Volumetric brain analysis in neurosurgery: Part 1. Particle filter segmentation of brain and cerebrospinal fluid growth dynamics from MRI and CT images

Fully automated measurement of intracranial CSF and brain parenchyma volumes in pediatric hydrocephalus by segmentation of clinical MRI studies

BACKGROUND

Brain parenchyma (BP) and intracranial cerebrospinal fluid (iCSF) volumes measured by fully automated segmentation of clinical brain MRI studies may be useful for the diagnosis and follow-up of pediatric hydrocephalus. However, previously published segmentation techniques either rely on dedicated sequences, not routinely used in clinical practice, or on spatial normalization, which has limited accuracy when severe brain distortions, such as in hydrocephalic patients, are present.

PURPOSE

We developed a fully automated method to measure BP and iCSF volumes from clinical brain MRI studies of pediatric hydrocephalus patients, exploiting the complementary information contained in T2- and T1-weighted images commonly used in clinical practice.

METHODS

The proposed procedure, following skull-stripping of the combined volumes, performed using a multiparametric method to obtain a reliable definition of the inner skull profile, maximizes the CSF-to-parenchyma contrast by dividing the T2w- by the T1w- volume after full-scale dynamic rescaling, thus allowing separation of iCSF and BP through a simple thresholding routine.

RESULTS

Validation against manual tracing on 23 studies (four controls and 19 hydrocephalic patients) showed excellent concordance (ICC > 0.98) and spatial overlap (Dice coefficients ranging from 77.2% for iCSF to 96.8% for intracranial volume). Accuracy was comparable to the intra-operator reproducibility of manual segmentation, as measured in 14 studies processed twice by the same experienced neuroradiologist. Results of the application of the algorithm to a dataset of 63 controls and 57 hydrocephalic patients (19 with parenchymal damage), measuring volumes' changes with normal development and in hydrocephalic patients, are also reported for demonstration purposes.

CONCLUSIONS

The proposed approach allows fully automated segmentation of BP and iCSF in clinical studies, also in severely distorted brains, enabling to assess age- and disease-related changes in intracranial tissue volume with an accuracy comparable to expert manual segmentation.

Improving Infant Hydrocephalus Outcomes in Uganda: A Longitudinal Prospective Study Protocol for Predicting Developmental Outcomes and Identifying Patients at Risk for Early Treatment Failure after ETV/CPC

Infant hydrocephalus poses a severe global health burden; 80% of cases occur in the developing world where patients have limited access to neurosurgical care. Surgical treatment combining endoscopic third ventriculostomy and choroid plexus cauterization (ETV/CPC), first practiced at CURE Children's Hospital of Uganda (CCHU), is as effective as standard ventriculoperitoneal shunt (VPS) placement while requiring fewer resources and less post-operative care. Although treatment focuses on controlling ventricle size, this has little association with treatment failure or long-term outcome. This study aims to monitor the progression of hydrocephalus and treatment response, and investigate the association between cerebral physiology, brain growth, and neurodevelopmental outcomes following surgery. We will enroll 300 infants admitted to CCHU for treatment. All patients will receive pre/post-operative measurements of cerebral tissue oxygenation (SO2), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) using frequency-domain near-infrared combined with diffuse correlation spectroscopies (FDNIRS-DCS). Infants will also receive brain imaging, to monitor tissue/ventricle volume, and neurodevelopmental assessments until two years of age. This study will provide a foundation for implementing cerebral physiological monitoring to establish evidence-based guidelines for hydrocephalus treatment. This paper outlines the protocol, clinical workflow, data management, and analysis plan of this international, multi-center trial.

