Automatic segmentation of organs-at-risk from head-and-neck CT using separable convolutional neural network with hard-region-weighted loss-Reference-Cited by-同舟云学术

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Automatic segmentation of organs-at-risk from head-and-neck CT using separable convolutional neural network with hard-region-weighted loss

Published:2021-06 Issue: Volume:442 Page:184-199
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Lei Wenhui,Mei Haochen,Sun Zhengwentai,Ye Shan,Gu Ran,Wang Huan,Huang Rui,Zhang Shichuan,Zhang Shaoting,Wang Guotai

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference52 articles.

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2. The enigmatic epidemiology of nasopharyngeal carcinoma;Chang;Cancer Epidemiol. Biomark. Prev,2006

3. Treatment planning and delivery of intensity-modulated radiation therapy for primary nasopharynx cancer;Hunt;Int. J. Radiat. Oncol. Biol. Phys.,2001

4. AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy;Zhu;Med. Phys.,2019

5. FocusNet: Imbalanced Large and Small Organ Segmentation with an End-to-End Deep Neural Network for Head and Neck CT Images;Gao,2019

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3. Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning;Communications Medicine;2024-06-08

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