Atlas-based Auto-segmentation of CT Images in Head and Neck Cancer: What is the Best Approach?-Reference-Cited by-同舟云学术

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Atlas-based Auto-segmentation of CT Images in Head and Neck Cancer: What is the Best Approach?

Published:2008-09 Issue:1 Volume:72 Page:S591
ISSN:0360-3016
Container-title:International Journal of Radiation Oncology*Biology*Physics
language:en
Short-container-title:International Journal of Radiation Oncology*Biology*Physics

Author:

Hoogeman M.S.,Han X.,Teguh D.,Voet P.,Nowak P.,Wolf T.,Hibbard L.,Heijmen B.,Levendag P.

Publisher

Elsevier BV

Subject

Cancer Research,Radiology, Nuclear Medicine and imaging,Oncology,Radiation

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PCG-net: feature adaptive deep learning for automated head and neck organs-at-risk segmentation;Frontiers in Oncology;2023-10-20

2. A clinical evaluation of the performance of five commercial artificial intelligence contouring systems for radiotherapy;Frontiers in Oncology;2023-08-04

3. Comparative parotid gland segmentation by using ResNet ‐18 and MobileNetV2 based DeepLab v3+ architectures from magnetic resonance images;Concurrency and Computation: Practice and Experience;2022-10-17

4. Autodelineation of organ at risk in head and neck cancer radiotherapy using artificial intelligence;Journal of Cancer Research and Therapeutics;2022

5. Automated delineation of head and neck organs at risk using synthetic MRI‐aided mask scoring regional convolutional neural network;Medical Physics;2021-08-18

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