166 related articles for article (PubMed ID: 32906088)
21. A multi-label CNN model for the automatic detection and segmentation of gliomas using [
Rahimpour M; Boellaard R; Jentjens S; Deckers W; Goffin K; Koole M
Eur J Nucl Med Mol Imaging; 2023 Jul; 50(8):2441-2452. PubMed ID: 36933075
[TBL] [Abstract][Full Text] [Related]
22. 18F-FDG PET/CT in primary brain lymphoma.
Albano D; Bosio G; Bertoli M; Giubbini R; Bertagna F
J Neurooncol; 2018 Feb; 136(3):577-583. PubMed ID: 29147860
[TBL] [Abstract][Full Text] [Related]
23. Computer-aided detection and segmentation of malignant melanoma lesions on whole-body
Dirks I; Keyaerts M; Neyns B; Vandemeulebroucke J
Comput Methods Programs Biomed; 2022 Jun; 221():106902. PubMed ID: 35636357
[TBL] [Abstract][Full Text] [Related]
24. TMTV-Net: fully automated total metabolic tumor volume segmentation in lymphoma PET/CT images - a multi-center generalizability analysis.
Yousefirizi F; Klyuzhin IS; O JH; Harsini S; Tie X; Shiri I; Shin M; Lee C; Cho SY; Bradshaw TJ; Zaidi H; Bénard F; Sehn LH; Savage KJ; Steidl C; Uribe CF; Rahmim A
Eur J Nucl Med Mol Imaging; 2024 Jun; 51(7):1937-1954. PubMed ID: 38326655
[TBL] [Abstract][Full Text] [Related]
25. Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET/CT in Hodgkin and non-Hodgkin lymphomas.
Sadik M; Lind E; Polymeri E; Enqvist O; Ulén J; Trägårdh E
Clin Physiol Funct Imaging; 2019 Jan; 39(1):78-84. PubMed ID: 30284376
[TBL] [Abstract][Full Text] [Related]
26. The correlation of
Qu YH; Long N; Ran C; Sun J
Clin Transl Oncol; 2021 Mar; 23(3):620-627. PubMed ID: 32683540
[TBL] [Abstract][Full Text] [Related]
27. A 3D deep convolutional neural network approach for the automated measurement of cerebellum tracer uptake in FDG PET-CT scans.
Xiong X; Linhardt TJ; Liu W; Smith BJ; Sun W; Bauer C; Sunderland JJ; Graham MM; Buatti JM; Beichel RR
Med Phys; 2020 Mar; 47(3):1058-1066. PubMed ID: 31855287
[TBL] [Abstract][Full Text] [Related]
28. Baseline
Aide N; Fruchart C; Nganoa C; Gac AC; Lasnon C
Eur Radiol; 2020 Aug; 30(8):4623-4632. PubMed ID: 32248365
[TBL] [Abstract][Full Text] [Related]
29. Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases.
Lindgren Belal S; Sadik M; Kaboteh R; Enqvist O; Ulén J; Poulsen MH; Simonsen J; Høilund-Carlsen PF; Edenbrandt L; Trägårdh E
Eur J Radiol; 2019 Apr; 113():89-95. PubMed ID: 30927965
[TBL] [Abstract][Full Text] [Related]
30. Evaluation of the prognostic value of different methods of calculating the tumour metabolic volume with
Guzmán Ortiz S; Mucientes Rasilla J; Vargas Núñez JA; Royuela A; Navarro Matilla B; Mitjavila Casanovas M
Rev Esp Med Nucl Imagen Mol (Engl Ed); 2020; 39(6):340-346. PubMed ID: 32646783
[TBL] [Abstract][Full Text] [Related]
31. Baseline Total Metabolic Tumor Volume Measured with Fixed or Different Adaptive Thresholding Methods Equally Predicts Outcome in Peripheral T Cell Lymphoma.
