Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR-Reference-Cited by-同舟云学术

Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric MR

Published:2017-07-13 Issue:1 Volume:7 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Trebeschi Stefano^ORCID,van Griethuysen Joost J. M.,Lambregts Doenja M. J.,Lahaye Max J.,Parmar Chintan,Bakers Frans C. H.,Peters Nicky H. G. M.,Beets-Tan Regina G. H.,Aerts Hugo J. W. L.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/s41598-017-05728-9.pdf

Reference39 articles.

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3. Martens, M. H. et al. Prospective, multicenter validation study of magnetic resonance volumetry for response assessment after preoperative chemoradiation in rectal cancer: Can the results in the literature be reproduced? Int. J. Radiat. Oncol. Biol. Phys. 93, 1005–1014 (2015).

4. Lambregts, D. M. J. et al. MRI and Diffusion-weighted MRI Volumetry for Identification of Complete Tumor Responders After Preoperative Chemoradiotherapy in Patients With Rectal Cancer: A Bi-institutional Validation Study. Ann. Surg. 262, 1034–9 (2015).

5. Carbone, S. F. et al. Assessment of response to chemoradiation therapy in rectal cancer using MR volumetry based on diffusion-weighted data sets: a preliminary report. Radiol. Med. 117, 1112–24 (2012).

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