AUTOMATIC BODY LOCALIZATION AND BRAIN VENTRICLE SEGMENTATION IN 3D HIGH FREQUENCY ULTRASOUND IMAGES OF MOUSE EMBRYOS.

This paper presents a fully automatic segmentation system for whole-body high-frequency ultrasound (HFU) images of mouse embryos that can simultaneously segment the body contour and the brain ventricles (BVs). Our system first locates a region of interest (ROI), which covers the interior of the uterus, by sub-surface analysis. Then, it segments the ROI into BVs, the body, the amniotic fluid, and the uterine wall, using nested graph cut. Simultaneously multilevel thresholding is applied to the whole-body image to propose candidate BV components. These candidates are further truncated by the embryo mask (body+BVs) to refine the BV candidates. Finally, subsets of all candidate BVs are compared with pre-trained spring models describing valid BV structures, to identify true BV components. The system can segment the body accurately in most cases based on visual inspection, and achieves average Dice similarity coefficient of 0.8924 ± 0.043 for the BVs on 36 HFU image volumes.

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

Previous studies by our group have shown that 3-D high-frequency quantitative ultrasound (QUS) methods have the potential to differentiate metastatic lymph nodes (LNs) from cancer-free LNs dissected from human cancer patients. To successfully perform these methods inside the LN parenchyma (LNP), an automatic segmentation method is highly desired to exclude the surrounding thin layer of fat from QUS processing and accurately correct for ultrasound attenuation. In high-frequency ultrasound images of LNs, the intensity distribution of LNP and fat varies spatially because of acoustic attenuation and focusing effects. Thus, the intensity contrast between two object regions (e.g., LNP and fat) is also spatially varying. In our previous work, nested graph cut (GC) demonstrated its ability to simultaneously segment LNP, fat, and the outer phosphate-buffered saline bath even when some boundaries are lost because of acoustic attenuation and focusing effects. This paper describes a novel approach called GC with locally adaptive energy to further deal with spatially varying distributions of LNP and fat caused by inhomogeneous acoustic attenuation. The proposed method achieved Dice similarity coefficients of 0.937±0.035 when compared with expert manual segmentation on a representative data set consisting of 115 3-D LN images obtained from colorectal cancer patients.

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

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.

Classification of breast tumors using elastographic and B-mode features: comparison of automatic selection of representative slice and physician-selected slice of images.

Inter-observer variability and image quality are two key factors that can affect the diagnostic performance of elastography and B-mode ultrasound for breast tumor characterization. The purpose of this study is to use an image quantification method that automatically chooses a representative slice and then segments the tumor contour to evaluate the diagnostic features for tumor characterization. First, the representative slice is selected based on either the stiffness inside the tumor (the signal-to-noise ratio on the elastogram [SNRe]) or the contrast between the tumor and the surrounding normal tissue (the contrast-to-noise ratio on the elastogram [CNRe]). Next, the level set method is used to segment the tumor contour. Finally, the B-mode and elastographic features related to the segmented tumor are extracted for tumor characterization. The performance of the representative slice selected using the proposed methods is compared to that of the physician-selected slice in 151 biopsy-proven lesions (89 benign and 62 malignant). The diagnostic accuracies using elastographic features are 82.1% (124/151) for the slice with the maximum CNRe value, 82.1% (124/151) for the slice with the maximum SNRe value and 82.8% (125/151) for the physician-selected slice, whereas the diagnostic accuracies using B-mode features are 80.8% (122/151) for the slice with the maximum CNRe value, 87.4% (132/151) for the slice with the maximum SNRe value and 84.1% (127/151) for the physician-selected slice. When using both the B-mode and elastographic features to characterize the tumor, the accuracy of diagnosis is 86.1% (130/151) for the slice with the maximum CNRe value, 90.1% (136/151) for the slice with the maximum SNRe value and 89.4% (135/151) for the physician-selected slice. Our results show that the representative slice selected by SNRe and CNRe could be used to reduce the observer variability and to increase the diagnostic performance by the B-mode and elastographic features.