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BACKGROUND The metopic suture is unlike other cranial sutures in that it normally closes in infancy. Consequently, the diagnosis of metopic synostosis depends primarily on a subjective assessment of cranial shape. The purpose of this study was to create a simple, reproducible radiographic method to quantify forehead shape and distinguish trigonocephaly from normal cranial shape variation. METHODS Computed tomography scans were acquired for 92 control patients (mean age, 4.2 ± 3.3 months) and 18 patients (mean age, 6.2 ± 3.3 months) with a diagnosis of metopic synostosis. A statistical model of the normal cranial shape was constructed, and deformation fields were calculated for patients with metopic synostosis. Optimal and divergence (simplified) interfrontal angles (IFA) were defined based on the three points of maximum average deformation on the frontal bones and metopic suture, respectively. Statistical analysis was performed to assess the accuracy and reliability of the diagnostic procedure. RESULTS The optimal interfrontal angle was found to be significantly different between the synostosis (116.5 ± 5.8 degrees; minimum, 106.8 degrees; maximum, 126.6 degrees) and control (136.7 ± 6.2 degrees; minimum, 123.8 degrees; maximum, 169.3 degrees) groups (p < 0.001). Divergence interfrontal angles were also significantly different between groups. Accuracy, in terms of available clinical diagnosis, for the optimal and divergent angles, was 0.981 and 0.954, respectively. CONCLUSIONS Cranial shape analysis provides an objective and extremely accurate measure by which to diagnose abnormal interfrontal narrowing, the hallmark of metopic synostosis. The simple planar angle measurement proposed is reproducible and accurate, and can eliminate diagnostic subjectivity in this disorder. CLINICAL QUESTION/LEVEL OF EVIDENCE Diagnostic, IV.

We define sonographic biomarkers for hydronephrotic renal units that can predict the necessity of diuretic nuclear renography.We selected a cohort of 50 consecutive patients with hydronephrosis of varying severity in whom 2-dimensional sonography and diuretic mercaptoacetyltriglycine renography had been performed. A total of 131 morphological parameters were computed using quantitative image analysis algorithms. Machine learning techniques were then applied to identify ultrasound based safety thresholds that agreed with the t½ for washout. A best fit model was then derived for each threshold level of t½ that would be clinically relevant at 20, 30 and 40 minutes. Receiver operating characteristic curve analysis was performed. Sensitivity, specificity and area under the receiver operating characteristic curve were determined. Improvement obtained by the quantitative imaging method compared to the Society for Fetal Urology grading system and the hydronephrosis index was statistically verified.For the 3 thresholds considered and at 100% sensitivity the specificities of the quantitative imaging method were 94%, 70% and 74%, respectively. Corresponding area under the receiver operating characteristic curve values were 0.98, 0.94 and 0.94, respectively. Improvement obtained by the quantitative imaging method over the Society for Fetal Urology grade and hydronephrosis index was statistically significant (p0.05 in all cases).Quantitative imaging analysis of renal sonograms in children with hydronephrosis can identify thresholds of clinically significant washout times with 100% sensitivity to decrease the number of diuretic renograms in up to 62% of children.

This paper introduces a complete framework for the quantification of renal structures (parenchyma, and collecting system) in 3D ultrasound (US) images. First, the segmentation of the kidney is performed using Gabor-based appearance models (GAM), a variant of the popular active shape models, properly tailored to the imaging physics of US image data. The framework also includes a new graph-cut based method for the segmentation of the collecting system, including brightness and contrast normalization, and positional prior information. The significant advantage (p = 0.03) of the new method over previous approaches in terms of segmentation accuracy has been successfully verified on clinical 3DUS data from pediatric cases with hydronephrosis. The promising results obtained in the estimation of the volumetric hydronephrosis index demonstrate the potential of our new framework to quantify anatomy in US and asses the severity of hydronephrosis.

