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Analyzing knee cartilage thickness and strain under load can help to furtherthe understanding of the effects of diseases like Osteoarthritis. A precisesegmentation 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, wherecontrast agent visualizes the border between femoral and tibial cartilage. Toovercome the main drawback of manual segmentation, namely its high timeinvestment, we propose to use a 3D Convolutional Neural Network for this task.The presented architecture consists of a V-Net with SeLu activation, and aTversky loss function. Due to the high imbalance between very few cartilagepixels and many background pixels, a high false positive rate is to beexpected. To reduce this rate, the two largest segmented point clouds areextracted using a connected component analysis, since they most likelyrepresent the medial and lateral tibial cartilage surfaces. The resultingsegmentations are compared to manual segmentations, and achieve on average arecall of 0.69, which confirms the feasibility of this approach.

Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT