177 related articles for article (PubMed ID: 37723444)
1. Automatic segmentation of large-scale CT image datasets for detailed body composition analysis.
Ahmad N; Strand R; Sparresäter B; Tarai S; Lundström E; Bergström G; Ahlström H; Kullberg J
BMC Bioinformatics; 2023 Sep; 24(1):346. PubMed ID: 37723444
[TBL] [Abstract][Full Text] [Related]
2. Voxel-wise body composition analysis using image registration of a three-slice CT imaging protocol: methodology and proof-of-concept studies.
Ahmad N; Dahlberg H; Jönsson H; Tarai S; Guggilla RK; Strand R; Lundström E; Bergström G; Ahlström H; Kullberg J
Biomed Eng Online; 2024 Apr; 23(1):42. PubMed ID: 38614974
[TBL] [Abstract][Full Text] [Related]
3. Automated segmentation of five different body tissues on computed tomography using deep learning.
Pu L; Gezer NS; Ashraf SF; Ocak I; Dresser DE; Dhupar R
Med Phys; 2023 Jan; 50(1):178-191. PubMed ID: 36008356
[TBL] [Abstract][Full Text] [Related]
4. Automated analysis of liver fat, muscle and adipose tissue distribution from CT suitable for large-scale studies.
Kullberg J; Hedström A; Brandberg J; Strand R; Johansson L; Bergström G; Ahlström H
Sci Rep; 2017 Sep; 7(1):10425. PubMed ID: 28874743
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. A fully convolutional neural network for comprehensive compartmentalization of abdominal adipose tissue compartments in MRI.
Kway YM; Thirumurugan K; Michael N; Tan KH; Godfrey KM; Gluckman P; Chong YS; Venkataraman K; Khoo EYH; Khoo CM; Leow MK; Tai ES; Chan JK; Chan SY; Eriksson JG; Fortier MV; Lee YS; Velan SS; Feng M; Sadananthan SA
Comput Biol Med; 2023 Dec; 167():107608. PubMed ID: 37897959
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet.
Özcan F; Uçan ON; Karaçam S; Tunçman D
Bioengineering (Basel); 2023 Feb; 10(2):. PubMed ID: 36829709
[TBL] [Abstract][Full Text] [Related]
9. Automated Segmentation of Visceral, Deep Subcutaneous, and Superficial Subcutaneous Adipose Tissue Volumes in MRI of Neonates and Young Children.
Kway YM; Thirumurugan K; Tint MT; Michael N; Shek LP; Yap FKP; Tan KH; Godfrey KM; Chong YS; Fortier MV; Marx UC; Eriksson JG; Lee YS; Velan SS; Feng M; Sadananthan SA
Radiol Artif Intell; 2021 Sep; 3(5):e200304. PubMed ID: 34617030
[TBL] [Abstract][Full Text] [Related]
10. Multi-scale segmentation squeeze-and-excitation UNet with conditional random field for segmenting lung tumor from CT images.
Zhang B; Qi S; Wu Y; Pan X; Yao Y; Qian W; Guan Y
Comput Methods Programs Biomed; 2022 Jul; 222():106946. PubMed ID: 35716533
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies.
Bhanu PK; Arvind CS; Yeow LY; Chen WX; Lim WS; Tan CH
MAGMA; 2022 Apr; 35(2):205-220. PubMed ID: 34338926
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Technical Note: Automatic segmentation of CT images for ventral body composition analysis.
Fu Y; Ippolito JE; Ludwig DR; Nizamuddin R; Li HH; Yang D
Med Phys; 2020 Nov; 47(11):5723-5730. PubMed ID: 32969050
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. 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]
18. 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]
19. Automatic segmentation and applicator reconstruction for CT-based brachytherapy of cervical cancer using 3D convolutional neural networks.
Zhang D; Yang Z; Jiang S; Zhou Z; Meng M; Wang W
J Appl Clin Med Phys; 2020 Oct; 21(10):158-169. PubMed ID: 32991783
[TBL] [Abstract][Full Text] [Related]
20. Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size.
Zhu G; Luo X; Yang T; Cai L; Yeo JH; Yan G; Yang J
Front Physiol; 2022; 13():1084202. PubMed ID: 36601346
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]