  Convolutional neural networks have been applied to a wide variety of computer
vision tasks. Recent advances in semantic segmentation have enabled their
application to medical image segmentation. While most CNNs use two-dimensional
kernels, recent CNN-based publications on medical image segmentation featured
three-dimensional kernels, allowing full access to the three-dimensional
structure of medical images. Though closely related to semantic segmentation,
medical image segmentation includes specific challenges that need to be
addressed, such as the scarcity of labelled data, the high class imbalance
found in the ground truth and the high memory demand of three-dimensional
images. In this work, a CNN-based method with three-dimensional filters is
demonstrated and applied to hand and brain MRI. Two modifications to an
existing CNN architecture are discussed, along with methods on addressing the
aforementioned challenges. While most of the existing literature on medical
image segmentation focuses on soft tissue and the major organs, this work is
validated on data both from the central nervous system as well as the bones of
the hand.
