Ontology highlight
ABSTRACT: Background
Epithelial ovarian cancer (EOC) segmentation is an indispensable step in assessing the extent of disease and guiding the treatment plan that follows. Currently, manual segmentation is the most commonly used method, despite it being tedious, time-consuming and subject to inter- and intra-observer variability. This study aims to assess the feasibility of deep learning methods in the automatic segmentation of EOC on T2-weighted magnetic resonance images.Methods
A total of 339 EOC patients from eight different clinical centers were enrolled and divided into 4 groups: training set (n=154), validation set (n=25), internal test set (n=50) and external test set (n=110). Six common-used algorithms, including convolutional neural networks (CNNs) (U-Net, DeepLabv3, U-Net++ and PSPNet) and transformers (TransUnet and Swin-Unet), were used to conduct automatic segmentations. The performances of these automatic segmentation methods were evaluated by means of dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetric surface distance (ASSD), precision and recall.Results
All the results look promising, which demonstrates the feasibility of using deep learning for EOC segmentation. Overall, CNNs and transformers showed similar performances in both internal and external test sets. Among all the models, U-Net++ performed best with a DSC, HD, ASSD, precision and recall of 0.851, 25.3, 1.75, 0.838, 0.882 and 0.740, 42.5, 4.21, 0.825, 0.725 in internal and external test sets, respectively.Conclusions
Fully automated segmentation of EOC is possible with deep learning. The segmentation performance is related to the International Federation of Gynecology and Obstetrics (FIGO) stages and histological types of EOC.
SUBMITTER: Hu D
PROVIDER: S-EPMC10006162 | biostudies-literature | 2023 Mar
REPOSITORIES: biostudies-literature
Hu Dingdu D Jian Junming J Li Yongai Y Gao Xin X
Quantitative imaging in medicine and surgery 20230209 3
<h4>Background</h4>Epithelial ovarian cancer (EOC) segmentation is an indispensable step in assessing the extent of disease and guiding the treatment plan that follows. Currently, manual segmentation is the most commonly used method, despite it being tedious, time-consuming and subject to inter- and intra-observer variability. This study aims to assess the feasibility of deep learning methods in the automatic segmentation of EOC on T2-weighted magnetic resonance images.<h4>Methods</h4>A total of ...[more]