Fast kidney detection and segmentation with learned kernel convolution and model deformation in 3D ultrasound images

We present a method to segment kidneys in 3D ultrasound images. The main challenges are the high variability in kidney appearance, the frequent presence of artifacts (shadows, speckle noise, etc.) and a strong constraint on computation time for clinical acceptance (less than 10 seconds). Our algorithm leverages a database of 480 3D images through a support vector machine(SVM)-based detection algorithm followed by a model-based deformation technique. Since severe pathologies induce strong deformations of kidneys, the proposed method encompasses intuitive interaction functions allowing the user to refine the result with a few clicks. Validation has been performed by learning on 120 cases and testing on 360; a perfect segmentation was reached automatically in 50% of the cases, and in 90% of the cases in less than 3 clicks.