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KODYM, O. ŠPANĚL, M. HEROUT, A.
Original Title
Segmentation of Head and Neck Organs at Risk Using CNN with Batch Dice Loss
Type
conference paper
Language
English
Original 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
Authors
KODYM, O.; ŠPANĚL, M.; HEROUT, A.
Released
20. 7. 2018
Publisher
Springer International Publishing
Location
Stuttgart
ISBN
978-3-030-12938-5
Book
Pattern Recognition, 40th German Conference, GCPR 2018 Proceedings
Edition
LNCS, volume 11269
0302-9743
Periodical
Lecture Notes in Computer Science
Year of study
2018
Number
11269
State
Federal Republic of Germany
Pages from
105
Pages to
114
Pages count
9
BibTex
@inproceedings{BUT155013, author="Oldřich {Kodym} and Michal {Španěl} and Adam {Herout}", title="Segmentation of Head and Neck Organs at Risk Using CNN with Batch Dice Loss", booktitle="Pattern Recognition, 40th German Conference, GCPR 2018 Proceedings", year="2018", series="LNCS, volume 11269", journal="Lecture Notes in Computer Science", volume="2018", number="11269", pages="105--114", publisher="Springer International Publishing", address="Stuttgart", doi="10.1007/978-3-030-12939-2\{_}8", isbn="978-3-030-12938-5", issn="0302-9743" }