A Comparison of Cranial Cavity Extraction Tools for Non-contrast Enhanced CT Scans in Acute Stroke Patients

Cranial cavity extraction is often the first step in quantitative neuroimaging analyses. However, few automated, validated extraction tools have been developed for non-contrast enhanced CT scans (NECT). The purpose of this study was to compare and contrast freely available tools in an unseen dataset of real-world clinical NECT head scans in order to assess the performance and generalisability of these tools. This study included data from a demographically representative sample of 428 patients who had completed NECT scans following hospitalisation for stroke. In a subset of the scans (n = 20), the intracranial spaces were segmented using automated tools and compared to the gold standard of manual delineation to calculate accuracy, precision, recall, and dice similarity coefficient (DSC) values. Further, three readers independently performed regional visual comparisons of the quality of the results in a larger dataset (n = 428). Three tools were found; one of these had unreliable performance so subsequent evaluation was discontinued. The remaining tools included one that was adapted from the FMRIB software library (fBET) and a convolutional neural network- based tool (rBET). Quantitative comparison showed comparable accuracy, precision, recall and DSC values (fBET: 0.984 ± 0.002; rBET: 0.984 ± 0.003; p = 0.99) between the tools; however, intracranial volume was overestimated. Visual comparisons identified characteristic regional differences in the resulting cranial cavity segmentations. Overall fBET had highest visual quality ratings and was preferred by the readers in the majority of subject results (84%). However, both tools produced high quality extractions of the intracranial space and our findings should improve confidence in these automated CT tools. Pre- and post-processing techniques may further improve these results.

Spinal alignment shift between supine and prone CT imaging occurs frequently and regardless of the anatomic region, risk factors, or pathology

Computer-assisted spine surgery based on preoperative CT imaging may be hampered by sagittal alignment shifts due to an intraoperative switch from supine to prone. In the present study, we systematically analyzed the occurrence and pattern of sagittal spinal alignment shift between corresponding preoperative (supine) and intraoperative (prone) CT imaging in patients that underwent navigated posterior instrumentation between 2014 and 2017. Sagittal alignment across the levels of instrumentation was determined according to the C2 fracture gap (C2-F) and C2 translation (C2-T) in odontoid type 2 fractures, next to the modified Cobb angle (CA), plumbline (PL), and translation (T) in subaxial pathologies. One-hundred and twenty-one patients (C1/C2: n = 17; C3-S1: n = 104) with degenerative (39/121; 32%), oncologic (35/121; 29%), traumatic (34/121; 28%), or infectious (13/121; 11%) pathologies were identified. In the subaxial spine, significant shift occurred in 104/104 (100%) cases (CA: *p = .044; T: *p = .021) compared to only 10/17 (59%) cases that exhibited shift at the C1/C2 level (C2-F: **p = .002; C2-T: *p < .016). The degree of shift was not affected by the anatomic region or pathology but significantly greater in cases with an instrumentation length > 5 segments (“∆PL > 5 segments”: 4.5 ± 1.8 mm; “∆PL ≤ 5 segments”: 2 ± 0.6 mm; *p = .013) or in revision surgery with pre-existing instrumentation (“∆PL presence”: 5 ± 2.6 mm; “∆PL absence”: 2.4 ± 0.7 mm; **p = .007). Interestingly, typical morphological instability risk factors did not influence the degree of shift. In conclusion, intraoperative spinal alignment shift due to a change in patient position should be considered as a cause for inaccuracy during computer-assisted spine surgery and when correcting spinal alignment according to parameters that were planned in other patient positions.

Paenibacillus infection with frequent viral coinfection contributes to postinfectious hydrocephalus in Ugandan infants

Postinfectious hydrocephalus (PIH), which often follows neonatal sepsis, is the most common cause of pediatric hydrocephalus worldwide, yet the microbial pathogens underlying this disease remain to be elucidated. Characterization of the microbial agents causing PIH would enable a shift from surgical palliation of cerebrospinal fluid (CSF) accumulation to prevention of the disease. Here, we examined blood and CSF samples collected from 100 consecutive infant cases of PIH and control cases comprising infants with non-postinfectious hydrocephalus in Uganda. Genomic sequencing of samples was undertaken to test for bacterial, fungal, and parasitic DNA; DNA and RNA sequencing was used to identify viruses; and bacterial culture recovery was used to identify potential causative organisms. We found that infection with the bacterium Paenibacillus, together with frequent cytomegalovirus (CMV) coinfection, was associated with PIH in our infant cohort. Assembly of the genome of a facultative anaerobic bacterial isolate recovered from cultures of CSF samples from PIH cases identified a strain of Paenibacillus thiaminolyticus This strain, designated Mbale, was lethal when injected into mice in contrast to the benign reference Paenibacillus strain. These findings show that an unbiased pan-microbial approach enabled characterization of Paenibacillus in CSF samples from PIH cases, and point toward a pathway of more optimal treatment and prevention for PIH and other proximate neonatal infections.

