Automatic segmentation of prostate and organs at risk in CT images using an encoder–decoder structure based on residual neural network

Accurate segmentation of the prostate and surrounding organs at risk (OARs) from CT scans is critical for radiotherapy treatment planning in prostate cancer. However, manual segmentation is time-consuming and prone to variability. This paper proposes a deep learning-based approach using a pre-traine...

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Veröffentlicht in:	Biomedical signal processing and control Jg. 101; S. 107234
Hauptverfasser:	Gutiérrez-Ramos, Silvia M., Altuve, Miguel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.03.2025
Schlagworte:	Automatic segmentation Computed tomography imaging Deep learning Dice similarity coefficient Prostate cancer ResNet-18 Deep learning ResNet-18 Automatic segmentation Computed tomography imaging Prostate cancer Dice similarity coefficient
ISSN:	1746-8094
Online-Zugang:	Volltext
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