  We present an approach for fully automatic urinary bladder segmentation in CT
images with artificial neural networks in this study. Automatic medical image
analysis has become an invaluable tool in the different treatment stages of
diseases. Especially medical image segmentation plays a vital role, since
segmentation is often the initial step in an image analysis pipeline. Since
deep neural networks have made a large impact on the field of image processing
in the past years, we use two different deep learning architectures to segment
the urinary bladder. Both of these architectures are based on pre-trained
classification networks that are adapted to perform semantic segmentation.
Since deep neural networks require a large amount of training data,
specifically images and corresponding ground truth labels, we furthermore
propose a method to generate such a suitable training data set from Positron
Emission Tomography/Computed Tomography image data. This is done by applying
thresholding to the Positron Emission Tomography data for obtaining a ground
truth and by utilizing data augmentation to enlarge the dataset. In this study,
we discuss the influence of data augmentation on the segmentation results, and
compare and evaluate the proposed architectures in terms of qualitative and
quantitative segmentation performance. The results presented in this study
allow concluding that deep neural networks can be considered a promising
approach to segment the urinary bladder in CT images.