Biomimetic phantom with anatomical accuracy for evaluating brain volumetric measurements with magnetic resonance imaging

Purpose: Brain image volumetric measurements (BVM) methods have been used to quantify brain tissue volumes using magnetic resonance imaging (MRI) when investigating abnormalities. Although BVM methods are widely used, they need to be evaluated to quantify their reliability. Currently, the gold-standard reference to evaluate a BVM is usually manual labeling measurement. Manual volume labeling is a time-consuming and expensive task, but the confidence level ascribed to this method is not absolute. We describe and evaluate a biomimetic brain phantom as an alternative for the manual validation of BVM. Methods: We printed a three-dimensional (3D) brain mold using an MRI of a three-year-old boy diagnosed with Sturge-Weber syndrome. Then we prepared three different mixtures of styrene-ethylene/butylene-styrene gel and paraffin to mimic white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The mold was filled by these three mixtures with known volumes. We scanned the brain phantom using two MRI scanners, 1.5 and 3.0 Tesla. Our suggestion is a new challenging model to evaluate the BVM which includes the measured volumes of the phantom compartments and its MRI. We investigated the performance of an automatic BVM, i.e., the expectation-maximization (EM) method, to estimate its accuracy in BVM. Results: The automatic BVM results using the EM method showed a relative error (regarding the phantom volume) of 0.08, 0.03, and 0.13 ( ± 0.03 uncertainty) percentages of the GM, CSF, and WM volume, respectively, which was in good agreement with the results reported using manual segmentation. Conclusions: The phantom can be a potential quantifier for a wide range of segmentation methods.

Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms

Background

For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a classical machine learning algorithm. It demonstrated good clinical usability and a very accurate correlation of the volumes to the single area determination in a reproducible axial layer. This study aims to investigate whether these established segmentation algorithms can be transferred to new, more generalizable deep learning algorithms employing an extended transfer learning procedure and whether medically meaningful segmentation is possible.

Methods

Ninety-five routinely performed true FISP MRI sequences were retrospectively analyzed in 43 patients with pediatric hydrocephalus. Using a freely available and clinically established segmentation algorithm based on a hidden Markov random field model, four classes of segmentation (brain, cerebrospinal fluid (CSF), background, and tissue) were generated. Fifty-nine randomly selected data sets (10,432 slices) were used as a training data set. Images were augmented for contrast, brightness, and random left/right and X/Y translation. A convolutional neural network (CNN) for semantic image segmentation composed of an encoder and corresponding decoder subnetwork was set up. The network was pre-initialized with layers and weights from a pre-trained VGG 16 model. Following the network was trained with the labeled image data set. A validation data set of 18 scans (3289 slices) was used to monitor the performance as the deep CNN trained. The classification results were tested on 18 randomly allocated labeled data sets (3319 slices) and on a T2-weighted BrainWeb data set with known ground truth.

Results

The segmentation of clinical test data provided reliable results (global accuracy 0.90, Dice coefficient 0.86), while the CNN segmentation of data from the BrainWeb data set showed comparable results (global accuracy 0.89, Dice coefficient 0.84). The segmentation of the BrainWeb data set with the classical FAST algorithm produced consistent findings (global accuracy 0.90, Dice coefficient 0.87). Likewise, the area development of brain and CSF in the long-term clinical course of three patients was presented.

Conclusion

Using the presented methods, we showed that conventional segmentation algorithms can be transferred to new advances in deep learning with comparable accuracy, generating a large number of training data sets with relatively little effort. A clinically meaningful segmentation possibility was demonstrated.

Normal cerebral ventricular volume growth in childhood

OBJECTIVE

Normal percentile growth charts for head circumference, length, and weight are well-established tools for clinicians to detect abnormal growth patterns. Currently, no standard exists for evaluating normal size or growth of cerebral ventricular volume. The current standard practice relies on clinical experience for a subjective assessment of cerebral ventricular size to determine whether a patient is outside the normal volume range. An improved definition of normal ventricular volumes would facilitate a more data-driven diagnostic process. The authors sought to develop a growth curve of cerebral ventricular volumes using a large number of normal pediatric brain MR images.

