Kidney Segmentation in 3-D 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 contour of the kidney. 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-modified phase-based detection approach is applied. The proposed framework was tested in 15 3D ultrasound images. The method showed to be accurate and feasible in very challenging poor-quality ultrasound images, achieving a Dice overlap of approximately 81% and an average point-to-surface error of 2.4 mm. Moreover, a validation set composed of 24 additional images of healthy kidneys and 8 additional images of pathologic ones was evaluated to corroborate the accuracy of the method. The obtained results demonstrated the potential of the proposed method to be used in clinical practice.

Artificial intelligence-aided ultrasound in renal diseases: a systematic review

“…Seven studies focused on the innovation and efficiency of kidney segmentation algorithms ( 3 , 12 - 17 ). Wang et al proposed a two-step level set method for segmentation of both the kidney boundary (distance regularized level set evolution) and kidney collecting system (region-scalable fitting energy minimization) to determine the renal parenchymal area, and the SN score evaluated using ten cases was >0.9 ( 12 ).…”

“…Seven studies focused on the innovation and efficiency of kidney segmentation algorithms (3,(12)(13)(14)(15)(16)(17). Wang et al proposed a two-step level set method for segmentation of both the kidney boundary (distance regularized level set evolution) and kidney collecting system (region-scalable and anatomical knowledge, to propose a detecting and segmenting method in 3D kidney ultrasound image segmentation.…”

Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images

“…Their findings showed that precise automatic segmentation of the kidneys and their interior components is possible. Similarly, several published studies used ultrasound imaging to segment kidneys [ 32 , 34 , 35 , 36 , 37 , 38 ], and a few detected kidney stones [ 39 ].…”

3D segmentation of the left atrial appendage in computed tomography for planning of transcatheter occlusion

“…cardiac wall and blood pool). The edge map was calculated through a signed phase-based filter targeting white to black transitions [9,13]. Therefore, in each iteration, the contour is updated by computing 𝑔̅ (𝒙 * ) as:…”