Whole-Body Lesion Segmentation in 18F-FDG PET/CT

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Im HJ, Bradshaw T, Solaiyappan M, Cho SY. Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better? Nucl Med Mol Imaging. Feb 2018;52(1):5-15. doi:10.1007/s13139-017-0493-6

2018

Earlier work this paper cites.

Brito AE, Mourato F, Santos A, Mosci C, Ramos C, Etchebehere E. Validation of the Semiautomatic Quantification of (18)F-Fluoride PET/CT Whole-Body Skeletal Tumor Burden. J Nucl Med Technol. Dec 2018;46(4):378-383. doi:10.2967/jnmt.118.211474

2018

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Falk, T. et al. UNet: deep learning for cell counting, detection, and morphometry. Nat. Methods 16

2019

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Zhao Y, Gafita A, Vollnberg B, et al. Deep neural network for automatic characterization of lesions on (68)Ga-PSMA-11 PET/CT. Eur J Nucl Med Mol Imaging. Mar 2020;47(3):603-613. doi:10.1007/s00259-019-04606-y

2020

Cited alongside, same era.

Weisman AJ, Kieler MW, Perlman S, et al. Comparison of 11 automated PET segmentation methods in lymphoma. Phys Med Biol. Nov 27 2020;65(23):235019. doi:10.1088/1361-6560/abb6bd

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Tamal M. Intensity threshold based solid tumour segmentation method for Positron Emission Tomography (PET) images: A review. Heliyon. Oct 2020;6(10):e05267. doi:10.1016/j.heliyon.2020.e05267

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Barrington SF, Zwezerijnen B, de Vet HCW, et al. Automated Segmentation of Baseline Metabolic Total Tumor Burden in Diffuse Large B-Cell Lymphoma: Which Method Is Most Successful? A Study on Behalf of the PETRA Consortium. J Nucl Med. Mar 2021;62(3):332-337. doi:10.2967/jnumed.119.238923

2021

Cited alongside, same era.

Früh M, Fischer M, Schilling A, Gatidis S, Hepp T. Weakly supervised segmentation of tumor lesions in PET-CT hybrid imaging. J Med Imaging (Bellingham). Sep 2021;8(5):054003. doi:10.1117/1.Jmi.8.5.054003

2021

Later among the works it cites.

Isensee, F., Jaeger, P.F., Kohl, S.A.A. et al. nnUNet: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18, 203–211 (2021). \doi

2021

Later among the works it cites.

Nickols N, Anand A, Johnsson K, et al. aPROMISE: A Novel Automated PROMISE Platform to Standardize Evaluation of Tumor Burden in (18)F-DCFPyL Images of Veterans with Prostate Cancer. J Nucl Med. Feb 2022;63(2):233-239. doi:10.2967/jnumed.120.261863

2022

Closest in time.

Dhiraj Maji, Prarthana Sigedar, Munendra Singh. Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors. Biomedical Signal Processing and Control, 71, 2022, \doi

2022

Closest in time.