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Technical Note: More accurate and efficient segmentation of organs‐at‐risk in radiotherapy with convolutional neural networks cascades
Author(s) -
Men Kuo,
Geng Huaizhi,
Cheng Chingyun,
Zhong Haoyu,
Huang Mi,
Fan Yong,
Plastaras John P.,
Lin Alexander,
Xiao Ying
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13296
Subject(s) - segmentation , convolutional neural network , hausdorff distance , artificial intelligence , computer science , pattern recognition (psychology) , region of interest , sørensen–dice coefficient , artificial neural network , deep learning , similarity (geometry) , image segmentation , image (mathematics)
Purpose Manual delineation of organs‐at‐risk ( OAR s) in radiotherapy is both time‐consuming and subjective. Automated and more accurate segmentation is of the utmost importance in clinical application. The purpose of this study is to further improve the segmentation accuracy and efficiency with a novel network named convolutional neural networks ( CNN ) Cascades. Methods CNN Cascades was a two‐step, coarse‐to‐fine approach that consisted of a simple region detector ( SRD ) and a fine segmentation unit ( FSU ). The SRD first used a relative shallow network to define the region of interest ( ROI ) where the organ was located, and then, the FSU took the smaller ROI as input and adopted a deep network for fine segmentation. The imaging data (14,651 slices) of 100 head‐and‐neck patients with segmentations were used for this study. The performance was compared with the state‐of‐the‐art single CNN in terms of accuracy with metrics of Dice similarity coefficient ( DSC ) and Hausdorff distance ( HD ) values. Results The proposed CNN Cascades outperformed the single CNN on accuracy for each OAR . Similarly, for the average of all OAR s, it was also the best with mean DSC of 0.90 ( SRD : 0.86, FSU : 0.87, and U‐Net: 0.85) and the mean HD of 3.0 mm ( SRD : 4.0, FSU : 3.6, and U‐Net: 4.4). Meanwhile, the CNN Cascades reduced the mean segmentation time per patient by 48% ( FSU ) and 5% (U‐Net), respectively. Conclusions The proposed two‐step network demonstrated superior performance by reducing the input region. This potentially can be an effective segmentation method that provides accurate and consistent delineation with reduced clinician interventions for clinical applications as well as for quality assurance of a multicenter clinical trial.