Generalizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT images

This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at risk (OAR)-the esophagus, trachea, heart, and aorta-in CT images in the context of radiotherapy planning. We compare a multi-organ segmentation approach and the fusion of multip...

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Veröffentlicht in:	European radiology 2024-12
Hauptverfasser:	Guérendel, Corentin, Petrychenko, Liliana, Chupetlovska, Kalina, Bodalal, Zuhir, Beets-Tan, Regina G H, Benson, Sean
Format:	Artikel
Sprache:	eng
Online-Zugang:	Volltext
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