Prostate Segmentation with Encoder-Decoder Densely Connected Convolutional Network (Ed-Densenet)

Prostate cancer is a leading cause of mortality among men. Prostate segmentation of Magnetic Resonance (MR) images plays a critical role in treatment planning and image guided interventions. However, manual delineation of prostate is very time-consuming and subjects to large inter-observer variation...

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Bibliographic Details

Published in:	Proceedings (International Symposium on Biomedical Imaging) pp. 434 - 437
Main Authors:	Yuan, Yixuan, Qin, Wenjian, Guo, Xiaoqing, Buyyounouski, Mark, Hancock, Steve, Han, Bin, Xing, Lei
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.04.2019
Subjects:	Decoding DenseNet Encoder-Deconder network Feature extraction Image reconstruction Image segmentation Magnetic resonance imaging Prostate cancer Prostate segmentation reconstruction error and prediction error Training
ISSN:	1945-8452
Online Access:	Get full text
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