EVC-EVC2 complex stability and ciliary targeting are regulated by modification with ubiquitin and SUMO.

Ellis van Creveld syndrome and Weyers acrofacial dysostosis are two rare genetic diseases affecting skeletal development. They are both ciliopathies, as they are due to malfunction of primary cilia, microtubule-based plasma membrane protrusions that function as cellular antennae and are required for Hedgehog signaling, a key pathway during skeletal morphogenesis. These ciliopathies are caused by mutations affecting the EVC-EVC2 complex, a transmembrane protein heterodimer that regulates Hedgehog signaling from inside primary cilia.

Ultrasound-Based Phenotyping of Lateral Ventricles to Predict Hydrocephalus Outcome in Premature Neonates.

Objective: Prediction of post-hemorrhagic hydrocephalus (PHH) outcome-i.e., whether it requires intervention or not-in premature neonates using cranial ultrasound (CUS) images is challenging.

Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models.

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes.

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.

Computational anatomy for multi-organ analysis in medical imaging: A review.

The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy.

Ventricular shape evaluation on early ultrasound predicts post-hemorrhagic hydrocephalus.

Background: To compare the ability of ventricular morphology on cranial ultrasound (CUS) versus standard clinical variables to predict the need for temporizing cerebrospinal fluid drainage in newborns with intraventricular hemorrhage (IVH).

Method: This is a retrospective study of newborns (gestational age <29 weeks) diagnosed with IVH. Clinical variables known to increase the risk for post-hemorrhagic hydrocephalus were collected.

Automatic Segmentation of Neonatal Ventricles from Cranial Ultrasound for Prediction of Intraventricular Hemorrhage Outcome.

Intraventricular hemorrhage (IVH) followed by post hemorrhagic hydrocephalus (PHH) in premature neonates is one of the recognized reasons of brain injury in newborns. Cranial ultrasound (CUS) is a noninvasive imaging tool that has been used widely to diagnose and monitor neonates with IVH. In our previous work, we showed the potential of quantitative morphological analysis of lateral ventricles from early CUS to predict the PHH outcome in neonates with IVH.

Fetal Skull Reconstruction via Deep Convolutional Autoencoders.

Ultrasound (US) imaging is arguably the most commonly used modality for fetal screening. Recently, 3DUS has been progressively adopted in modern obstetric practice, showing promising diagnosis capabilities, and alleviating many of the inherent limitations of traditional 2DUS, such as subjectivity and operator dependence. However, the involuntary movements of the fetus, and the difficulty for the operator to inspect the entire volume in real-time can hinder the acquisition of the entire region of interest.

Fast Multiple Landmark Localisation Using a Patch-based Iterative Network.

We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn the spatial relationship between an image patch and anatomical landmark positions. During inference, patches are repeatedly passed to the CNN until the estimated landmark position converges to the true landmark location.

Cranial ultrasound-based prediction of post hemorrhagic hydrocephalus outcome in premature neonates with intraventricular hemorrhage.

Premature neonates with intraventricular hemorrhage (IVH) followed by post hemorrhagic hydrocephalus (PHH) are at high risk for brain injury. Cranial ultrasound (CUS) is used for monitoring of premature neonates during the first weeks after birth to identify IVH and follow the progression to PHH. However, the lack of a standardized method for CUS evaluation has led to significant variability in decision making regarding treatment.

Design of patient-specific concentric tube robots using path planning from 3-D ultrasound.

Percutaneous techniques and robot-assisted surgical systems have enabled minimally invasive procedures that offer reduced scarring, recovery time, and complications compared to traditional open surgeries. Despite these improvements, access to diseased sites using the standard, straight needle-based percutaneous techniques is still limited for certain procedures due to intervening tissues. These limitations can be further exacerbated in specific patient groups, particularly pediatric patients, whose anatomy does not fit the traditional tools and systems.

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.

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.

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.

Quantification of kidneys from 3D ultrasound in pediatric hydronephrosis.

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.

Quantitative Ultrasound for Measuring Obstructive Severity in Children with Hydronephrosis.

Purpose: We define sonographic biomarkers for hydronephrotic renal units that can predict the necessity of diuretic nuclear renography.

Materials And Methods: 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.

Automatic multi-resolution shape modeling of multi-organ structures.

Point Distribution Models (PDM) are among the most popular shape description techniques and their usefulness has been demonstrated in a wide variety of medical imaging applications. However, to adequately characterize the underlying modeled population it is essential to have a representative number of training samples, which is not always possible. This problem is especially relevant as the complexity of the modeled structure increases, being the modeling of ensembles of multiple 3D organs one of the most challenging cases.

Generalized multiresolution hierarchical shape models via automatic landmark clusterization.

Point Distribution Models (PDM) are some of the most popular shape description techniques in medical imaging. However, to create an accurate shape model it is essential to have a representative sample of the underlying population, which is often challenging. This problem is particularly relevant as the dimensionality of the modeled structures increases, and becomes critical when dealing with complex 3D shapes.

Multiresolution hierarchical shape models in 3D subcortical brain structures.

Point distribution models (PDM) are one of the most extended methods to characterize the underlying population of set of samples, whose usefulness has been demonstrated in a wide variety of applications, including medical imaging. However, one important issue remains unsolved: the large number of training samples required. This problem becomes critical as the complexity of the problem increases, and the modeling of 3D multiobjects/organs represents one of the most challenging cases.

Hierarchical statistical shape models of multiobject anatomical structures: application to brain MRI.

The accurate segmentation of subcortical brain structures in magnetic resonance (MR) images is of crucial importance in the interdisciplinary field of medical imaging. Although statistical approaches such as active shape models (ASMs) have proven to be particularly useful in the modeling of multiobject shapes, they are inefficient when facing challenging problems. Based on the wavelet transform, the fully generic multiresolution framework presented in this paper allows us to decompose the interobject relationships into different levels of detail.