Jessica Kishimoto

Automatic segmentation approach to extracting neonatal cerebral ventricles from 3D ultrasound images.

&NA; Preterm neonates with a very low birth weight of less than 1,500 g are at increased risk for developing intraventricular hemorrhage (IVH). Progressive ventricle dilatation of IVH patients may cause increased intracranial pressure, leading to neurological damage, such as neurodevelopmental delay and cerebral palsy. The technique of 3D ultrasound (US) imaging has been used to quantitatively monitor the ventricular volume in IVH neonates, which may elucidate the ambiguity surrounding the timing of interventions in these patients as 2D clinical US imaging relies on linear measurement and visual estimation of ventricular dilation from a series of 2D slices. To translate 3D US imaging into the clinical setting, a fully automated segmentation algorithm is necessary to extract the ventricular system from 3D neonatal brain US images. In this paper, an automatic segmentation approach is proposed to delineate lateral ventricles of preterm neonates from 3D US images. The proposed segmentation approach makes use of phase congruency map, multi‐atlas initialization technique, atlas selection strategy, and a multiphase geodesic level‐sets (MGLS) evolution combined with a spatial shape prior derived from multiple pre‐segmented atlases. Experimental results using 30 IVH patient images show that the proposed GPU‐implemented approach is accurate in terms of the Dice similarity coefficient (DSC), the mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). To the best of our knowledge, this paper reports the first study on automatic segmentation of the ventricular system of premature neonatal brains from 3D US images. HighlightsFirst study on automatic segmentation of neonatal ventricles from 3D US images.Multi‐atlas initialized multi‐region segmentation approach.Convex optimization based minimization of the defined energy function.Parallelized GPU implementation. Graphical abstract Figure. No caption available.

Development of a combined broadband near-infrared and diffusion correlation system for monitoring cerebral blood flow and oxidative metabolism in preterm infants.

Neonatal neuromonitoring is a major clinical focus of near-infrared spectroscopy (NIRS) and there is an increasing interest in measuring cerebral blood flow (CBF) and oxidative metabolism (CMRO2) in addition to the classic tissue oxygenation saturation (StO2). The purpose of this study was to assess the ability of broadband NIRS combined with diffusion correlation spectroscopy (DCS) to measured changes in StO2, CBF and CMRO2 in preterm infants undergoing pharmaceutical treatment of patent ductus arteriosus. CBF was measured by both DCS and contrast-enhanced NIRS for comparison. No significant difference in the treatment-induced CBF decrease was found between DCS (27.9 ± 2.2%) and NIRS (26.5 ± 4.3%). A reduction in StO2 (70.5 ± 2.4% to 63.7 ± 2.9%) was measured by broadband NIRS, reflecting the increase in oxygen extraction required to maintain CMRO2. This study demonstrates the applicability of broadband NIRS combined with DCS for neuromonitoring in this patient population.

Lateral Ventricle Segmentation of 3D Pre-term Neonates US Using Convex Optimization

Intraventricular hemorrhage (IVH) is a common disease among preterm infants with an occurrence of 12-20% in those born at less than 35 weeks gestational age. Neonates at risk of IVH are monitored by conventional 2D ultrasound (US) for hemorrhage and potential ventricular dilation. Compared to 2D US relying on linear measurements from a single slice and visually estimates to determine ventricular dilation, 3D US can provide volumetric ventricle measurements, more sensitive to longitudinal changes in ventricular volume. In this work, we propose a global optimization-based surface evolution approach to the segmentation of the lateral ventricles in preterm neonates with IVH. The proposed segmentation approach makes use of convex optimization technique in combination with a subject-specific shape model. We show that the introduced challenging combinatorial optimization problem can be solved globally by means of convex relaxation. In this regard, we propose a coupled continuous max-flow model, which derives a new and efficient dual based algorithm, that can be implemented on GPUs to achieve a high-performance in numerics. Experiments demonstrate the advantages of our approach in both accuracy and efficiency. To the best of our knowledge, this paper reports the first study on semi-automatic segmentation of lateral ventricles in neonates with IVH from 3D US images.

3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets.

Intraventricular hemorrhage (IVH) or bleed within the cerebral ventricles is a common condition among very low birth weight pre-term neonates. The prognosis for these patients is worsened should they develop progressive ventricular dilatation, i.e., post-hemorrhagic ventricle dilatation (PHVD), which occurs in 10-30% of IVH patients. Accurate measurement of ventricular volume would be valuable information and could be used to predict PHVD and determine whether that specific patient with ventricular dilatation requires treatment. While the monitoring of PHVD in infants is typically done by repeated transfontanell 2D ultrasound (US) and not MRI, once the patients fontanels have closed around 12-18months of life, the follow-up patient scans are done by MRI. Manual segmentation of ventricles from MR images is still seen as a gold standard. However, it is extremely time- and labor-consuming, and it also has observer variability. This paper proposes an accurate multiphase geodesic level-set segmentation algorithm for the extraction of the cerebral ventricle system of pre-term PHVD neonates from 3D T1 weighted MR images. The proposed segmentation algorithm makes use of multi-region segmentation technique associated with spatial priors built from a multi-atlas registration scheme. The leave-one-out cross validation with 19 patients with mild enlargement of ventricles and 7 hydrocephalus patients shows that the proposed method is accurate, suggesting that the proposed approach could be potentially used for volumetric and morphological analysis of the ventricle system of IVH neonatal brains in clinical practice.

