Kidney segmentation in three-dimensional ultrasound images using a fast phase-based approach

Abstract

Renal ultrasound imaging is the primary imaging modality for the assessment of the kidney’s condition and is essential for diagnosis, treatment and surgical intervention planning, and follow-up. In this regard, kidney delineation in three-dimensional ultrasound images represents a relevant and challenging task in clinical practice. In this paper, a novel framework is proposed to accurately segment the kidney in 3D ultrasound images. The proposed framework can be divided into two stages: 1) initialization of the segmentation method; and 2) kidney segmentation. Within the initialization stage, a phase-based feature detection method is used to detect edge points at kidney boundaries, from which the segmentation is automatically initialized. In the segmentation stage, the B-Spline Explicit Active Surface framework is adapted to obtain the final kidney contour. Here, a novel hybrid energy functional that combines localized region-based and edge-based terms is used during segmentation. For the edge term, a fast signed phase-based detection approach is applied. The proposed framework was validated in two distinct datasets: (1) 15 3D challenging poor-quality ultrasound images used for experimental development, parameters assessment, and evaluation; and (2) 42 3D ultrasound images (both healthy and pathologic kidneys) used to unbiasedly assess its accuracy. Overall, the proposed method achieved a Dice overlap around 81% and an average point-to-surface error of ~2.8 mm. These results demonstrate the potential of the proposed method for clinical usage.