Automated Pre-delineation of CTV in Patients with Cervical Cancer Using Dense V-Net / 中国医疗器械杂志
Chinese Journal of Medical Instrumentation
;
(6): 409-414, 2020.
Article
in Chinese
| WPRIM
| ID: wpr-942751
ABSTRACT
We use a dense and fully connected convolutional network with good feature learning in small samples, to automatically pre-deline CTV of cervical cancer patients based on CT images and evaluate the effect. The CT data of stage IB and IIA postoperative cervical cancer with similar delineation scope were selected to be used to evaluate the pre-sketching accuracy from three aspectssketching similarity, sketching offset and sketching volume difference. It has been proved that the 8 most representative parameters are superior to those with single network and reported internationally before. Dense V-Net can accurately predict CTV pre-delineation of cervical cancer patients, which can be used clinically after simple modification by doctors.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Patients
/
Automation
/
Tomography, X-Ray Computed
/
Uterine Cervical Neoplasms
/
Machine Learning
Limits:
Female
/
Humans
Language:
Chinese
Journal:
Chinese Journal of Medical Instrumentation
Year:
2020
Type:
Article
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