Deep learning for variational multimodality tumor segmentation in PET/CT-Reference-Cited by-同舟云学术

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Deep learning for variational multimodality tumor segmentation in PET/CT

Published:2020-06 Issue: Volume:392 Page:277-295
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Li Laquan,Zhao Xiangming,Lu Wei,Tan Shan

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference62 articles.

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2. Simultaneous tumor segmentation, image restoration, and blur kernel estimation in PET using multiple regularizations;Li;Comput. Vis. Image Underst.,2017

3. Medical image fusion: a survey of the state of the art;James;Inf. Fusion,2013

4. Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy;Tan;Int. J. Radiat. Oncol. Biol. Phys.,2013

5. Modeling pathologic response of esophageal cancer to chemoradiotherapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics;Zhang;Int. J. Radiat. Oncol. Biol. Phys.,2014

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