Deep learning model for automatic contouring of cardiovascular substructures on radiotherapy planning CT images: Dosimetric validation and reader study based clinical acceptability testing

•Accurate automatic contouring is essential for cardiac radiotoxicity research.•Developed residual U-net model achieves high geometric and dosimetric accuracy.•Model validated in multi-institutional NSCLC dataset.•Model robust against adjacency of tumors to contoured structures.•Average clinical acc...

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Veröffentlicht in:	Radiotherapy and oncology 2021-12, Vol.165, p.52-59
Hauptverfasser:	Garrett Fernandes, Miguel, Bussink, Johan, Stam, Barbara, Wijsman, Robin, Schinagl, Dominic A.X., Monshouwer, René, Teuwen, Jonas
Format:	Artikel
Sprache:	eng
Schlagworte:	Automatic cardiac contouring Carcinoma, Non-Small-Cell Lung Cardiac radiotoxicity Deep Learning Humans Lung cancer Lung Neoplasms NSCLC Organs at Risk Radiotherapy Planning, Computer-Assisted Segmentation Surface dice Tomography, X-Ray Computed
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