1. Hoink AJ, Wessling J, Koch R, Schulke C, Kohlhase N, Wassenaar L, et al. Comparison of manual and semi-automatic measuring techniques in MSCT scans of patients with lymphoma: a multicentre study. European radiology. 2014;24(11):2709-18.
2. Fabel M, Bolte H, von Tengg-Kobligk H, Bornemann L, Dicken V, Delorme S, et al. Semi-automated volumetric analysis of lymph node metastases during follow-up--initial results. European radiology. 2011;21(4):683-92.
3. Arnljots TS, Al-Sharbaty Z, Lardner E, All-Eriksson C, Seregard S, Stalhammar G. Tumour thickness, diameter, area or volume? The prognostic significance of conventional versus digital image analysis-based size estimation methods in uveal melanoma. Acta ophthalmologica. 2018;96(5):510-8.
4. Hesamian MH, Jia W, He X, Kennedy P. Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges. Journal of Digital Imaging. 2019;32(4):582-96.
5. Sakinis T, Milletari F, Roth H, Korfiatis P, Kostandy P, Philbrick K, et al. Interactive segmentation of medical images through fully convolutional neural networks. arXiv e-prints [Internet]. 2019 March 01, 2019.
Available from: https://ui.adsabs.harvard.edu/abs/2019arXiv190308205S.
6. Sakinis T, Jenssen H. DeepGrow online lymph node segmentation 2020.
Available from: https://ecrlymphnode.medicalsegmentation.com.
7. Holger R, Lu, Le, Seff, Ari, Cherry, Kevin M, Hoffman, Joanne, Wang, Shijun, … Summers, Ronald M. A new 2.5 D representation for lymph node detection in CT. The Cancer Imaging Archive.
http://doi.org/10.7937/K9/TCIA.2015.AQIIDCNM2015.