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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| Published in: | 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) pp. 1 - 4 |
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IEEE
01.04.2020
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| Abstract | 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 convolutional neural network (CNN) architecture, which adopts the encoder-decoder design, to automatically segment the OARs in thoracic CT images. In our method, hybrid dilated convolution (HDC) is employed to enlarge the receptive field of the encoder part, and a pyramid backbone with lateral connections between encoder and decoder is utilized to capture contextual information at multiple scales. To reduce the false-positive segmentation results, we use the multi-task learning strategy to add an auxiliary classifier branch to the network. The experiments demonstrate that the proposed method outperforms other state-of-the-art models and the results have a good consistency with that of experienced radiologists. |
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| AbstractList | 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 convolutional neural network (CNN) architecture, which adopts the encoder-decoder design, to automatically segment the OARs in thoracic CT images. In our method, hybrid dilated convolution (HDC) is employed to enlarge the receptive field of the encoder part, and a pyramid backbone with lateral connections between encoder and decoder is utilized to capture contextual information at multiple scales. To reduce the false-positive segmentation results, we use the multi-task learning strategy to add an auxiliary classifier branch to the network. The experiments demonstrate that the proposed method outperforms other state-of-the-art models and the results have a good consistency with that of experienced radiologists. |
| Author | Zhang, Hongyan Hao, Xiaoyu Shi, Jun Wen, Ke An, Hong Xue, Xudong |
| Author_xml | – sequence: 1 givenname: Jun surname: Shi fullname: Shi, Jun organization: University of Science and Technology of China,School of Computer Science and Technology,Hefei,China – sequence: 2 givenname: Ke surname: Wen fullname: Wen, Ke organization: University of Science and Technology of China,School of Computer Science and Technology,Hefei,China – sequence: 3 givenname: Xiaoyu surname: Hao fullname: Hao, Xiaoyu organization: University of Science and Technology of China,School of Computer Science and Technology,Hefei,China – sequence: 4 givenname: Xudong surname: Xue fullname: Xue, Xudong organization: University of Science and Technology of China,The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine,Hefei,China – sequence: 5 givenname: Hong surname: An fullname: An, Hong organization: University of Science and Technology of China,School of Computer Science and Technology,Hefei,China – sequence: 6 givenname: Hongyan surname: Zhang fullname: Zhang, Hongyan organization: University of Science and Technology of China,School of Computer Science and Technology,Hefei,China |
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| Snippet | Accurate segmentation of organs at risk (OARs) in computed tomography (CT) is an essential step in radiation therapy for thoracic cancer treatment. However,... |
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| SubjectTerms | 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 |
| Title | A Novel U-Like Network For The Segmentation Of Thoracic Organs |
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