Development of a 3D ultrasound system to investigate post-hemorrhagic hydrocephalus in pre-term neonates

Clinical intracranial ultrasound (US) is performed as a standard of care on neonates at risk of intraventricular hemorrhaging (IVH) and is also used after a diagnosis to monitor for potential ventricular dilation. However, it is difficult to estimate the volume of ventricles with 2D US due to their irregular shape. We developed a 3D US system to be used as an adjunct to a clinical system to investigate volumetric changes in the ventricles of neonates with IVH. Our system has been found have an error of within 1% of actual distance measurements in all three directions and volume measurements of manually segmented volumes from phantoms were not statistically significantly different from the actual values (p>0.3). Interobserver volume measurements of the lateral ventricles in a patient with grade III IVH found no significant differences between measurements. There is the potential to use this system in IVH patients to monitor the progression of ventriculomegaly over time.

Longitudinal Analysis of Pre-term Neonatal Brain Ventricle in Ultrasound Images Based on Convex Optimization

Intraventricular hemorrhage (IVH) is a major cause of brain injury in preterm neonates and leads to dilatation of the ventricles. Measuring ventricular volume quantitatively is an important step in monitoring patients and evaluating treatment options. 3D ultrasound (US) has been developed to monitor ventricle volume as a biomarker for ventricular dilatation and deformation. Ventricle volume as a global indicator, however, does not allow for the precise analysis of local ventricular changes. In this work, we propose a 3D+t spatial-temporal nonlinear registration approach, which is used to analyze the detailed local changes of the ventricles of preterm IVH neonates from 3D US images. In particular, a novel sequential convex/dual optimization is introduced to extract the optimal 3D+t spatial-temporal deformable registration. The experiments with five patients with 4 time-point images for each patient showed that the proposed registration approach accurately and efficiently recovered the longitudinal deformation of the ventricles from 3D US images. To the best of our knowledge, this paper reports the first study on the longitudinal analysis of the ventriclar system of pre-term newborn brains from 3D US images.

Characterization of neonatal patients with intraventricular hemorrhage using 3D ultrasound cerebral ventricle volumes

One of the major non-congenital cause of neurological impairment among neonates born very preterm is intraventricular hemorrhage (IVH) - bleeding within the lateral ventricles. Most IVH patients will have a transient period of ventricle dilation that resolves spontaneously. However, those patients most at risk of long-term impairment are those who have progressive ventricle dilation as this causes macrocephaly, an abnormally enlarged head, then later causes increases intracranial pressure (ICP). 2D ultrasound (US) images through the fontanelles of the patients are serially acquired to monitor the progression of the ventricle dilation. These images are used to determine when interventional therapies such as needle aspiration of the built up CSF might be indicated for a patient. Initial therapies usually begin during the third week of life. Such interventions have been shown to decrease morbidity and mortality in IVH patients; however, this comes with risks of further hemorrhage or infection; therefore only patients requiring it should be treated. Previously we have developed and validated a 3D US system to monitor the progression of ventricle volumes (VV) in IVH patients. This system has been validated using phantoms and a small set of patient images. The aim of this work is to determine the ability of 3D US generated VV to categorize patients into those who will require interventional therapies, and those who will have spontaneous resolution. Patients with higher risks could therefore be monitored better, by re-allocating some of the resources as the low risks infants would need less monitoring.

Quantification of cerebral ventricle volume change of preterm neonates using 3D ultrasound images

Intraventricular hemorrhage (IVH) is a major cause of brain injury in preterm neonates. Quantitative measurement of ventricular dilation or shrinkage is important for monitoring patients and in evaluation of treatment options. 3D ultrasound (US) has been used to monitor the ventricle volume as a biomarker for ventricular dilation. However, volumetric quantification does not provide information as to where dilation occurs. The location where dilation occurs may be related to specific neurological problems later in life. For example, posterior horn enlargement, with thinning of the corpus callosum and parietal white matter fibres, could be linked to poor visuo-spatial abilities seen in hydrocephalic children. In this work, we report on the development and application of a method used to analyze local surface change of the ventricles of preterm neonates with IVH from 3D US images. The technique is evaluated using manual segmentations from 3D US images acquired in two imaging sessions. The surfaces from baseline and follow-up were registered and then matched on a point-by-point basis. The distance between each pair of corresponding points served as an estimate of local surface change of the brain ventricle at each vertex. The measurements of local surface change were then superimposed on the ventricle surface to produce the 3D local surface change map that provide information on the spatio-temporal dilation pattern of brain ventricles following IVH. This tool can be used to monitor responses to different treatment options, and may provide important information for elucidating the deficiencies a patient will have later in life.

