X
J Appl Clin Med Phys; 2022 Jul; 23(7):e13631. PubMed ID: 35533205
[TBL] [Abstract][Full Text] [Related]
17. Radiomics prognostic analysis of PET/CT images in a multicenter head and neck cancer cohort: investigating ComBat strategies, sub-volume characterization, and automatic segmentation.
Xu H; Abdallah N; Marion JM; Chauvet P; Tauber C; Carlier T; Lu L; Hatt M
Eur J Nucl Med Mol Imaging; 2023 May; 50(6):1720-1734. PubMed ID: 36690882
[TBL] [Abstract][Full Text] [Related]
18. Congenital diaphragmatic hernia: automatic lung and liver MRI segmentation with nnU-Net, reproducibility of pyradiomics features, and a machine learning application for the classification of liver herniation.
Conte L; Amodeo I; De Nunzio G; Raffaeli G; Borzani I; Persico N; Griggio A; Como G; Cascio D; Colnaghi M; Mosca F; Cavallaro G
Eur J Pediatr; 2024 May; 183(5):2285-2300. PubMed ID: 38416256
[TBL] [Abstract][Full Text] [Related]
19. Radiomics-Based Quality Control System for Automatic Cardiac Segmentation: A Feasibility Study.
Liu Q; Lu Q; Chai Y; Tao Z; Wu Q; Jiang M; Pu J
Bioengineering (Basel); 2023 Jul; 10(7):. PubMed ID: 37508818
[TBL] [Abstract][Full Text] [Related]
20. Stability and Reproducibility of Radiomic Features Based Various Segmentation Technique on MR Images of Hepatocellular Carcinoma (HCC).
Haniff NSM; Abdul Karim MK; Osman NH; Saripan MI; Che Isa IN; Ibahim MJ
Diagnostics (Basel); 2021 Aug; 11(9):. PubMed ID: 34573915
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
