782 related articles for article (PubMed ID: 34365038)
1. 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]
2. 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]
3. Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network.
Lee SB; Cho YJ; Yoon SH; Lee YY; Kim SH; Lee S; Choi YH; Cheon JE
Eur Radiol; 2022 Dec; 32(12):8463-8472. PubMed ID: 35524785
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Anthropometer3D: Automatic Multi-Slice Segmentation Software for the Measurement of Anthropometric Parameters from CT of PET/CT.
Decazes P; Tonnelet D; Vera P; Gardin I
J Digit Imaging; 2019 Apr; 32(2):241-250. PubMed ID: 30756268
[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. A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images.
Wang Y; Qiu Y; Thai T; Moore K; Liu H; Zheng B
Comput Methods Programs Biomed; 2017 Jun; 144():97-104. PubMed ID: 28495009
[TBL] [Abstract][Full Text] [Related]
8. Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.
Blanc-Durand P; Campedel L; Mule S; Jegou S; Luciani A; Pigneur F; Itti E
Eur Radiol; 2020 Jun; 30(6):3528-3537. PubMed ID: 32055950
[TBL] [Abstract][Full Text] [Related]
9. Automatic segmentation of prostate cancer metastases in PSMA PET/CT images using deep neural networks with weighted batch-wise dice loss.
Xu Y; Klyuzhin I; Harsini S; Ortiz A; Zhang S; Bénard F; Dodhia R; Uribe CF; Rahmim A; Lavista Ferres J
Comput Biol Med; 2023 May; 158():106882. PubMed ID: 37037147
[TBL] [Abstract][Full Text] [Related]
10. Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.
Panda A; Korfiatis P; Suman G; Garg SK; Polley EC; Singh DP; Chari ST; Goenka AH
Med Phys; 2021 May; 48(5):2468-2481. PubMed ID: 33595105
[TBL] [Abstract][Full Text] [Related]
11. ABCNet: A new efficient 3D dense-structure network for segmentation and analysis of body tissue composition on body-torso-wide CT images.
Liu T; Pan J; Torigian DA; Xu P; Miao Q; Tong Y; Udupa JK
Med Phys; 2020 Jul; 47(7):2986-2999. PubMed ID: 32170754
[TBL] [Abstract][Full Text] [Related]
12. Fully Automated, Semantic Segmentation of Whole-Body
Shiyam Sundar LK; Yu J; Muzik O; Kulterer OC; Fueger B; Kifjak D; Nakuz T; Shin HM; Sima AK; Kitzmantl D; Badawi RD; Nardo L; Cherry SR; Spencer BA; Hacker M; Beyer T
J Nucl Med; 2022 Dec; 63(12):1941-1948. PubMed ID: 35772962
[TBL] [Abstract][Full Text] [Related]
13. Quantification of body-torso-wide tissue composition on low-dose CT images via automatic anatomy recognition.
Liu T; Udupa JK; Miao Q; Tong Y; Torigian DA
Med Phys; 2019 Mar; 46(3):1272-1285. PubMed ID: 30614020
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network.
Blanc-Durand P; Jégou S; Kanoun S; Berriolo-Riedinger A; Bodet-Milin C; Kraeber-Bodéré F; Carlier T; Le Gouill S; Casasnovas RO; Meignan M; Itti E
Eur J Nucl Med Mol Imaging; 2021 May; 48(5):1362-1370. PubMed ID: 33097974
[TBL] [Abstract][Full Text] [Related]
17. GA-Net: A geographical attention neural network for the segmentation of body torso tissue composition.
Dai J; Liu T; Torigian DA; Tong Y; Han S; Nie P; Zhang J; Li R; Xie F; Udupa JK
Med Image Anal; 2024 Jan; 91():102987. PubMed ID: 37837691
[TBL] [Abstract][Full Text] [Related]
18. 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]
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. 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]
[Next] [New Search]
