Author(s): Torres, Helena R. ; Queirós, Sandro ; Morais, Pedro ; Oliveira, Bruno ; Gomes-Fonseca, João ; Mota, Paulo ; Lima, Estevão ; D'hooge, Jan ; Fonseca, Jaime C. ; Vilaça, João L.
Date: 2020
Persistent ID: http://hdl.handle.net/11110/1998
Origin: CiencIPCA
Subject(s): B-spline Explicit Actives Surfaces; feature detection; kidney segmentation; 3D ultrasound
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.
This work was funded by project “NORTE-01-0145-FEDER-000045”, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). This project was also funded by national funds (PIDDAC), through the FCT – Fundação para a Ciência e Tecnologia and FCT/MCTES under the scope of the project UIDB/05549/2020 and UIDP/05549/2020. The authors also acknowledge support from FCT and the European Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018, SFRH/BD/136721/2018, and PD/BDE/113597/2015.
