Early Detection of Ureteropelvic Junction Obstruction Using Signal Analysis and Machine Learning: A Dynamic Solution to a Dynamic Problem.

PURPOSE: We sought to define features that describe the dynamic information in diuresis renograms for the early detection of clinically significant hydronephrosis caused by ureteropelvic junction obstruction. MATERIALS AND METHODS: We studied the diuresis renogram of 55 patients with a mean ± SD age of 75 ± 66 days who had congenital hydronephrosis at initial presentation. Five patients had bilaterally affected kidneys for a total of 60 diuresis renograms. Surgery was performed on 35 kidneys. We extracted 45 features based on curve shape and wavelet analysis from the drainage curves recorded after furosemide administration. The optimal features were selected as the combination that maximized the ROC AUC obtained from a linear support vector machine classifier trained to classify patients as with or without obstruction. Using these optimal features we performed leave 1 out cross validation to estimate the accuracy, sensitivity and specificity of our framework. Results were compared to those obtained using post-diuresis drainage half-time and the percent of clearance after 30 minutes. RESULTS: Our framework had 93% accuracy, including 91% sensitivity and 96% specificity, to predict surgical cases. This was a significant improvement over the same accuracy of 82%, including 71% sensitivity and 96% specificity obtained from half-time and 30-minute clearance using the optimal thresholds of 24.57 minutes and 55.77%, respectively. CONCLUSIONS: Our machine learning framework significantly improved the diagnostic accuracy of clinically significant hydronephrosis compared to half-time and 30-minute clearance. This aids in the clinical decision making process by offering a tool for earlier detection of severe cases and it has the potential to reduce the number of diuresis renograms required for diagnosis.

22q11.2 deletion syndrome in diverse populations.

22q11.2 deletion syndrome (22q11.2 DS) is the most common microdeletion syndrome and is underdiagnosed in diverse populations. This syndrome has a variable phenotype and affects multiple systems, making early recognition imperative. In this study, individuals from diverse populations with 22q11.2 DS were evaluated clinically and by facial analysis technology. Clinical information from 106 individuals and images from 101 were collected from individuals with 22q11.2 DS from 11 countries; average age was 11.7 and 47% were male. Individuals were grouped into categories of African descent (African), Asian, and Latin American. We found that the phenotype of 22q11.2 DS varied across population groups. Only two findings, congenital heart disease and learning problems, were found in greater than 50% of participants. When comparing the clinical features of 22q11.2 DS in each population, the proportion of individuals within each clinical category was statistically different except for learning problems and ear anomalies (P < 0.05). However, when Africans were removed from analysis, six additional clinical features were found to be independent of ethnicity (P ≥ 0.05). Using facial analysis technology, we compared 156 Caucasians, Africans, Asians, and Latin American individuals with 22q11.2 DS with 156 age and gender matched controls and found that sensitivity and specificity were greater than 96% for all populations. In summary, we present the varied findings from global populations with 22q11.2 DS and demonstrate how facial analysis technology can assist clinicians in making accurate 22q11.2 DS diagnoses. This work will assist in earlier detection and in increasing recognition of 22q11.2 DS throughout the world.

A Generic Approach to Lung Field Segmentation From Chest Radiographs Using Deep Space and Shape Learning.

Computer-aided diagnosis (CAD) techniques for lung field segmentation from chest radiographs (CXR) have been proposed for adult cohorts, but rarely for pediatric subjects. Statistical shape models (SSMs), the workhorse of most state-of-the-art CXR-based lung field segmentation methods, do not efficiently accommodate shape variation of the lung field during the pediatric developmental stages. The main contributions of our work are: 1) a generic lung field segmentation framework from CXR accommodating large shape variation for adult and pediatric cohorts; 2) a deep representation learning detection mechanism, ensemble space learning, for robust object localization; and 3) marginal shape deep learning for the shape deformation parameter estimation. Unlike the iterative approach of conventional SSMs, the proposed shape learning mechanism transforms the parameter space into marginal subspaces that are solvable efficiently using the recursive representation learning mechanism. Furthermore, our method is the first to include the challenging retro-cardiac region in the CXR-based lung segmentation for accurate lung capacity estimation. The framework is evaluated on 668 CXRs of patients between 3 month to 89 year of age. We obtain a mean Dice similarity coefficient of 0.96 ±0.03 (including the retro-cardiac region). For a given accuracy, the proposed approach is also found to be faster than conventional SSM-based iterative segmentation methods. The computational simplicity of the proposed generic framework could be similarly applied to the fast segmentation of other deformable objects.

Liver segmentation from low-radiation-dose pediatric computed tomography using patient-specific, statistical modeling.

PURPOSE: The pediatric computed tomography (CT) volume is acquired at a low dose because radiation is harmful to young children. Consequently, the pediatric CT volume has lower signal-to-noise ratio, which makes organ segmentation difficult. In this paper, we propose a liver segmentation algorithm for pediatric CT scan using a patient-specific level set distribution model (LSDM). METHODS: The patient-specific LSDM was constructed using a conditional LSDM (C-LSDM) conditioned on age. Furthermore, a patient-specific probabilistic atlas (PA) was generated using the model, which became a priori to the maximum a posteriori-based segmentation. The patient-specific PA generation by the C-LSDM using kernel density estimation was quicker than the conventional PA generation method using random numbers, and also, it was more accurate as it did not include any approximations. RESULTS: The liver segmentation algorithm was tested on 42 CT volumes of children aged between 2 weeks and 7 years. In the proposed method, the calculation time of the PA was about 9 s for the single Gaussian method, while it was 337 s for the conventional PA generation method using random numbers. Furthermore, using the kernel density estimation, median and 25%/75% tile of the generalized Dice similarity index between the PA and the correct liver region were found to be 0.3443 and 0.3191/0.3595. The Dice similarity index in the segmentation was 0.8821 and 0.8545/0.9085, which are higher than those obtained by the conventional method, and requires lower computational cost. CONCLUSION: We proposed a method to quickly and accurately generate a PA, combined with C-LSDM using kernel density estimation, which enabled efficient calculation and improved segmentation accuracy.

