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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| Veröffentlicht in: | Proceedings (International Symposium on Biomedical Imaging) S. 434 - 437 |
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| Sprache: | Englisch |
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IEEE
01.04.2019
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| ISSN: | 1945-8452 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Qin, Wenjian Hancock, Steve Yuan, Yixuan Guo, Xiaoqing Buyyounouski, Mark Han, Bin Xing, Lei |
| Author_xml | – sequence: 1 givenname: Yixuan surname: Yuan fullname: Yuan, Yixuan organization: Department of Electronic Engineering, City University of Hong Kong, Hong Kong – sequence: 2 givenname: Wenjian surname: Qin fullname: Qin, Wenjian organization: Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, China – sequence: 3 givenname: Xiaoqing surname: Guo fullname: Guo, Xiaoqing organization: Department of Electronic Engineering, City University of Hong Kong, Hong Kong – sequence: 4 givenname: Mark surname: Buyyounouski fullname: Buyyounouski, Mark organization: Department of Radiation Oncology, Stanford University, Stanford, 94305, US – sequence: 5 givenname: Steve surname: Hancock fullname: Hancock, Steve organization: Department of Radiation Oncology, Stanford University, Stanford, 94305, US – sequence: 6 givenname: Bin surname: Han fullname: Han, Bin organization: Department of Radiation Oncology, Stanford University, Stanford, 94305, US – sequence: 7 givenname: Lei surname: Xing fullname: Xing, Lei organization: Department of Radiation Oncology, Stanford University, Stanford, 94305, US |
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| Snippet | Prostate cancer is a leading cause of mortality among men. Prostate segmentation of Magnetic Resonance (MR) images plays a critical role in treatment planning... |
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| SubjectTerms | Decoding DenseNet Encoder-Deconder network Feature extraction Image reconstruction Image segmentation Magnetic resonance imaging Prostate cancer Prostate segmentation reconstruction error and prediction error Training |
| Title | Prostate Segmentation with Encoder-Decoder Densely Connected Convolutional Network (Ed-Densenet) |
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