Lower jawbone data generation for deep learning tools under MeVisLab

Birgit Pfarrkirchner; Christina Gsaxner; Lydia Lindner; Norbert Jakse; Jürgen Wallner; Dieter Schmalstieg; Jan Egger

doi:10.1117/12.2292708

12 March 2018 Lower jawbone data generation for deep learning tools under MeVisLab

Birgit Pfarrkirchner; Christina Gsaxner; Lydia Lindner; Norbert Jakse; Jürgen Wallner; Dieter Schmalstieg; Jan Egger

Author Affiliations +

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105782O (2018) https://doi.org/10.1117/12.2292708
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

In this contribution, the preparation of data for training deep learning networks that are used to segment the lower jawbone in computed tomography (CT) images is proposed. To train a neural network, we had initially only ten CT datasets of the head-neck region with a diverse number of image slices from the clinical routine of a maxillofacial surgery department. In these cases, facial surgeons segmented the lower jawbone in each image slice to generate the ground truth for the segmentation task. Since the number of present images was deemed insufficient to train a deep neural network efficiently, the data was augmented with geometric transformations and added noise. Flipping, rotating and scaling images as well as the addition of various noise types (uniform, Gaussian and salt-and-pepper) were connected within a global macro module under MeVisLab. Our macro module can prepare the data for general deep learning data in an automatic and flexible way. Augmentation methods for segmentation tasks can easily be incorporated.

Citation Download Citation

Birgit Pfarrkirchner, Christina Gsaxner, Lydia Lindner, Norbert Jakse, Jürgen Wallner, Dieter Schmalstieg, Jan Egger, "Lower jawbone data generation for deep learning tools under MeVisLab", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105782O (12 March 2018); doi: 10.1117/12.2292708; https://doi.org/10.1117/12.2292708

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