Effect of Training Volume on the Automatic Segmentation of Clinical Target Volume and
Organs at Risk in Patients with Cervical Cancer Based on Deep Learning
HU Jing, CHEN Fei, GONG Xiaoqin, YOU Tao, ZHANG Kaijun, DAI Chunhua
Department of Radiation Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang Jiangsu 212000, China
Abstract：Objective To evaluate the effect of the amount of training data on the automatic segmentation of clinical target volume
(CTV) and organs at risk (OARs) of cervical cancer patients based on deep learning using 2D U-net. Methods CT images of 140
patients with cervical cancer in our hospital were enrolled. CT image data of 120 patients were randomly selected as deep learning
training set, and the rest of 20 cases were used as test set. The AccuLearning (AL) platform based on 2D U-net was used to train
these datasets and generate five groups of automatic segmentation models (the amount of training datasets was 15, 30, 60, 90, 120
cases respectively). Meanwhile the other 20 cases were selected for automatic segmentation test. Three evaluation indexes, including
the Dice similarity coefficient (DSC), Hausdorff distance (HD) and relative volume difference (RVD) were analyzed to compare the
difference of automatic segmentation among the five groups, thereby discussing the effect of the amount of training data on automatic
segmentation. Results DSC and RVD of CTV, the DSC, HD and RVD of bowel bag, the DSC of rectum and bladder and the HD of
left femoral head showed statistically significant differences among the five models with different number training cases (P<0.05).
Meanwhile these indexes showed a good trend of change with the increase of the number of training data. Conclusion When building
the automatic segmentation models of CTV and OARs of patients with cervical cancer based on deep learning, 90 cases of training
data can be selected for CTV, 60 cases for bowel bag and rectum, 15 cases for bladder, marrow and bilateral femoral head.
胡静，陈飞，龚筱钦，游涛，张开军，戴春华. 训练集病例数对基于深度学习宫颈癌临床靶区及危及器官自动勾画的影响[J]. 中国医疗设备, 2022, 37(9): 33-37.
HU Jing, CHEN Fei, GONG Xiaoqin, YOU Tao, ZHANG Kaijun, DAI Chunhua. Effect of Training Volume on the Automatic Segmentation of Clinical Target Volume and
Organs at Risk in Patients with Cervical Cancer Based on Deep Learning. China Medical Devices, 2022, 37(9): 33-37.
