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1 October 2018 Evaluation of deep learning methods for parotid gland segmentation from CT images
Annika Hänsch, Michael Schwier, Tobias Gass, Tomasz Morgas, Benjamin Haas, Volker Dicken, Hans Meine, Jan Klein, Horst K. Hahn
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Journal of Medical Imaging, Vol. 6, Issue 1, 011005 (October 2018). https://doi.org/10.1117/1.JMI.6.1.011005
Abstract
The segmentation of organs at risk is a crucial and time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and low contrast to surrounding structures, segmenting the parotid gland is challenging. Motivated by the recent success of deep learning, we study the use of two-dimensional (2-D), 2-D ensemble, and three-dimensional (3-D) U-Nets for segmentation. The mean Dice similarity to ground truth is ∼0.83 for all three models. A patch-based approach for class balancing seems promising for false-positive reduction. The 2-D ensemble and 3-D U-Net are applied to the test data of the 2015 MICCAI challenge on head and neck autosegmentation. Both deep learning methods generalize well onto independent data (Dice 0.865 and 0.88) and are superior to a selection of model- and atlas-based methods with respect to the Dice coefficient. Since appropriate reference annotations are essential for training but often difficult and expensive to obtain, it is important to know how many samples are needed for training. We evaluate the performance after training with different-sized training sets and observe no significant increase in the Dice coefficient for more than 250 training cases.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Annika Hänsch, Michael Schwier, Tobias Gass, Tomasz Morgas, Benjamin Haas, Volker Dicken, Hans Meine, Jan Klein, and Horst K. Hahn "Evaluation of deep learning methods for parotid gland segmentation from CT images," Journal of Medical Imaging 6(1), 011005 (1 October 2018). https://doi.org/10.1117/1.JMI.6.1.011005
Received: 31 May 2018; Accepted: 31 August 2018; Published: 1 October 2018
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Cited by 33 scholarly publications.
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KEYWORDS
Image segmentation
Neural networks
Computed tomography
Head
Neck
3D modeling
Data modeling
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