Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer.

ABSTRACT:

Background and purpose

Automatic segmentation methods have greatly changed the RadioTherapy (RT) workflow, but still need to be extended to target volumes. In this paper, Deep Learning (DL) models were compared for Gross Tumor Volume (GTV) segmentation in locally advanced cervical cancer, and a novel investigation into failure detection was introduced by utilizing radiomic features.

Methods and materials

We trained eight DL models (UNet, VNet, SegResNet, SegResNetVAE) for 2D and 3D segmentation. Ensembling individually trained models during cross-validation generated the final segmentation. To detect failures, binary classifiers were trained using radiomic features extracted from segmented GTVs as inputs, aiming to classify contours based on whether their Dice Similarity Coe

SUBMITTER: Rouhi R 

PROVIDER: S-EPMC11192799 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature