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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Proceedings (International Symposium on Biomedical Imaging) s. 434 - 437
Hlavní autori: Yuan, Yixuan, Qin, Wenjian, Guo, Xiaoqing, Buyyounouski, Mark, Hancock, Steve, Han, Bin, Xing, Lei
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.04.2019
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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 variations. To deal with this problem, we proposed a novel Encoder-Decoder Densely Connected Convolutional Network (ED-DenseNet) to segment prostate region automatically. Our model consists of two interconnected pathways, a dense encoder pathway, which learns discriminative high-level image features and a dense decoder pathway, which predicts the final segmentation in the pixel level. Instead of using the convolutional network as the basic unit in the encoder-decoder framework, we utilize Densely Connected Convolutional Network (DenseNet) to preserve the maximum information flow among layers by a densely-connected mechanism. In addition, a novel loss function that jointly considers the encoder-decoder reconstruction error and the prediction error is proposed to optimize the feature learning and segmentation result. Our automatic segmentation result shows high agreement (DSC 87.14%) to the clinical segmentation results by experienced radiation oncologists. In addition, comparison with state-of-the-art methods shows that our ED-DenseNet model is superior in segmentation performance.
ISSN:1945-8452
DOI:10.1109/ISBI.2019.8759498