203 related articles for article (PubMed ID: 35640004)
1. 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]
2. Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.
Wennmann M; Neher P; Stanczyk N; Kahl KC; Kächele J; Weru V; Hielscher T; Grözinger M; Chmelik J; Zhang KS; Bauer F; Nonnenmacher T; Debic M; Sauer S; Rotkopf LT; Jauch A; Schlamp K; Mai EK; Weinhold N; Afat S; Horger M; Goldschmidt H; Schlemmer HP; Weber TF; Delorme S; Kurz FT; Maier-Hein K
Invest Radiol; 2023 Apr; 58(4):273-282. PubMed ID: 36256790
[TBL] [Abstract][Full Text] [Related]
3. Prediction of Bone Marrow Biopsy Results From MRI in Multiple Myeloma Patients Using Deep Learning and Radiomics.
Wennmann M; Ming W; Bauer F; Chmelik J; Klein A; Uhlenbrock C; Grözinger M; Kahl KC; Nonnenmacher T; Debic M; Hielscher T; Thierjung H; Rotkopf LT; Stanczyk N; Sauer S; Jauch A; Götz M; Kurz FT; Schlamp K; Horger M; Afat S; Besemer B; Hoffmann M; Hoffend J; Kraemer D; Graeven U; Ringelstein A; Bonekamp D; Kleesiek J; Floca RO; Hillengass J; Mai EK; Weinhold N; Weber TF; Goldschmidt H; Schlemmer HP; Maier-Hein K; Delorme S; Neher P
Invest Radiol; 2023 Oct; 58(10):754-765. PubMed ID: 37222527
[TBL] [Abstract][Full Text] [Related]
4. Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram.
Zhong J; Zhang C; Hu Y; Zhang J; Liu Y; Si L; Xing Y; Ding D; Geng J; Jiao Q; Zhang H; Yang G; Yao W
Eur Radiol; 2022 Sep; 32(9):6196-6206. PubMed ID: 35364712
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.
Yang L; Wang T; Zhang J; Kang S; Xu S; Wang K
BMC Med Imaging; 2024 Mar; 24(1):56. PubMed ID: 38443817
[TBL] [Abstract][Full Text] [Related]
8. Automatic Vertebral Body Segmentation Based on Deep Learning of Dixon Images for Bone Marrow Fat Fraction Quantification.
Zhou J; Damasceno PF; Chachad R; Cheung JR; Ballatori A; Lotz JC; Lazar AA; Link TM; Fields AJ; Krug R
Front Endocrinol (Lausanne); 2020; 11():612. PubMed ID: 32982989
[No Abstract] [Full Text] [Related]
9. IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.
Choi Y; Nam Y; Lee YS; Kim J; Ahn KJ; Jang J; Shin NY; Kim BS; Jeon SS
Eur J Radiol; 2020 Jul; 128():109031. PubMed ID: 32417712
[TBL] [Abstract][Full Text] [Related]
10. Clinical Evaluation of a Multiparametric Deep Learning Model for Glioblastoma Segmentation Using Heterogeneous Magnetic Resonance Imaging Data From Clinical Routine.
Perkuhn M; Stavrinou P; Thiele F; Shakirin G; Mohan M; Garmpis D; Kabbasch C; Borggrefe J
Invest Radiol; 2018 Nov; 53(11):647-654. PubMed ID: 29863600
[TBL] [Abstract][Full Text] [Related]
11. Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis.
Spuhler KD; Ding J; Liu C; Sun J; Serrano-Sosa M; Moriarty M; Huang C
Magn Reson Med; 2019 Aug; 82(2):786-795. PubMed ID: 30957936
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics.
Wang S; Sun M; Sun J; Wang Q; Wang G; Wang X; Meng X; Wang Z; Yu H
Comput Biol Med; 2024 Jun; 175():108502. PubMed ID: 38678943
[TBL] [Abstract][Full Text] [Related]
15. A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet+.
Wang J; Peng Y; Jing S; Han L; Li T; Luo J
BMC Cancer; 2023 Nov; 23(1):1060. PubMed ID: 37923988
[TBL] [Abstract][Full Text] [Related]
16. Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI.
Zhang L; Mohamed AA; Chai R; Guo Y; Zheng B; Wu S
J Magn Reson Imaging; 2020 Feb; 51(2):635-643. PubMed ID: 31301201
[TBL] [Abstract][Full Text] [Related]
17. Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.
Gross M; Huber S; Arora S; Ze'evi T; Haider SP; Kucukkaya AS; Iseke S; Kuhn TN; Gebauer B; Michallek F; Dewey M; Vilgrain V; Sartoris R; Ronot M; Jaffe A; Strazzabosco M; Chapiro J; Onofrey JA
Eur Radiol; 2024 Jan; ():. PubMed ID: 38217704
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Fully Automated MR Detection and Segmentation of Brain Metastases in Non-small Cell Lung Cancer Using Deep Learning.
Jünger ST; Hoyer UCI; Schaufler D; Laukamp KR; Goertz L; Thiele F; Grunz JP; Schlamann M; Perkuhn M; Kabbasch C; Persigehl T; Grau S; Borggrefe J; Scheffler M; Shahzad R; Pennig L
J Magn Reson Imaging; 2021 Nov; 54(5):1608-1622. PubMed ID: 34032344
[TBL] [Abstract][Full Text] [Related]
20. Automated segmentation of magnetic resonance bone marrow signal: a feasibility study.
von Brandis E; Jenssen HB; Avenarius DFM; Bjørnerud A; Flatø B; Tomterstad AH; Lilleby V; Rosendahl K; Sakinis T; Zadig PKK; Müller LO
Pediatr Radiol; 2022 May; 52(6):1104-1114. PubMed ID: 35107593
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