METHODS

The authors performed a retrospective analysis of patients aged 0 to 18 years, who were evaluated at their institution between 2009 and 2016 with brain MRI performed for headaches, convulsions, or head injury. Patients were excluded for diagnoses of hydrocephalus, congenital brain malformations, intracranial hemorrhage, meningitis, or intracranial mass lesions established at any time during a 3- to 10-year follow-up. The volume of the cerebral ventricles for each T2-weighted MRI sequence was calculated with a custom semiautomated segmentation program written in MATLAB. Normal percentile curves were calculated using the lambda-mu-sigma smoothing method.

RESULTS

Ventricular volume was calculated for 687 normal brain MR images obtained in 617 different patients. A chart with standardized growth curves was developed from this set of normal ventricular volumes representing the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles. The charted data were binned by age at scan date by 3-month intervals for ages 0-1 year, 6-month intervals for ages 1-3 years, and 12-month intervals for ages 3-18 years. Additional percentile values were calculated for boys only and girls only.

CONCLUSIONS

The authors developed centile estimation growth charts of normal 3D ventricular volumes measured on brain MRI for pediatric patients. These charts may serve as a quantitative clinical reference to help discern normal variance from pathologic ventriculomegaly.

Neurodevelopmental outcome at 2 years after neuroendoscopic lavage in neonates with posthemorrhagic hydrocephalus

OBJECTIVE

A standardized guideline for treatment of posthemorrhagic hydrocephalus in premature infants is still missing. Because an early ventriculoperitoneal shunt surgery is avoided due to low body weight and fragility of the patients, the neurosurgical treatment focuses on temporary solutions for CSF diversion as a minimally invasive approach. Neuroendoscopic lavage (NEL) was additionally introduced for early elimination of intraventricular blood components to reduce possible subsequent complications such as shunt dependency, infection, and multiloculated hydrocephalus. The authors report their first experience regarding neurodevelopmental outcome after NEL in this patient cohort.

METHODS

In a single-center retrospective cohort study with 45 patients undergoing NEL, the authors measured neurocognitive development at 2 years with the Bayley Scales of Infant Development, 2nd Edition, Mental Developmental Index (BSID II MDI) and graded the ability to walk with the Gross Motor Function Classification System (GMFCS). They further recorded medication with antiepileptic drugs (AEDs) and quantified ventricular and brain volumes by using 3D MRI data sets.

RESULTS

Forty-four patients were alive at 2 years of age. Eight of 27 patients (30%) assessed revealed a fairly normal neurocognitive development (BSID II MDI ≥ 70), 28 of 36 patients (78%) were able to walk independently or with minimal aid (GMFCS 0-2), and 73% did not require AED treatment. Based on MR volume measurements, greater brain volume was positively correlated with BSID II MDI (rs = 0.52, 95% CI 0.08-0.79) and negatively with GMFCS (rs = -0.69, 95% CI -0.85 to -0.42). Based on Bayesian logistic regression, AED treatment, the presence of comorbidities, and also cerebellar pathology could be identified as relevant risk factors for both neurodevelopmental outcomes, increasing the odds more than 2-fold-but with limited precision in estimation.

CONCLUSIONS

Neuromotor outcome assessment after NEL is comparable to previously published drainage, irrigation, and fibrinolytic therapy (DRIFT) study results. A majority of NEL-treated patients showed independent mobility. Further validation of outcome measurements is warranted in an extended setup, as intended by the prospective international multicenter registry for treatment of posthemorrhagic hydrocephalus (TROPHY).

Planar single plane area determination is a viable substitute for total volumetry of CSF and brain in childhood hydrocephalus

BACKGROUND

In the treatment of childhood hydrocephalus, 3D volumetry seems to have many advantages over classical planar index measurements for dedicated monitoring of changes in cerebrospinal fluid and brain volume. Nevertheless, this method requires extensive technical effort and access to the complete three-dimensional data set. Against this background, we evaluated the possibility of planar area determination in a single plane and the correlation to volumetry.