Cottereau AS; Hapdey S; Chartier L; Modzelewski R; Casasnovas O; Itti E; Tilly H; Vera P; Meignan MA; Becker S
J Nucl Med; 2017 Feb; 58(2):276-281. PubMed ID: 27754905
[TBL] [Abstract][Full Text] [Related]
32. Association between textural and morphological tumor indices on baseline PET-CT and early metabolic response on interim PET-CT in bulky malignant lymphomas.
Ben Bouallègue F; Tabaa YA; Kafrouni M; Cartron G; Vauchot F; Mariano-Goulart D
Med Phys; 2017 Sep; 44(9):4608-4619. PubMed ID: 28513853
[TBL] [Abstract][Full Text] [Related]
33. Does enhanced CT influence the biological GTV measurement on FDG-PET images?
Vera P; Modzelewski R; Hapdey S; Gouel P; Tilly H; Jardin F; Ruan S; Gardin I
Radiother Oncol; 2013 Jul; 108(1):86-90. PubMed ID: 23618503
[TBL] [Abstract][Full Text] [Related]
34. Evaluation of a convolution neural network for baseline total tumor metabolic volume on [
Karimdjee M; Delaby G; Huglo D; Baillet C; Willaume A; Dujardin S; Bailliez A
Eur Radiol; 2023 May; 33(5):3386-3395. PubMed ID: 36600126
[TBL] [Abstract][Full Text] [Related]
35. Impact of consensus contours from multiple PET segmentation methods on the accuracy of functional volume delineation.
Schaefer A; Vermandel M; Baillet C; Dewalle-Vignion AS; Modzelewski R; Vera P; Massoptier L; Parcq C; Gibon D; Fechter T; Nemer U; Gardin I; Nestle U
Eur J Nucl Med Mol Imaging; 2016 May; 43(5):911-924. PubMed ID: 26567163
[TBL] [Abstract][Full Text] [Related]
36. The usefulness of (18)F-FDG PET/CT for assessing methotrexate-associated lymphoproliferative disorder (MTX-LPD).
Watanabe S; Manabe O; Hirata K; Oyama-Manabe N; Hattori N; Kikuchi Y; Kobayashi K; Toyonaga T; Tamaki N
BMC Cancer; 2016 Aug; 16():635. PubMed ID: 27528380
[TBL] [Abstract][Full Text] [Related]
37. Variability and Repeatability of Quantitative Uptake Metrics in
Zhuang M; García DV; Kramer GM; Frings V; Smit EF; Dierckx R; Hoekstra OS; Boellaard R
J Nucl Med; 2019 May; 60(5):600-607. PubMed ID: 30389824
[TBL] [Abstract][Full Text] [Related]
38. Dual time-point FDG PET/CT and FDG uptake and related enzymes in lymphadenopathies: preliminary results.
Christlieb SB; Strandholdt CN; Olsen BB; Mylam KJ; Larsen TS; Nielsen AL; Rohde M; Gerke O; Olsen KE; Møller MB; Kristensen BW; Abildgaard N; Alavi A; Høilund-Carlsen PF
Eur J Nucl Med Mol Imaging; 2016 Sep; 43(10):1824-36. PubMed ID: 27102266
[TBL] [Abstract][Full Text] [Related]
39. Prognostic value of metabolic tumor volume and total lesion glycolysis from ¹⁸F-FDG PET/CT in lymph node metastases and risk stratification of endometrial carcinoma.
Liu DD; Li J; Li X; Xie L; Qin L; Peng F; Cheng MH
J Gynecol Oncol; 2019 Nov; 30(6):e89. PubMed ID: 31576685
[TBL] [Abstract][Full Text] [Related]
40. Rapid Contour-based Segmentation for
Besson FL; Henry T; Meyer C; Chevance V; Roblot V; Blanchet E; Arnould V; Grimon G; Chekroun M; Mabille L; Parent F; Seferian A; Bulifon S; Montani D; Humbert M; Chaumet-Riffaud P; Lebon V; Durand E
Radiology; 2018 Jul; 288(1):277-284. PubMed ID: 29613842
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
