Automatic Segmentation of Metastatic Breast Cancer Lesions on <sup>18</sup>F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment

Automatic Segmentation of Metastatic Breast Cancer Lesions on <sup>18</sup>F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment

Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations...

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Bibliographic Details
Main Authors: Noémie Moreau, Caroline Rousseau, Constance Fourcade, Gianmarco Santini, Aislinn Brennan, Ludovic Ferrer, Marie Lacombe, Camille Guillerminet, Mathilde Colombié, Pascal Jézéquel, Mario Campone, Nicolas Normand, Mathieu Rubeaux
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Cancers
Subjects:
deep learning
automatic segmentation
metastatic breast cancer
imaging biomarkers
disease monitoring
Online Access:https://www.mdpi.com/2072-6694/14/1/101

Internet

https://www.mdpi.com/2072-6694/14/1/101

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