Influence of inputs for bone lesion segmentation in longitudinal <sup>18</sup>F-FDG PET/CT imaging studies-Reference-Cited by-同舟云学术

Influence of inputs for bone lesion segmentation in longitudinal ¹⁸F-FDG PET/CT imaging studies

Published:2022-07-11 Issue: Volume: Page:
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Container-title:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Moreau Noemie¹,Rousseau Caroline²,Fourcade Constance¹,Santini Gianmarco³,Ferrer Ludovic⁴,Lacombe Marie⁵,Guillerminet Camille⁵,Colombie Mathilde⁵,Jezequel Pascal⁴,Campone Mario⁴,Rubeaux Mathieu³,Normand Nicolas¹

Affiliation:

1. Université de Nantes, CNRS, LS2N,Nantes,France,F-44000

2. University of Nantes, CRCINA, INSERM UMR1232, CNRS-ERL6001,Nantes,France

3. Keosys Medical Imaging,Nantes,France

4. University of Angers, CRCINA, INSERM UMR1232, CNRS-ERL6001,Angers,France

5. ICO Cancer Center,Nantes - Angers,France

Funder

EPICURE

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871081.pdf?arnumber=9871081

Reference13 articles.

1. Fully automated longitudinal segmentation of new or enlarged multiple sclerosis lesions using 3D convolutional neural networks

2. Advanced normalization tools (ANTS);avants;Insight J,2009

3. A new parameter for measuring metastatic bone involvement by prostate cancer: The bone scan index;imbriaco;Clinical Cancer Research,1998

4. Index for rating diagnostic tests;youden;Cancer,1950

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