METHODS

138 routinely performed true FISP MRI sequences (1 mm isovoxel) were analyzed retrospectively in 68 patients with pediatric hydrocephalus. After preprocessing, the 3D-data sets were skull stripped to estimate the inner skull volume. A 2-class segmentation into different tissue types (brain matter and CSF) was performed, and the volumes of CSF (VCSF) and brain matter (VBrain) were calculated. A plane at the level of the foramina of Monro was manually identified in the ac-pc oriented data. In this plane, the areas of brain (ABrain) and CSF (ACSF) in cm² were calculated and used for further correlation analysis.

RESULTS

Mean VCSF was 340 ± 145 cm³ and VBrain 1173 ± 254 cm³. In the selected plane, ACSF was 26 ± 14 cm², and ABrain was 107 ± 25 cm². There was a very strong positive correlation between both ACSF and VCSF (r = 0.895) and between ABrain and VBrain (r = 0.846). The prediction equations for VBrain and VCSF were highly significant.

CONCLUSION

Planar area determination of brain and CSF correlates excellently with both VCSF and VBrain. Thus, areas can serve as a surrogate marker for total brain and CSF volumes for a quantitated objective tracking of changes during treatment of childhood hydrocephalus.

A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease

BACKGROUND AND PURPOSE

Total brain volume and total intracranial volume are important measures for assessing whole-brain atrophy in Alzheimer disease, dementia, and other neurodegenerative diseases. Unlike MR imaging, which has a number of well-validated fully-automated methods, only a handful of methods segment CT images. Available methods either use enhanced CT, do not estimate both volumes, or require formal validation. Reliable computation of total brain volume and total intracranial volume from CT is needed because head CTs are more widely used than head MRIs in the clinical setting. We present an automated head CT segmentation method (CTseg) to estimate total brain volume and total intracranial volume.

MATERIALS AND METHODS

CTseg adapts a widely used brain MR imaging segmentation method from the Statistical Parametric Mapping toolbox using a CT-based template for initial registration. CTseg was tested and validated using head CT images from a clinical archive.

RESULTS

CTseg showed excellent agreement with 20 manually segmented head CTs. The intraclass correlation was 0.97 (P < .001) for total intracranial volume and 0.94 (P < .001) for total brain volume. When CTseg was applied to a cross-sectional Alzheimer disease dataset (58 with Alzheimer disease patients and 58 matched controls), CTseg detected a loss in percentage total brain volume (as a percentage of total intracranial volume) with age (P < .001) as well as a group difference between patients with Alzheimer disease and controls (P < .01). We observed similar results when total brain volume was modeled with total intracranial volume as a confounding variable.

CONCLUSIONS

In current clinical practice, brain atrophy is assessed by inaccurate and subjective "eyeballing" of CT images. Manual segmentation of head CT images is prohibitively arduous and time-consuming. CTseg can potentially help clinicians to automatically measure total brain volume and detect and track atrophy in neurodegenerative diseases. In addition, CTseg can be applied to large clinical archives for a variety of research studies.

Automatic volumetry of cerebrospinal fluid and brain volume in severe paediatric hydrocephalus, implementation and clinical course after intervention

BACKGROUND

In childhood hydrocephalus, both the amount of cerebrospinal fluid and the brain volume are relevant for the prognosis of the development and for therapy monitoring. Since classical planar measurements of ventricular size are subject to strong limitations, imprecise and neglect brain volume, 3D volumetry is most desirable. We used and evaluated the robust segmentation algorithms of the freely available FSL-toolbox in paediatric hydrocephalus patients before and after specific therapy.

METHODS

Retrospectively 76 pre- and postoperative high-resolution T2-weighted MRI sequences (true FISP, 1 mm isovoxel) were analyzed in 38 patients with paediatric hydrocephalus (mean 4.4 ± 5.1 years) who underwent surgical treatment (ventriculo-peritoneal (VP) shunt n = 22, endoscopic third ventriculostomy (ETV) n = 16). After preprocessing, the 3D-datasets were skull stripped to estimate the inner skull surface. Following, a 2 class segmentation into different tissue types (brain matter and CSF) was performed. The volumes of CSF and brain were calculated.