This paper presents a new segmentation method for 3D ultrasound imag es of the pediatric kidney. Based on the popular active shape models, the algorithm is tailored to deal with the particular challenges raised by US images. First, a weighted statistical shape model allows to compensate the image variation with the propagat ion direction of the US wavefront. Second, an orientation correction approach is used to create a Gabor - based appearance model for each landmark at different scales. This multiscale characteristic is incorporated into the segmentation algorithm, creating a hierarchical approach where different appearance models are considered as the segmentation process evolves. The performance of the algorithm was evaluated on a dataset of 14 cases, both healthy and pathological, obtaining an average Dice's coefficient of 0.85, an average point -to-point distance of 4.07 mm, and 0.12 average relative volume difference. \ Index Terms ² Segmentation, Statistical Shape Model, Kidney, Hydronephrosis, Ultrasound. 1. INTRODUCTION Ultrasound (US) imaging is one of the most widely and conveniently used medical imaging methods. The non -ionizing and non -invasive properties of sonography, along with its real -time nature, safety, and relatively low cost, make US imaging especially useful in the pediatric population. Thus, renal US is one of the most common pediatric US studies, allowing to observe the state of the kidneys and the urinary tract quickly and safely. In particular, the most common abnormal finding in these studies is hydronep hrosis, the dilation of the renal pelvis and calyces due to obstruction of the urinary tract, affecting 2 -2.5% of children [1]. In this context, a n early diagnosis is very important in order to distinguish those kidneys that require surgery from those that do not. Additionally, the accurate parameterization and segmentation of the kidney anatomy plays an important role in the diagnosis of renal diseases [2], [3] and intervention planning. Although the quality of US images has increased in recent years, they still suffer from low signal -to-noise ratio, speckle, signal attenuation and dropout, and missing boundaries due to the orientation dependence of acquisition. These peculiarities make the detection of organs and object of interest form US images particula rly challenging, even when performed manually by a trained expert [4]. The improvement in image quality achieved in recent years has led to an increasing interest in developing new segmentation methods for sonographic images [5]. However, unlike other appl ication areas like echocardiography, or transrectal ultrasound (TRUS), kidney segmentation from renal US has received limited attention from the scientific community. To the best of our knowledge, only a few kidney segmentation approaches have already been reported [6] -[8], most of them focused exclusively on 2DUS. Xie et al [6] presented a 2D segmentation method based on texture and shape priors. From the set of features extracted by means of a Gabor filter bank, they create a texture model using an expect ation -maximization Gaussian -mixture approach, and a shape model to improve the robustness of the method. Though additional experiments with other type of images are provided, the validation of the method was limited. More recently, the work presented by Me ndoza et al. [8 ] takes into consideration the image formation process in US to combine the traditional Active Shape Models (ASM) [9] with a novel texture orientation correction. However, in spite of the promising results reported, the method is only app lied over 2D longitudinal kidney sections, which must be manually selected by an expert radiologist. Martin -Fernandez and Alberola -Lopez [7] proposed a segmentation approach for the kidney in 3DUS images using a probabilistic Bayesian method. However, the slices of the 3D volume are processed independently, trying to identify the kidney contour on each 2D image, and thus, loosing relevant information about the true 3D shape of the organ. Second, the significant user intervention required to adjust the templ ate reduces the automation and the reproducibility of the method. In this paper we present a new variant of the classic ASM, Gabor -based Appearance Models (GAM). This new approach addresses the inherent limitations of the original ASM when dealing with 3DU S renal images. First, a weighted statistical shape model correction compensates the image dependency with the propagation direction of the US wavefront. Second, a new multiscale Gabor based texture model is incorporated in the algorithm, reducing the spec kle noise effect and allowing to detect the kidney contours at 978-1-4673-1961-4/14/$31.00 ©2014 IEEE 633

Pectus excavatum (PE) is the most common major congenital deformity that involves the lower sternum and cartilages. Noncontrast CT is useful to assess the deformity of the bones and guide minimally invasive surgery. However, it has very poor visibility of cartilages even for the experienced clinicians who need to assess the 3D geometry of cartilages. In this study, we propose a novel method to estimate cartilages in noncontrast CT scans. The ribs and sternum are first segmented using region growing. The skeleton of the ribs is extracted and modeled by cosine series expansion. Then a statistical shape model is built with the cosine coefficients to estimate the cartilages as curves that connect the ribs and sternum. The results are refined by the cartilage surface that is approximated by contracting the skin surface to the bones. Leave-one-out validation was performed on 12 CT scans from healthy and PE subjects. The average distance between the estimated cartilages and ground truth is 1.53 mm. The promising results indicate that our method could estimate the costal cartilages in noncontrast CT effectively and assist to develop an imagebased surgical planning system for PE correction.

In this paper we present a segmentation method for 2D ultrasound images of the pediatric kidney. Our method relies on minimal user intervention and produces accurate segmentations thanks to a combination of improvements made to the Active Shape Model (ASM) framework. The initialization of the ASM module is based on a Covariance Matrix Adaptation Evolution Strategy (CMA-ES) genetic algorithm that optimizes the pose and the main shape variation modes of the kidney shape model. In order to account for the image formation process in ultrasound, the appearance model is obtained not according to the anatomically corresponding contour landmarks, but to those that exhibit a similar angle of incidence with respect to the wavefront traveling from the probe. The results indicate a median Dice's coefficient of 90.2% and a relative area difference of 10.8% for segmentation of a set of 80 kidney images.

We present a technique for the computational analysis of craniosynostosis from CT images. Our fully automatic methodology uses a statistical shape model to produce diagnostic features tailored to the anatomy of the subject. We propose a computational anatomy approach for measuring shape abnormality in terms of the closest case from a multi-atlas of normal cases. Although other authors have tackled malformation characterization for craniosynostosis in the past, our approach involves several novel contributions (automatic labeling of cranial regions via graph cuts, identification of the closest morphology to a subject using a multi-atlas of normal anatomy, detection of suture fusion, registration using masked regions and diagnosis via classification using quantitative measures of local shape and malformation). Using our automatic technique we obtained for each subject an index of cranial suture fusion, and deformation and curvature discrepancy averages across five cranial bones and six suture regions. Significant differences between normal and craniosynostotic cases were obtained using these characteristics. Machine learning achieved a 92.7% sensitivity and 98.9% specificity for diagnosing craniosynostosis automatically, values comparable to those achieved by trained radiologists. The probability of correctly classifying a new subject is 95.7%.

In this paper we present a segmentation method for ultrasound (US) images of the pediatric kidney, a difficult and barely studied problem. Our method segments the kidney on 2D sagittal US images and relies on minimal user intervention and a combination of improvements made to the Active Shape Model (ASM) framework. Our contributions include particle swarm initialization and profile training with rotation correction. We also introduce our methodology for segmentation of the kidney's collecting system (CS), based on graph-cuts (GC) with intensity and positional priors. Our intensity model corrects for intensity bias by comparison with other biased versions of the most similar kidneys in the training set. We prove significant improvements (p0.001) with respect to classic ASM and GC for kidney and CS segmentation, respectively. We use our semi-automatic method to compute the hydronephrosis index (HI) with an average error of 2.67 +/- 5.22 percentage points similar to the error of manual HI between different operators of 2.31 +/- 4.54 percentage points.