Representation learning through deep learning (DL) architecture has shown tremendous potential for identification, local-
ization, 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.
Hydronephrosis is the most common abnormal finding in pediatric urology. Thanks to its non-ionizing nature, ultrasound (US) imaging is the preferred diagnostic modality for the evaluation of the kidney and the urinary track. However, due to the lack of correlation of US with renal function, further invasive and/or ionizing studies might be required (e.g., diuretic renograms). This paper presents a computer-aided diagnosis (CAD) tool for the accurate and objective assessment of pediatric hydronephrosis based on morphological analysis of kidney from 3DUS scans. The integration of specific segmentation tools in the system, allows to delineate the relevant renal structures from 3DUS scans of the patients with minimal user interaction, and the automatic computation of 90 anatomical features. Using the washout half time (T1/2) as indicative of renal obstruction, an optimal subset of predictive features is selected to differentiate, with maximum sensitivity, those severe cases where further attention is required (e.g., in the form of diuretic renograms), from the noncritical ones. The performance of this new 3DUS-based CAD system is studied for two clinically relevant T1/2 thresholds, 20 and 30 min. Using a dataset of 20 hydronephrotic cases, pilot experiments show how the system outperforms previous 2D implementations by successfully identifying all the critical cases (100% of sensitivity), and detecting up to 100% (T1/2 = 20 min) and 67% (T1/2 = 30 min) of non-critical ones for T1/2 thresholds of 20 and 30 min, respectively.
The use of magnetic resonance enterography (MRE) has become a mainstay in the evaluation, assessment and follow up of inflammatory bowel diseases, such as Crohn’s disease (CD), thanks to its high image quality and its non-ionizing nature. In particular, the advent of faster MRE sequences less sensitive to image-motion artifacts offers the possibility to obtain visual, structural and functional information of the patient’s small bowel. However, the inherent subjectivity of the mere visual inspection of these images often hinders the accurate identification and monitoring of the pathological areas. In this paper, we present a framework that provides quantitative and objective motility information of the small bowel from free-breathing MRE dynamic sequences. After compensating for the breathing motion of the patient, we create personalized peristaltic activity maps via optical flow analysis. The result is the creation of a new set of images providing objective and precise functional information of the small bowel. The accuracy of the new method was also evaluated from two different perspectives: objective accuracy (1.1 ± 0.6 mm/s of error), i.e., the ability of the system to provide quantitative and accurate information about the motility of moving bowel landmarks, and subjective accuracy (avg. difference of 0.7 ± 0.7 in a range of 1 to 5), i.e., the degree of agreement with the subjective evaluation of an expert. Finally, the practical utility of the new method was successfully evaluated in a preliminary study with 32 studies of healthy and CD cases, showing its potential for the fast and accurate assessment and follow up of CD in the small bowel.
Ultrasound (US) tissue characterization provides valuable information for the initialization of automatic segmentation algorithms, and can further provide complementary information for diagnosis of pathologies. US tissue characterization is challenging due to the presence of various types of image artifacts and dependence on the sonographer’s skills. One way of overcoming this challenge is by characterizing images based on the distribution of the backscatter data derived from the interaction between US waves and tissue. The goal of this work is to classify liver versus kidney tissue in 3D volumetric US data using the distribution of backscatter US data recovered from end-user displayed Bmode image available in clinical systems. To this end, we first propose the computation of a large set of features based on the homodyned-K distribution of the speckle as well as the correlation coefficients between small patches in 3D images. We then utilize the random forests framework to select the most important features for classification. Experiments on in-vivo 3D US data from nine pediatric patients with hydronephrosis showed an average accuracy of 94% for the classification of liver and kidney tissues showing a good potential of this work to assist in the classification and segmentation of abdominal soft tissue.
Ultrasound is the mainstay of imaging for pediatric hydronephrosis, though its potential as diagnostic tool is limited by its subjective assessment, and lack of correlation with renal function. Therefore, all cases showing signs of hydronephrosis undergo further invasive studies, like diuretic renogram, in order to assess the actual renal function. Under the hypothesis that renal morphology is correlated with renal function, a new ultrasound based computer-aided diagnosis (CAD) tool for pediatric hydronephrosis is presented. From 2D ultrasound, a novel set of morphological features of the renal collecting systems and the parenchyma, is automatically extracted using image analysis techniques. From the original set of features, including size, geometric and curvature descriptors, a subset of ten features are selected as predictive variables, combining a feature selection technique and area under the curve filtering. Using the washout half time (T1/2) as indicative of renal obstruction, two groups are defined. Those cases whose T1/2 is above 30 minutes are considered to be severe, while the rest would be in the safety zone, where diuretic renography could be avoided. Two different classification techniques are evaluated (logistic regression, and support vector machines). Adjusting the probability decision thresholds to operate at the point of maximum sensitivity, i.e., preventing any severe case be misclassified, specificities of 53%, and 75% are achieved, for the logistic regression and the support vector machine classifier, respectively. The proposed CAD system allows to establish a link between non-invasive non-ionizing imaging techniques and renal function, limiting the need for invasive and ionizing diuretic renography.