214 related articles for article (PubMed ID: 36555950)
1. 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]
2. Development and evaluation of two open-source nnU-Net models for automatic segmentation of lung tumors on PET and CT images with and without respiratory motion compensation.
Carles M; Kuhn D; Fechter T; Baltas D; Mix M; Nestle U; Grosu AL; Martí-Bonmatí L; Radicioni G; Gkika E
Eur Radiol; 2024 Apr; ():. PubMed ID: 38662100
[TBL] [Abstract][Full Text] [Related]
3. Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography.
Abbani N; Baudier T; Rit S; Franco FD; Okoli F; Jaouen V; Tilquin F; Barateau A; Simon A; de Crevoisier R; Bert J; Sarrut D
Med Phys; 2022 Nov; 49(11):6930-6944. PubMed ID: 36000762
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Performance of alternative manual and automated deep learning segmentation techniques for the prediction of benign and malignant lung nodules.
Selby HM; Mukherjee P; Parham C; Malik SB; Gevaert O; Napel S; Shah RP
J Med Imaging (Bellingham); 2023 Jul; 10(4):044006. PubMed ID: 37564098
[TBL] [Abstract][Full Text] [Related]
6. Deep learning for segmentation of the cervical cancer gross tumor volume on magnetic resonance imaging for brachytherapy.
Rodríguez Outeiral R; González PJ; Schaake EE; van der Heide UA; Simões R
Radiat Oncol; 2023 May; 18(1):91. PubMed ID: 37248490
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis.
Ataei A; Eggermont F; Verdonschot N; Lessmann N; Tanck E
Bone; 2024 Feb; 179():116987. PubMed ID: 38061504
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study.
Wennmann M; Klein A; Bauer F; Chmelik J; Grözinger M; Uhlenbrock C; Lochner J; Nonnenmacher T; Rotkopf LT; Sauer S; Hielscher T; Götz M; Floca RO; Neher P; Bonekamp D; Hillengass J; Kleesiek J; Weinhold N; Weber TF; Goldschmidt H; Delorme S; Maier-Hein K; Schlemmer HP
Invest Radiol; 2022 Nov; 57(11):752-763. PubMed ID: 35640004
[TBL] [Abstract][Full Text] [Related]
11. Automatic segmentation of organs at risk and tumors in CT images of lung cancer from partially labelled datasets with a semi-supervised conditional nnU-Net.
Zhang G; Yang Z; Huo B; Chai S; Jiang S
Comput Methods Programs Biomed; 2021 Nov; 211():106419. PubMed ID: 34563895
[TBL] [Abstract][Full Text] [Related]
12. An open-source nnU-net algorithm for automatic segmentation of MRI scans in the male pelvis for adaptive radiotherapy.
Lorenzen EL; Celik B; Sarup N; Dysager L; Christiansen RL; Bertelsen AS; Bernchou U; Agergaard SN; Konrad ML; Brink C; Mahmood F; Schytte T; Nyborg CJ
Front Oncol; 2023; 13():1285725. PubMed ID: 38023233
[TBL] [Abstract][Full Text] [Related]
13. Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images.
Veiga-Canuto D; Cerdà-Alberich L; Sangüesa Nebot C; Martínez de Las Heras B; Pötschger U; Gabelloni M; Carot Sierra JM; Taschner-Mandl S; Düster V; Cañete A; Ladenstein R; Neri E; Martí-Bonmatí L
Cancers (Basel); 2022 Jul; 14(15):. PubMed ID: 35954314
[TBL] [Abstract][Full Text] [Related]
14. Deep learning-based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net.
Choi Y; Bang J; Kim SY; Seo M; Jang J
Eur Radiol; 2024 Jan; ():. PubMed ID: 38243135
[TBL] [Abstract][Full Text] [Related]
15. Fully automatic segmentation of craniomaxillofacial CT scans for computer-assisted orthognathic surgery planning using the nnU-Net framework.
Dot G; Schouman T; Dubois G; Rouch P; Gajny L
Eur Radiol; 2022 Jun; 32(6):3639-3648. PubMed ID: 35037088
[TBL] [Abstract][Full Text] [Related]
16. Comparison of Prostate MRI Lesion Segmentation Agreement Between Multiple Radiologists and a Fully Automatic Deep Learning System.
Schelb P; Tavakoli AA; Tubtawee T; Hielscher T; Radtke JP; Görtz M; Schütz V; Kuder TA; Schimmöller L; Stenzinger A; Hohenfellner M; Schlemmer HP; Bonekamp D
Rofo; 2021 May; 193(5):559-573. PubMed ID: 33212541
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Independent Validation of a Deep Learning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images.
Veiga-Canuto D; Cerdà-Alberich L; Jiménez-Pastor A; Carot Sierra JM; Gomis-Maya A; Sangüesa-Nebot C; Fernández-Patón M; Martínez de Las Heras B; Taschner-Mandl S; Düster V; Pötschger U; Simon T; Neri E; Alberich-Bayarri Á; Cañete A; Hero B; Ladenstein R; Martí-Bonmatí L
Cancers (Basel); 2023 Mar; 15(5):. PubMed ID: 36900410
[TBL] [Abstract][Full Text] [Related]
19. Automatic Segmentation and Radiomics for Identification and Activity Assessment of CTE Lesions in Crohn's Disease.
Gao Y; Zhang B; Zhao D; Li S; Rong C; Sun M; Wu X
Inflamm Bowel Dis; 2023 Nov; ():. PubMed ID: 38011673
[TBL] [Abstract][Full Text] [Related]
20. Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.
Orlando N; Gillies DJ; Gyacskov I; Romagnoli C; D'Souza D; Fenster A
Med Phys; 2020 Jun; 47(6):2413-2426. PubMed ID: 32166768
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