RESULTS

The method could be implemented in an automated fashion in all 76 MRIs. In the VP shunt cohort, the amount of CSF (p < 0.001) decreased. Consecutively brain volume increased significantly (p < 0.001). Following ETV, CSF volume (p = 0.019) decreased significantly (p = 0.012) although the reduction was less pronounced than after shunt implantation. Brain volume expanded (p = 0.02).

CONCLUSION

A reliable automated segmentation of CSF and brain could be performed with the implemented algorithm. The method was able to track changes after therapy and detected significant differences in CSF and brain volumes after shunting and after ETV.

Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods

Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by manual segmentation on Computed Tomography (CT) images. Further, some of the segmentations are prone to radiologist bias and high intraobserver variability. To improve this, researchers are exploring methods to automate the process, which would enable faster and more unbiased results. In this study, we propose the application of U-Net convolutional neural network in order to automatically segment CT brain scans for location of CSF. U-Net is a neural network that has proven to be successful for various interdisciplinary segmentation tasks. We optimised training using state of the art methods, including “1cycle” learning rate policy, transfer learning, generalized dice loss function, mixed float precision, self-attention, and data augmentation. Even though the study was performed using a limited amount of data (80 CT images), our experiment has shown near human-level performance. We managed to achieve a 0.917 mean dice score with 0.0352 standard deviation on cross validation across the training data and a 0.9506 mean dice score on a separate test set. To our knowledge, these results are better than any known method for CSF segmentation in hydrocephalic patients, and thus, it is promising for potential practical applications.

Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography

OBJECTIVE

The goal of this study was to develop and validate a system for automatic segmentation of the spine, pedicle identification, and screw path suggestion for use with an intraoperative 3D surgical navigation system.

METHODS

Cone-beam CT (CBCT) images of the spines of 21 cadavers were obtained. An automated model-based approach was used for segmentation. Using machine learning methodology, the algorithm was trained and validated on the image data sets. For measuring accuracy, surface area errors of the automatic segmentation were compared to the manually outlined reference surface on CBCT. To further test both technical and clinical accuracy, the algorithm was applied to a set of 20 clinical cases. The authors evaluated the system's accuracy in pedicle identification by measuring the distance between the user-defined midpoint of each pedicle and the automatically segmented midpoint. Finally, 2 independent surgeons performed a qualitative evaluation of the segmentation to judge whether it was adequate to guide surgical navigation and whether it would have resulted in a clinically acceptable pedicle screw placement.

RESULTS

The clinically relevant pedicle identification and automatic pedicle screw planning accuracy was 86.1%. By excluding patients with severe spinal deformities (i.e., Cobb angle > 75° and severe spinal degeneration) and previous surgeries, a success rate of 95.4% was achieved. The mean time (± SD) for automatic segmentation and screw planning in 5 vertebrae was 11 ± 4 seconds.

CONCLUSIONS

The technology investigated has the potential to aid surgeons in navigational planning and improve surgical navigation workflow while maintaining patient safety.

Thresholds for identifying pathological intracranial pressure in paediatric traumatic brain injury

Intracranial pressure (ICP) monitoring forms an integral part of the management of severe traumatic brain injury (TBI) in children. The prediction of elevated ICP from imaging is important when deciding on whether to implement invasive ICP monitoring for a patient. However, the radiological markers of pathologically elevated ICP have not been specifically validated in paediatric studies. Here in, we describe an objective, non-invasive, quantitative method of stratifying which patients are likely to require invasive monitoring. A retrospective review of patients admitted to Cambridge University Hospital's Paediatric Intensive Care Unit between January 2009 and December 2016 with a TBI requiring invasive neurosurgical monitoring was performed. Radiological biomarkers of TBI (basal cistern volume, ventricular volume, volume of extra-axial haematomas) from CT scans were measured and correlated with epochs of continuous high frequency variables of pressure monitoring around the time of imaging. 38 patients were identified. Basal cistern volume was found to correlate significantly with opening ICP (r = -0.53, p < 0.001). The optimal threshold of basal cistern volume for predicting high ICP ([Formula: see text]20 mmHg) was a relative volume of 0.0055 (sensitivity 79%, specificity 80%). Ventricular volume and extra-axial haematoma volume did not correlate significantly with opening ICP. Our results show that the features of pathologically elevated ICP in children may differ considerably from those validated in adults. The development of quantitative parameters can help to predict which patients would most benefit from invasive neurosurgical monitoring and we present a novel radiological threshold for this.

