Abstract：
PURPOSE:Precise segmentation of clinical target volume (CTV) is the key to stomach cancer radiotherapy. We proposed a novel stochastic width - deep neural network (SW-DNN) for better automatically contouring stomach CTV METHODS: SW-DNN was an end-to-end approach, of which the core component was a novel SW mechanism that employed shortcut connections between the encoder and decoder in a random manner, and thus the width of the SW-DNN was stochastically adjustable to obtain improved segmentation results. 150 stomach cancer patient CT cases with the corresponding CTV labels were collected and used to train and evaluate the SW-DNN. Three common quantitative measures: true positive volume fraction (TPVF), positive predictive value (PPV) and dice similarity coefficient (DSC) were used to evaluate the segmentation accuracy. RESULTS:CTVs calculated by SW-DNN had significant quantitative advantages over three state-of-the-art methods. The average DSC value of SW-DNN was 2.1%, 2.8% and 3.6% higher than that of three state-of-the-art methods. The average DSC, TPVF and PPV values of SW-DNN was 2.1%,4.0% and 0.3% higher than that of the corresponding constant width DNN. CONCLUSIONS:SW-DNN provided better performance for contouring stomach cancer CTV accurately and efficiently. It is a promising solution in clinical radiotherapy planning for stomach cancer.
journal_name
Med Physjournal_title
Medical physicsauthors
Xu L,Hu J,Song Y,Bai S,Yi Zdoi
10.1002/mp.14733subject
Has Abstractpub_date
2021-01-27 00:00:00eissn
0094-2405issn
2473-4209pub_type
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