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Deep convolutional neural networks for automatic segmentation of thoracic organs-at-risk in radiation oncology - use of non-domain transfer learning.
Journal of Applied Clinical Medical Physics ( IF 2.1 ) Pub Date : 2020-06-29 , DOI: 10.1002/acm2.12871 Charles C Vu 1, 2 , Zaid A Siddiqui 1, 2 , Leonid Zamdborg 1, 2 , Andrew B Thompson 1, 2 , Thomas J Quinn 1, 2 , Edward Castillo 2 , Thomas M Guerrero 1, 2
Journal of Applied Clinical Medical Physics ( IF 2.1 ) Pub Date : 2020-06-29 , DOI: 10.1002/acm2.12871 Charles C Vu 1, 2 , Zaid A Siddiqui 1, 2 , Leonid Zamdborg 1, 2 , Andrew B Thompson 1, 2 , Thomas J Quinn 1, 2 , Edward Castillo 2 , Thomas M Guerrero 1, 2
Affiliation
Segmentation of organs‐at‐risk (OARs) is an essential component of the radiation oncology workflow. Commonly segmented thoracic OARs include the heart, esophagus, spinal cord, and lungs. This study evaluated a convolutional neural network (CNN) for automatic segmentation of these OARs.
中文翻译:
深度卷积神经网络用于放射肿瘤学中处于危险中的胸腔器官的自动分割-使用非域转移学习。
危险器官分割(OARs)是放射肿瘤学工作流程的重要组成部分。通常分割的胸腔OAR包括心脏,食道,脊髓和肺。这项研究评估了卷积神经网络(CNN)对这些OAR的自动分割。
更新日期:2020-06-29
中文翻译:
深度卷积神经网络用于放射肿瘤学中处于危险中的胸腔器官的自动分割-使用非域转移学习。
危险器官分割(OARs)是放射肿瘤学工作流程的重要组成部分。通常分割的胸腔OAR包括心脏,食道,脊髓和肺。这项研究评估了卷积神经网络(CNN)对这些OAR的自动分割。