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Segmentation of Head and Neck Organs at Risk Using CNN with Batch Dice Loss

  • Oldřich KodymEmail author
  • Michal Španěl
  • Adam Herout
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11269)

Abstract

This paper deals with segmentation of organs at risk (OAR) in head and neck area in CT images which is a crucial step for reliable intensity modulated radiotherapy treatment. We introduce a convolution neural network with encoder-decoder architecture and a new loss function, the batch soft Dice loss function, used to train the network. The resulting model produces segmentations of every OAR in the public MICCAI 2015 Head And Neck Auto-Segmentation Challenge dataset. Despite the heavy class imbalance in the data, we improve accuracy of current state-of-the-art methods by 0.33 mm in terms of average surface distance and by 0.11 in terms of Dice overlap coefficient on average.

Keywords

Convolutional neural networks Computed Tomography Multi-label segmentation Head and neck radiotherapy 

Notes

Acknowledgements

This work was supported in part by the company TESCAN 3DIM (fka 3Dim Laboratory) and by the Technology Agency of the Czech Republic project TE01020415 (V3C – Visual Computing Competence Center).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Graph@FITBrno University of TechnologyBrnoCzech Republic
  2. 2.TESCAN 3DIMBrnoCzech Republic

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