Similar articles for PMID: 35772962

Year	Number of Results
2009	1
2010	1
2011	3
2012	3
2013	8
2014	3
2015	2
2016	8
2017	8
2018	6
2019	19
2020	21
2021	20
2022	31
2023	20

1

Fully Automated, Semantic Segmentation of Whole-Body ¹⁸F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence.

Sundar LKS, 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. Sundar LKS, et al. J Nucl Med. 2022 Dec;63(12):1941-1948. doi: 10.2967/jnumed.122.264063. Epub 2022 Jun 30. J Nucl Med. 2022. PMID: 35772962

2

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. Lee YS, et al. Clin Nutr. 2021 Aug;40(8):5038-5046. doi: 10.1016/j.clnu.2021.06.025. Epub 2021 Jul 15. Clin Nutr. 2021. PMID: 34365038 Free article.

3

Computer-aided detection and segmentation of malignant melanoma lesions on whole-body ¹⁸F-FDG PET/CT using an interpretable deep learning approach.

Dirks I, Keyaerts M, Neyns B, Vandemeulebroucke J. Dirks I, et al. Comput Methods Programs Biomed. 2022 Jun;221:106902. doi: 10.1016/j.cmpb.2022.106902. Epub 2022 May 22. Comput Methods Programs Biomed. 2022. PMID: 35636357 Free article.

4

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach.

Lavdas I, Glocker B, Kamnitsas K, Rueckert D, Mair H, Sandhu A, Taylor SA, Aboagye EO, Rockall AG. Lavdas I, et al. Med Phys. 2017 Oct;44(10):5210-5220. doi: 10.1002/mp.12492. Epub 2017 Aug 31. Med Phys. 2017. PMID: 28756622 Free article.

5

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. Lee SB, et al. Eur Radiol. 2022 Dec;32(12):8463-8472. doi: 10.1007/s00330-022-08829-w. Epub 2022 May 7. Eur Radiol. 2022. PMID: 35524785

6

A Dirichlet process mixture model for automatic (18)F-FDG PET image segmentation: Validation study on phantoms and on lung and esophageal lesions.

Giri MG, Cavedon C, Mazzarotto R, Ferdeghini M. Giri MG, et al. Med Phys. 2016 May;43(5):2491. doi: 10.1118/1.4947123. Med Phys. 2016. PMID: 27147360

7

A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging.

Bagci U, Foster B, Miller-Jaster K, Luna B, Dey B, Bishai WR, Jonsson CB, Jain S, Mollura DJ. Bagci U, et al. EJNMMI Res. 2013 Jul 23;3(1):55. doi: 10.1186/2191-219X-3-55. EJNMMI Res. 2013. PMID: 23879987 Free PMC article.

8

Dual-modality multi-atlas segmentation of torso organs from [¹⁸F]FDG-PET/CT images.

Wang H, Zhang N, Huo L, Zhang B. Wang H, et al. Int J Comput Assist Radiol Surg. 2019 Mar;14(3):473-482. doi: 10.1007/s11548-018-1879-3. Epub 2018 Nov 2. Int J Comput Assist Radiol Surg. 2019. PMID: 30390179

9

More advantages in detecting bone and soft tissue metastases from prostate cancer using ¹⁸F-PSMA PET/CT.

Pianou NK, Stavrou PZ, Vlontzou E, Rondogianni P, Exarhos DN, Datseris IE. Pianou NK, et al. Hell J Nucl Med. 2019 Jan-Apr;22(1):6-9. doi: 10.1967/s002449910952. Epub 2019 Mar 7. Hell J Nucl Med. 2019. PMID: 30843003 Free article.

10

An artificial intelligence-driven image quality assessment system for whole-body [¹⁸F]FDG PET/CT.

Qi C, Wang S, Yu H, Zhang Y, Hu P, Tan H, Shi Y, Shi H. Qi C, et al. Eur J Nucl Med Mol Imaging. 2023 Apr;50(5):1318-1328. doi: 10.1007/s00259-022-06078-z. Epub 2022 Dec 19. Eur J Nucl Med Mol Imaging. 2023. PMID: 36529840

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