Lower jawbone data generation for deep learning tools under MeVisLab

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.