Neurosurgery in East Africa: Innovations

In the last 10 years, considerable work has been done to promote and improve neurosurgical care in East Africa with the development of national training programs, expansion of hospitals and creation of new institutions, and the foundation of epidemiologic and cost-effectiveness research. Many of the results have been accomplished through collaboration with partners from abroad. This article is the third in a series of articles that seek to provide readers with an understanding of the development of neurosurgery in East Africa (Foundations), the challenges that arise in providing neurosurgical care in developing countries (Challenges), and an overview of traditional and novel approaches to overcoming these challenges to improve healthcare in the region (Innovations). In this article, we describe the ongoing programs active in East Africa and their current priorities, and we outline lessons learned and what is required to create self-sustained neurosurgical service.

Normative human brain volume growth

OBJECTIVE While there is a long history of interest in measuring brain growth, as of yet there is no definitive model for normative human brain volume growth. The goal of this study was to analyze a variety of candidate models for such growth and select the model that provides the most statistically applicable fit. The authors sought to optimize clinically applicable growth charts that would facilitate improved treatment and predictive management for conditions such as hydrocephalus. METHODS The Weibull, two-term power law, West ontogenic, and Gompertz models were chosen as potential models. Normative brain volume data were compiled from the NIH MRI repository, and the data were fit using a nonlinear least squares regression algorithm. Appropriate statistical measures were analyzed for each model, and the best model was characterized with prediction bound curves to provide percentile estimates for clinical use. RESULTS Each model curve fit and the corresponding statistics were presented and analyzed. The Weibull fit had the best statistical results for both males and females, while the two-term power law generated the worst scores. The statistical measures and goodness of fit parameters for each model were provided to assure reproducibility. CONCLUSIONS The authors identified the Weibull model as the most effective growth curve fit for both males and females. Clinically usable growth charts were developed and provided to facilitate further clinical study of brain volume growth in conditions such as hydrocephalus. The authors note that the homogenous population from which the normative MRI data were compiled limits the study. Gaining a better understanding of the dynamics that underlie childhood brain growth would yield more predictive growth curves and improved neurosurgical management of hydrocephalus.

Automatically measuring brain ventricular volume within PACS using artificial intelligence

The picture archiving and communications system (PACS) is currently the standard platform to manage medical images but lacks analytical capabilities. Staying within PACS, the authors have developed an automatic method to retrieve the medical data and access it at a voxel level, decrypted and uncompressed that allows analytical capabilities while not perturbing the system’s daily operation. Additionally, the strategy is secure and vendor independent. Cerebral ventricular volume is important for the diagnosis and treatment of many neurological disorders. A significant change in ventricular volume is readily recognized, but subtle changes, especially over longer periods of time, may be difficult to discern. Clinical imaging protocols and parameters are often varied making it difficult to use a general solution with standard segmentation techniques. Presented is a segmentation strategy based on an algorithm that uses four features extracted from the medical images to create a statistical estimator capable of determining ventricular volume. When compared with manual segmentations, the correlation was 94% and holds promise for even better accuracy by incorporating the unlimited data available. The volume of any segmentable structure can be accurately determined utilizing the machine learning strategy presented and runs fully automatically within the PACS.

Endoscopic Treatment versus Shunting for Infant Hydrocephalus in Uganda

BACKGROUND

Postinfectious hydrocephalus in infants is a major health problem in sub-Saharan Africa. The conventional treatment is ventriculoperitoneal shunting, but surgeons are usually not immediately available to revise shunts when they fail. Endoscopic third ventriculostomy with choroid plexus cauterization (ETV-CPC) is an alternative treatment that is less subject to late failure but is also less likely than shunting to result in a reduction in ventricular size that might facilitate better brain growth and cognitive outcomes.

