Research Article
Eff-Unet for Trachea Segmentation on CT Scans
@INPROCEEDINGS{10.1007/978-3-031-60665-6_10, author={Arthur Guilherme Santos Fernandes and Geraldo Braz Junior and Jo\"{a}o Ot\^{a}vio Bandeira Diniz and Marcos Melo Ferreira and Jos\^{e} Ribamar Durand Rodrigues Junior and Mackele Lourrane Jurema Da Silva and Lucas Ara\^{u}jo Gon\`{e}alves}, title={Eff-Unet for Trachea Segmentation on CT Scans}, proceedings={Wireless Mobile Communication and Healthcare. 12th EAI International Conference, MobiHealth 2023, Vila Real, Portugal, November 29-30, 2023 Proceedings}, proceedings_a={MOBIHEALTH}, year={2024}, month={6}, keywords={Deep Learning Semantic Segmentation Radiotherapy Fully Convolutional Neural Networks Organs at Risk}, doi={10.1007/978-3-031-60665-6_10} }- Arthur Guilherme Santos Fernandes
Geraldo Braz Junior
João Otávio Bandeira Diniz
Marcos Melo Ferreira
José Ribamar Durand Rodrigues Junior
Mackele Lourrane Jurema Da Silva
Lucas Araújo Gonçalves
Year: 2024
Eff-Unet for Trachea Segmentation on CT Scans
MOBIHEALTH
Springer
DOI: 10.1007/978-3-031-60665-6_10
Abstract
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.
