Nested Graph Cut for Automatic Segmentation of High-Frequency Ultrasound Images of the Mouse Embryo

Abstract

We propose a fully automatic segmentation method called nested graph cut to segment images (2D or 3D) that contain multiple objects with a nested structure. Compared to other graph-cut-based methods developed for multiple regions, our method can work well for nested objects without requiring manual selection of initial seeds, even if different objects have similar intensity distributions and some object boundaries are missing. Promising results were obtained for separating the brain ventricles, the head, and the uterus region in the mouse-embryo head images obtained using high-frequency ultrasound imaging. The proposed method achieved mean Dice similarity coefficients of 0.87 ±0.04 and 0.89 ±0.06 for segmenting BVs and the head, respectively, compared to manual segmentation results by experts on 40 3D images over five gestation stages.

Automatic body localization and brain ventricle segmentation in 3D high frequency ultrasound images of mouse embryos

“…With the detected ROI that only contains a set of objects satisfying a recursive containment relationship with alternating dark and bright intensities, we now can apply NGC [3] to segment the ROI into four objects (i.e. BVs, body, fluid, and uterine wall).…”

“…As shown in these examples, a loss of ultrasound signal due to either specular reflection or shadowing from overlaying tissues may lead to the loss of boundary contrast and, therefore, BVs and the amniotic fluid regions may contact each other in the HFU images. In order to tackle this issue and leverage the nested structure of different regions in the head region, an automatic segmentation method known as nested graph cut (NGC) has been successfully developed for the segmented HFU images of the mouse embryonic head in a prior work [3]. The NGC works very well in the head images (manually trimmed) because these images contain only nested objects with alternating dark and bright intensities (Fig.…”

“…In this paper, we propose a novel fully automatic and robust segmentation framework for segmenting both the body and the BVs, shown in Figure 2. First, the input image is pre-processed with an adaptive intensity mapping function and anisotropic filtering to enhance contrast and reduce noise (for details, please refer to [3]). Then a ROI containing the region inside the uterus is located by sub-surface analysis.…”

Segmentation of 3-D High-Frequency Ultrasound Images of Human Lymph Nodes Using Graph Cut With Energy Functional Adapted to Local Intensity Distribution

“…The white region contains LNP and fat, and the PBS is labeled in black. The details of NGC can be found in [10].…”

COVID-19 Lesion Segmentation using Lung CT Scan Images: Comparative Study based on Active Contour Models

“…Currently, there are several segmentation algorithms in the literature that showed accurate and robust segmentation results. Generally, they can be classified into four categories, the clustering based 2,3 , the graph-cut-based 4,5 , neural-network-based 6,7 and the active-contour-based methods [8][9][10] . The clustering-based methods use clustering algorithms such as Kmeans and fuzzy C-means based on the assumption that each pixel can be assigned to the pixels belonging to the same class since they have specific distribution.…”