CNN-based Segmentation of Medical Imaging Data

Baris Kayalibay, Grady Jensen, Patrick van der Smagt

Abstract:

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.