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Automatic Tumor Segmentation with Deep Convolutional Neural Networks for Radiotherapy Applications

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Abstract

Accurate tumor delineation in medical images is of great importance in guiding radiotherapy. In nasopharyngeal carcinoma (NPC), due to its high variability, low contrast and discontinuous boundaries in magnetic resonance images (MRI), the margin of the tumor is especially difficult to be identified, making the radiotherapy planning a more challenging problem. The objective of this paper is to develop an automatic segmentation method of NPC in MRI for radiosurgery applications. To this end, we present to segment NPC using a deep convolutional neural network. Specifically, to obtain spatial consistency as well as accurate feature details for segmentation, multiple convolution kernel sizes are employed. The network contains a large number of trainable parameters which capture the relationship between the MRI intensity images and the corresponding label maps. When trained on subjects with pre-labeled MRI, the network can estimate the label class of each voxel for the testing subject which is only given the intensity image. To demonstrate the segmentation performance, we carry on our method on the T1-weighted images of 15 NPC patients, and compare the segmentation results against the radiologist’s reference outline. Experimental results show that the proposed method outperforms the traditional hand-crafted features based segmentation methods. The presented method in this paper could be useful for NPC diagnosis and helpful for guiding radiotherapy.

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Acknowledgements

This work was supported in part by NSFC61701324, Science and Technology Department of Sichuan Province 2016JZ0014 and Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (No. MJUKF201715).

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Authors and Affiliations

  1. College of Computer Science, Sichuan University, Chengdu, China

    Yan Wang, Zongqing Ma, Kun He & Jiliu Zhou

  2. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), Fuzhou, 350121, China

    Yan Wang

  3. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Chen Zu

  4. College of Electronic and Information Engineering, Sichuan University, Chengdu, China

    Guangliang Hu

  5. Department of Head & Neck and Mammary Oncology, West China Hospital, Sichuan University, Chengdu, China

    Yong Luo

  6. Department of Computer Science, Chengdu University of Information Technology, Chengdu, China

    Xi Wu & Jiliu Zhou

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  1. Yan Wang
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  2. Chen Zu
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  3. Guangliang Hu
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  7. Xi Wu
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  8. Jiliu Zhou
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Correspondence to Xi Wu.

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Wang, Y., Zu, C., Hu, G. et al. Automatic Tumor Segmentation with Deep Convolutional Neural Networks for Radiotherapy Applications. Neural Process Lett 48, 1323–1334 (2018). https://doi.org/10.1007/s11063-017-9759-3

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  • DOI: https://doi.org/10.1007/s11063-017-9759-3

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