  Analyzing knee cartilage thickness and strain under load can help to further
the understanding of the effects of diseases like Osteoarthritis. A precise
segmentation of the cartilage is a necessary prerequisite for this analysis.
This segmentation task has mainly been addressed in Magnetic Resonance Imaging,
and was rarely investigated on contrast-enhanced Computed Tomography, where
contrast agent visualizes the border between femoral and tibial cartilage. To
overcome the main drawback of manual segmentation, namely its high time
investment, we propose to use a 3D Convolutional Neural Network for this task.
The presented architecture consists of a V-Net with SeLu activation, and a
Tversky loss function. Due to the high imbalance between very few cartilage
pixels and many background pixels, a high false positive rate is to be
expected. To reduce this rate, the two largest segmented point clouds are
extracted using a connected component analysis, since they most likely
represent the medial and lateral tibial cartilage surfaces. The resulting
segmentations are compared to manual segmentations, and achieve on average a
recall of 0.69, which confirms the feasibility of this approach.
