Eff-Unet for Trachea Segmentation on CT Scans

Organ at Risk segmentation has an important role in the meticulous planning of radiotherapy for cancer treatment. Its primary objective is to safeguard the surrounding healthy tissues while precisely directing radiation to target cancer cells. Currently, this task needs manual intervention by physicians, a process that can be time-consuming and susceptible to errors. Consequently, the integration of automatic segmentation methods offers the potential to accelerate the delineation of organs during radiotherapy planning. In this study, we applied Eff-Unet, a fully convolutional neural network model, and trained it to perform the semantic segmentation of trachea in computed tomography images. This approach yielded a noteworthy 78.9% dice score, underscoring its capability to enhance the efficiency and precision of organ segmentation during the radiotherapy planning process.