Simultaneous vibration and high-speed microscopy to study mechanotransduction in living cells

Cells exhibit the ability to sense and respond to local mechanical stimuli, leading to changes in function. This capability, referred to as mechanotransduction, is essential to normal tissue function, but the exact mechanisms by which cells sense local forces (strain, shear, compression and vibration) remain unclear. Recent studies in small animals and humans indicate that the frequency of cyclic mechanical stimuli is important, with physiological responses observed for stimuli ranging between 1 and 90 Hz. To better understand the cellular and molecular mechanisms underlying mechanotransduction, it will be important to observe cells in real time, using optical microscopy during high-frequency mechanical stimulation. We have developed a motion-control platform that can produce sinusoidal vibration of live cells during simultaneous high-speed microscopy and fluorimetry, at frequencies up to 100 Hz with peak acceleration up to 9.8 m s-2. The platform is driven by a voice coil and acceleration is measured with an accelerometer (Dytran 7521A1). The motion waveform was verified by high-speed imaging, using a digital camera (Casio EX-F1) operating at 1200 frames s-1 attached to an inverted microscope (Nikon Diaphot). When operating at 45 Hz and 2.94 m s-2 peak acceleration, the observed motion waveform exhibited sinusoidal behaviour, with measured peak-to-peak amplitude of 72 μm. Cultured osteoblast-like cells (UMR-106) were subjected to 2.94 m s-2 vibration at 45 Hz and remained attached and viable. This device provides - for the first time - the capability to mechanically stimulate living cells while simultaneously observing responses with optical microscopy.

A framework for quantification and visualization of segmentation accuracy and variability in 3D lateral ventricle ultrasound images of preterm neonates

PURPOSE Intraventricular hemorrhage (IVH) is a major cause of brain injury in preterm neonates. Three dimensional ultrasound (US) imaging systems have been developed to visualize 3D anatomical structure of preterm neonatal intracranial ventricular system with IVH and ventricular dilation. To allow quantitative analysis, the ventricle system is required to be segmented accurately and efficiently from 3D US images. Although semiautomatic segmentation algorithms have been developed, local segmentation accuracy and variability associated with these algorithms should be evaluated statistically before they can be applied in clinical settings. This work proposes a statistical framework to quantify the local accuracy and variability and performs statistical tests to identify locations where the semiautomatically segmented surfaces are significantly different from manually segmented surfaces. METHODS Three dimensional lateral ventricle US images of preterm neonates were each segmented six times manually and using a semiautomated segmentation algorithm. The local difference between manually and algorithmically segmented surfaces as well as the segmentation variability for each method was computed and superimposed on the ventricular surface of each subject. To summarize the segmentation performance for a whole group of subjects, the subject-specific local difference and standard deviation maps were registered onto a 3D template ventricular surface using a nonrigid registration algorithm. Pointwise, intersubject average accuracy and pooled variability for the whole group of subjects can be computed and visualized on the template surface, providing a summary of performance of the segmentation algorithm for the whole group of ventricles with highly variable geometry. In addition to pointwise statistical analysis performed on the template surface, statistical conclusion regarding the accuracy of the segmentation algorithm was made for subregions and the whole ventricle with the spatial correlation of pointwise accuracy taken into account. RESULTS Ten 3D US images were involved in this study. Pointwise local difference, ΔS, its absolute value |ΔS| as well as the standard deviations of the manual and algorithm segmentations were computed and superimposed on the each ventricle surface. Regions with lower segmentation accuracy and higher segmentation variability can be identified from these maps, and the localized information was applied to improve the accuracy of the algorithm. Intersubject average ΔS and |ΔS| as well as pooled standard deviations was computed on the template surface. Intersubject average ΔS and |ΔS| indicated that the algorithm underestimated regions in the neighborhood of the tips of anterior, inferior, and posterior horns. Intersubject pooled standard deviations indicated that manual segmentation had a higher segmentation variability than algorithm segmentation over the whole ventricle. Statistical analysis on the template surface showed that there was significant difference between algorithm and manual methods for segmenting the right lateral ventricle but not for the left lateral ventricle. CONCLUSIONS A framework was proposed for evaluating, visualizing, and summarizing the local accuracy and variability of a segmentation algorithm. This framework can be used for improving the accuracy of segmentation algorithms, as well as providing useful feedback to improve the manual segmentation performance. More importantly, this framework can be applied for longitudinal monitoring of local ventricular changes of neonates with IVH.