A Novel U-Like Network For The Segmentation Of Thoracic Organs

Accurate segmentation of organs at risk (OARs) in computed tomography (CT) is an essential step in radiation therapy for thoracic cancer treatment. However, the manual segmentation of OARs is time-consuming and subject to inter-observer variation. In this paper, we propose a novel U-like deep convol...

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Veröffentlicht in:	2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) S. 1 - 4
Hauptverfasser:	Shi, Jun, Wen, Ke, Hao, Xiaoyu, Xue, Xudong, An, Hong, Zhang, Hongyan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.04.2020
Schlagworte:	Biomedical imaging Computed tomography Convolution convolutional neural network (CNN) Decoding hybrid dilated convolution Image segmentation Lung multi-task learning OARs segmentation radiation therapy Two dimensional displays
Online-Zugang:	Volltext
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