Automatic delineation of rectal cancer target volume and organs at risk based on convolutional neural network / 中华放射肿瘤学杂志
Chinese Journal of Radiation Oncology
;
(6): 374-377, 2020.
Article
in Chinese
| WPRIM
| ID: wpr-868610
ABSTRACT
Objective:
To realize automatic delineation of rectal cancer target volume and normal tissues and improve clinical work efficiency.Methods:
The deep learning method based on convolutional neural network was adopted to construct neural network, learn and realize automatic delineation, and compare the differences between automatic delineation and manual delineation.Results:
Two hundred and ten cases with rectal cancer were randomly assigned to a training set of 190 and a validation set of 20. The complete delineation of a single case took about 10s; the average Dice of CTV was 0.87±0.04; the average Dice of other normal tissues was bigger than 0.8; the Hausdorff distance (HD) index of CTV was 25.33±16.05; the mean distance to agreement (MDA) index was 3.07±1.49, and the Jaccard similarity coefficient (JSC) index was 0.77±0.07.Conclusion:
The deep learning method based on full convolutional neural network can realize the automatic delineation of rectal cancer target volume and improve work efficiency.
Full text:
Available
Index:
WPRIM (Western Pacific)
Type of study:
Etiology study
/
Practice guideline
Language:
Chinese
Journal:
Chinese Journal of Radiation Oncology
Year:
2020
Type:
Article
Similar
MEDLINE
...
LILACS
LIS