Renal Segmentation From 3D Ultrasound via Fuzzy Appearance Models and Patient-Specific Alpha Shapes.

Ultrasound (US) imaging is the primary imaging modality for pediatric hydronephrosis, which manifests as the dilation of the renal collecting system (CS). In this paper, we present a new framework for the segmentation of renal structures, kidney and CS, from 3DUS scans. First, the kidney is segmented using an active shape model-based approach, tailored to deal with the challenges raised by US images. A weighted statistical shape model allows to compensate the image variation with the propagation direction of the US wavefront. The model is completed with a new fuzzy appearance model and a multi-scale omnidirectional Gabor-based appearance descriptor. Next, the CS is segmented using an active contour formulation, which combines contour- and intensity-based terms. The new positive alpha detector presented here allows to control the propagation process by means of a patient-specific stopping function created from the bands of adipose tissue within the kidney. The performance of the new segmentation approach was evaluated on a dataset of 39 cases, showing an average Dice's coefficient of 0.86±0.05 for the kidney, and 0.74 ± 0.10 for the CS segmentation, respectively. These promising results demonstrate the potential utility of this framework for the US-based assessment of the severity of pediatric hydronephrosis.