Marginal Shape Deep Learning: Applications to Pediatric Lung Field Segmentation.

Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, localization, and texture classification in various medical imaging modalities. However, DL applications to segmentation of objects especially to deformable objects are rather limited and mostly restricted to pixel classification. In this work, we propose marginal shape deep learning (MaShDL), a framework that extends the application of DL to deformable shape segmentation by using deep classifiers to estimate the shape parameters. MaShDL combines the strength of statistical shape models with the automated feature learning architecture of DL. Unlike the iterative shape parameters estimation approach of classical shape models that often leads to a local minima, the proposed framework is robust to local minima optimization and illumination changes. Furthermore, since the direct application of DL framework to a multi-parameter estimation problem results in a very high complexity, our framework provides an excellent run-time performance solution by independently learning shape parameter classifiers in marginal eigenspaces in the decreasing order of variation. We evaluated MaShDL for segmenting the lung field from 314 normal and abnormal pediatric chest radiographs and obtained a mean Dice similarity coefficient of 0.927 using only the four highest modes of variation (compared to 0.888 with classical ASM1 (p-value=0.01) using same configuration). To the best of our knowledge this is the first demonstration of using DL framework for parametrized shape learning for the delineation of deformable objects.

Renal Segmentation From 3D Ultrasound via Fuzzy Appearance Models and Patient-Specific Alpha Shapes.

Ultrasound (US) imaging is the primary imaging modality for pediatric hydronephrosis, which manifests as the dilation of the renal collecting system (CS). In this paper, we present a new framework for the segmentation of renal structures, kidney and CS, from 3DUS scans. First, the kidney is segmented using an active shape model-based approach, tailored to deal with the challenges raised by US images. A weighted statistical shape model allows to compensate the image variation with the propagation direction of the US wavefront. The model is completed with a new fuzzy appearance model and a multi-scale omnidirectional Gabor-based appearance descriptor. Next, the CS is segmented using an active contour formulation, which combines contour- and intensity-based terms. The new positive alpha detector presented here allows to control the propagation process by means of a patient-specific stopping function created from the bands of adipose tissue within the kidney. The performance of the new segmentation approach was evaluated on a dataset of 39 cases, showing an average Dice's coefficient of 0.86±0.05 for the kidney, and 0.74 ± 0.10 for the CS segmentation, respectively. These promising results demonstrate the potential utility of this framework for the US-based assessment of the severity of pediatric hydronephrosis.

Deep Learning Guided Partitioned Shape Model for Anterior Visual Pathway Segmentation.

Analysis of cranial nerve systems, such as the anterior visual pathway (AVP), from MRI sequences is challenging due to their thin long architecture, structural variations along the path, and low contrast with adjacent anatomic structures. Segmentation of a pathologic AVP (e.g., with low-grade gliomas) poses additional challenges. In this work, we propose a fully automated partitioned shape model segmentation mechanism for AVP steered by multiple MRI sequences and deep learning features. Employing deep learning feature representation, this framework presents a joint partitioned statistical shape model able to deal with healthy and pathological AVP. The deep learning assistance is particularly useful in the poor contrast regions, such as optic tracts and pathological areas. Our main contributions are: 1) a fast and robust shape localization method using conditional space deep learning, 2) a volumetric multiscale curvelet transform-based intensity normalization method for robust statistical model, and 3) optimally partitioned statistical shape and appearance models based on regional shape variations for greater local flexibility. Our method was evaluated on MRI sequences obtained from 165 pediatric subjects. A mean Dice similarity coefficient of 0.779 was obtained for the segmentation of the entire AVP (optic nerve only =0.791 ) using the leave-one-out validation. Results demonstrated that the proposed localized shape and sparse appearance-based learning approach significantly outperforms current state-of-the-art segmentation approaches and is as robust as the manual segmentation.

Quantitative MRI criteria for optic pathway enlargement in neurofibromatosis type 1.

OBJECTIVE: To determine quantitative size thresholds for enlargement of the optic nerve, chiasm, and tract in children with neurofibromatosis type 1 (NF1). METHODS: Children 0.5-18.6 years of age who underwent high-resolution T1-weighted MRI were eligible for inclusion. This consisted of children with NF1 with or without optic pathway gliomas (OPGs) and a control group who did not have other acquired, systemic, or genetic conditions that could alter their anterior visual pathway (AVP). Maximum and average diameter and volume of AVP structures were calculated from reconstructed MRI images. Values above the 95th percentile from the controls were considered the threshold for defining an abnormally large AVP measure. RESULTS: A total of 186 children (controls = 82; NF1noOPG = 54; NF1+OPG = 50) met inclusion criteria. NF1noOPG and NF1+OPG participants demonstrated greater maximum optic nerve diameter and volume, optic chiasm volume, and total brain volume compared to controls (p < 0.05, all comparisons). Total brain volume, rather than age, predicted optic nerve and chiasm volume in controls (p < 0.05). Applying the 95th percentile threshold to all NF1 participants, the maximum optic nerve diameter (3.9 mm) and AVP volumes resulted in few false-positive errors (specificity >80%, all comparisons). CONCLUSIONS: Quantitative reference values for AVP enlargement will enhance the development of objective diagnostic criteria for OPGs secondary to NF1.