209 related articles for article (PubMed ID: 35680433)
1. Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients.
Ackermans LLGC; Volmer L; Timmermans QMMA; Brecheisen R; Damink SMWO; Dekker A; Loeffen D; Poeze M; Blokhuis TJ; Wee L; Ten Bosch JA
Injury; 2022 Nov; 53 Suppl 3():S30-S41. PubMed ID: 35680433
[TBL] [Abstract][Full Text] [Related]
2. Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients.
Ackermans LLGC; Volmer L; Wee L; Brecheisen R; Sánchez-González P; Seiffert AP; Gómez EJ; Dekker A; Ten Bosch JA; Olde Damink SMW; Blokhuis TJ
Sensors (Basel); 2021 Mar; 21(6):. PubMed ID: 33809710
[TBL] [Abstract][Full Text] [Related]
3. Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment.
Lee YS; Hong N; Witanto JN; Choi YR; Park J; Decazes P; Eude F; Kim CO; Chang Kim H; Goo JM; Rhee Y; Yoon SH
Clin Nutr; 2021 Aug; 40(8):5038-5046. PubMed ID: 34365038
[TBL] [Abstract][Full Text] [Related]
4. End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT.
Nowak S; Theis M; Wichtmann BD; Faron A; Froelich MF; Tollens F; Geißler HL; Block W; Luetkens JA; Attenberger UI; Sprinkart AM
Eur Radiol; 2022 May; 32(5):3142-3151. PubMed ID: 34595539
[TBL] [Abstract][Full Text] [Related]
5. Concordance of Computed Tomography Regional Body Composition Analysis Using a Fully Automated Open-Source Neural Network versus a Reference Semi-Automated Program with Manual Correction.
Gomez-Perez SL; Zhang Y; Byrne C; Wakefield C; Geesey T; Sclamberg J; Peterson S
Sensors (Basel); 2022 Apr; 22(9):. PubMed ID: 35591047
[TBL] [Abstract][Full Text] [Related]
6. Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation.
Zopfs D; Bousabarah K; Lennartz S; Santos DPD; Schlaak M; Theurich S; Reimer RP; Maintz D; Haneder S; Große Hokamp N
Eur J Radiol; 2020 Sep; 130():109153. PubMed ID: 32717577
[TBL] [Abstract][Full Text] [Related]
7. Deep learning method for localization and segmentation of abdominal CT.
Dabiri S; Popuri K; Ma C; Chow V; Feliciano EMC; Caan BJ; Baracos VE; Beg MF
Comput Med Imaging Graph; 2020 Oct; 85():101776. PubMed ID: 32862015
[TBL] [Abstract][Full Text] [Related]
8. Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography.
Park HJ; Shin Y; Park J; Kim H; Lee IS; Seo DW; Huh J; Lee TY; Park T; Lee J; Kim KW
Korean J Radiol; 2020 Jan; 21(1):88-100. PubMed ID: 31920032
[TBL] [Abstract][Full Text] [Related]
9. Visceral adiposity and inflammatory bowel disease.
Rowan CR; McManus J; Boland K; O'Toole A
Int J Colorectal Dis; 2021 Nov; 36(11):2305-2319. PubMed ID: 34104989
[TBL] [Abstract][Full Text] [Related]
10. Automated versus manual analysis of body composition measures on computed tomography in patients with bladder cancer.
Rigiroli F; Zhang D; Molinger J; Wang Y; Chang A; Wischmeyer PE; Inman BA; Gupta RT
Eur J Radiol; 2022 Sep; 154():110413. PubMed ID: 35732083
[TBL] [Abstract][Full Text] [Related]
11. A Deep Learning Model to Automate Skeletal Muscle Area Measurement on Computed Tomography Images.
Amarasinghe KC; Lopes J; Beraldo J; Kiss N; Bucknell N; Everitt S; Jackson P; Litchfield C; Denehy L; Blyth BJ; Siva S; MacManus M; Ball D; Li J; Hardcastle N
Front Oncol; 2021; 11():580806. PubMed ID: 34026597
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of automated computed tomography segmentation to assess body composition and mortality associations in cancer patients.
Cespedes Feliciano EM; Popuri K; Cobzas D; Baracos VE; Beg MF; Khan AD; Ma C; Chow V; Prado CM; Xiao J; Liu V; Chen WY; Meyerhardt J; Albers KB; Caan BJ
J Cachexia Sarcopenia Muscle; 2020 Oct; 11(5):1258-1269. PubMed ID: 32314543
[TBL] [Abstract][Full Text] [Related]
13. Automated body composition analysis of clinically acquired computed tomography scans using neural networks.
Paris MT; Tandon P; Heyland DK; Furberg H; Premji T; Low G; Mourtzakis M
Clin Nutr; 2020 Oct; 39(10):3049-3055. PubMed ID: 32007318
[TBL] [Abstract][Full Text] [Related]
14. Pediatric body composition based on automatic segmentation of computed tomography scans: a pilot study.
Samim A; Spijkers S; Moeskops P; Littooij AS; de Jong PA; Veldhuis WB; de Vos BD; van Santen HM; Nievelstein RAJ
Pediatr Radiol; 2023 Nov; 53(12):2492-2501. PubMed ID: 37640800
[TBL] [Abstract][Full Text] [Related]
15. Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis.
Bedrikovetski S; Seow W; Kroon HM; Traeger L; Moore JW; Sammour T
Eur J Radiol; 2022 Apr; 149():110218. PubMed ID: 35183899
[TBL] [Abstract][Full Text] [Related]
16. Fully Automated Segmentation of Connective Tissue Compartments for CT-Based Body Composition Analysis: A Deep Learning Approach.
Nowak S; Faron A; Luetkens JA; Geißler HL; Praktiknjo M; Block W; Thomas D; Sprinkart AM
Invest Radiol; 2020 Jun; 55(6):357-366. PubMed ID: 32369318
[TBL] [Abstract][Full Text] [Related]
17. A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.
Shen H; He P; Ren Y; Huang Z; Li S; Wang G; Cong M; Luo D; Shao D; Lee EY; Cui R; Huo L; Qin J; Liu J; Hu Z; Liu Z; Zhang N
Quant Imaging Med Surg; 2023 Mar; 13(3):1384-1398. PubMed ID: 36915346
[TBL] [Abstract][Full Text] [Related]
18. Fully-automated sarcopenia assessment in head and neck cancer: development and external validation of a deep learning pipeline.
Ye Z; Saraf A; Ravipati Y; Hoebers F; Zha Y; Zapaishchykova A; Likitlersuang J; Tishler RB; Schoenfeld JD; Margalit DN; Haddad RI; Mak RH; Naser M; Wahid KA; Sahlsten J; Jaskari J; Kaski K; Mäkitie AA; Fuller CD; Aerts HJWL; Kann BH
medRxiv; 2023 Mar; ():. PubMed ID: 36945519
[TBL] [Abstract][Full Text] [Related]
19. Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography.
Ha J; Park T; Kim HK; Shin Y; Ko Y; Kim DW; Sung YS; Lee J; Ham SJ; Khang S; Jeong H; Koo K; Lee J; Kim KW
Sci Rep; 2021 Nov; 11(1):21656. PubMed ID: 34737340
[TBL] [Abstract][Full Text] [Related]
20. Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.
Hemke R; Buckless CG; Tsao A; Wang B; Torriani M
Skeletal Radiol; 2020 Mar; 49(3):387-395. PubMed ID: 31396667
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]