Deep ensemble optimized models for probabilistic CTV breast segmentation
Claudio Fiorino
IntroductionTo optimize radiotherapy treatment and minimize toxicities, effective segmentation of organs-at-risk (OARs) and clinical target volume (CTV) is essential. Deep learning (DL) models can achieve high segmentation accuracy through careful tuning. However, their reliability also hinges on addressing uncertainties stemming from variability in clinical contouring practices. This study systematically evaluates six advanced DL models for automatic CTV segmentation in whole-breast radiotherap