RefineNet‐based 2D and 3D automatic segmentations for clinical target volume and organs at risks for patients with cervical cancer in postoperative radiotherapy
Authors
Xiao, Chengjian
Jin, Juebin
Yi, Jinling
Han, Ce
Zhou, Yongqiang
Ai, Yao
Xie, Congying
Jin, Xiance
Issue Date
2022-05-09
Type
Article
Language
en_US
Keywords
Automatic Segmentation , Cervical Cancer , Clinical Target Volume , Deep Learning , Organs at Risk
Alternative Title
Abstract
Purpose:
An accurate and reliable target volume delineation is critical for the safe and successful radiotherapy. The purpose of this study is to develop new 2D and 3D automatic segmentation models based on RefineNet for clinical target volume (CTV) and organs at risk (OARs) for postoperative cervical cancer based on computed tomography (CT) images.
Methods:
A 2D RefineNet and 3D RefineNetPlus3D were adapted and built to automatically segment CTVs and OARs on a total of 44 222 CT slices of 313 patients with stage I–III cervical cancer. Fully convolutional networks (FCNs), U‐Net, context encoder network (CE‐Net), UNet3D, and ResUNet3D were also trained and tested with randomly divided training and validation sets, respectively. The performances of these automatic segmentation models were evaluated by Dice similarity coefficient (DSC), Jaccard similarity coefficient, and average symmetric surface distance when comparing them with manual segmentations with the test data.
Results:
The DSC for RefineNet, FCN, U‐Net, CE‐Net, UNet3D, ResUNet3D, and RefineNet3D were 0.82, 0.80, 0.82, 0.81, 0.80, 0.81, and 0.82 with a mean contouring time of 3.2, 3.4, 8.2, 3.9, 9.8, 11.4, and 6.4 s, respectively. The generated RefineNetPlus3D demonstrated a good performance in the automatic segmentation of bladder, small intestine, rectum, right and left femoral heads with a DSC of 0.97, 0.95, 091, 0.98, and 0.98, respectively, with a mean computation time of 6.6 s.
Conclusions:
The newly adapted RefineNet and developed RefineNetPlus3D were promising automatic segmentation models with accurate and clinically acceptable CTV and OARs for cervical cancer patients in postoperative radiotherapy.
Description
Citation
Xiao, C., Jin, J., Yi, J., Han, C., Zhou, Y., Ai, Y., Xie, C., & Jin, X. (2022). RefineNet-based 2D and 3D automatic segmentations for clinical target volume and organs at risks for patients with cervical cancer in postoperative radiotherapy. Journal of applied clinical medical physics, 23(7), e13631. https://doi.org/10.1002/acm2.13631
Publisher
Journal of Applied Clinical Medical Physics