Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT
This work addresses a domain-specific problem for medical imaging researchers and clinicians by providing a feasible automated approach for cartilage analysis in CT, though it is incremental as it adapts existing neural network techniques to a less common imaging modality.
The paper tackled the problem of automating knee cartilage segmentation in contrast-enhanced CT images to reduce the time investment of manual methods, achieving an average recall of 0.69 compared to manual segmentations.
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.