METHODS

We conducted a randomized trial to evaluate cognitive outcomes after ETV-CPC versus ventriculoperitoneal shunting in Ugandan infants with postinfectious hydrocephalus. The primary outcome was the Bayley Scales of Infant Development, Third Edition (BSID-3), cognitive scaled score 12 months after surgery (scores range from 1 to 19, with higher scores indicating better performance). The secondary outcomes were BSID-3 motor and language scores, treatment failure (defined as treatment-related death or the need for repeat surgery), and brain volume measured on computed tomography.

RESULTS

A total of 100 infants were enrolled; 51 were randomly assigned to undergo ETV-CPC, and 49 were assigned to undergo ventriculoperitoneal shunting. The median BSID-3 cognitive scores at 12 months did not differ significantly between the treatment groups (a score of 4 for ETV-CPC and 2 for ventriculoperitoneal shunting; Hodges-Lehmann estimated difference, 0; 95% confidence interval [CI], -2 to 0; P=0.35). There was no significant difference between the ETV-CPC group and the ventriculoperitoneal-shunt group in BSID-3 motor or language scores, rates of treatment failure (35% and 24%, respectively; hazard ratio, 0.7; 95% CI, 0.3 to 1.5; P=0.24), or brain volume (z score, -2.4 and -2.1, respectively; estimated difference, 0.3; 95% CI, -0.3 to 1.0; P=0.12).

CONCLUSIONS

This single-center study involving Ugandan infants with postinfectious hydrocephalus showed no significant difference between endoscopic ETV-CPC and ventriculoperitoneal shunting with regard to cognitive outcomes at 12 months. (Funded by the National Institutes of Health; ClinicalTrials.gov number, NCT01936272 .).

Learning Based Segmentation of CT Brain Images: Application to Postoperative Hydrocephalic Scans

OBJECTIVE

Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative (pre-op) versus postoperative (post-op)] plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-op computational tomographic (CT) scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity- and feature-based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e., a training set of CT scans with labeled pixel identities is employed.

METHODS

Our contributions include: 1) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes; 2) quantification of associated computation and memory footprint; and 3) a customized training and test procedure for segmenting post-op hydrocephalic CT images.

RESULTS

Experiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against a number of training samples, enhancing its deployment potential.

Cerebrospinal fluid volume does not have etiological role in the incidence of positional skull deformities

BACKGROUND

Positional skull deformities (PSD) are becoming a daily health concern for craniofacial surgeons. Several reports have indicated that cerebrospinal fluid (CSF) space increases on computed tomography (CT) scans of infants suffering from PSD, suggesting a potential causal link. Here, we describe a semi-automatic method to estimate total brain and CSF volumes quantitatively. We tested the potential correlation between total CSF volume and the occurrence of PSD.

METHODS

A single-center retrospective study was carried out using 79 CT scans of PSD and 60 CT scans of control subjects. The endocranium was segmented automatically using a three-dimensional deformable surface model, and the brain was segmented using a semi-automatic threshold-based method. Total CSF volume was estimated based on the difference between endocranial and brain volumes.

RESULTS

Automatic segmentation of the endocranium was possible in 75 CT scans. Semi-automatic brain and CSF volume evaluations were performed in 40 CT scans of infants with PSD (18 = occipital plagiocephaly, 11 = fronto-occipital plagiocephaly, and 11 = posterior brachycephaly) and in six control CT scans. Endocranial and total CSF volumes were not significantly different between patients with PSD and controls. The occipital plagiocephaly group had an enlarged brain volume compared with that in patients in the other groups.

CONCLUSIONS

Total CSF volume did not change in infants with PSD, and the results do not support a role for volume changes in CSF in the etiology of PSD. Macrocephaly in patients with occipital plagiocephaly may be a specific etiological factor compared with that in other PSDs.

Quantitative estimation of a ratio of intracranial cerebrospinal fluid volume to brain volume based on segmentation of CT images in patients with extra-axial hematoma

Background Diminishing volume of intracranial cerebrospinal fluid (CSF) in patients with space-occupying masses have been attributed to unfavorable outcome associated with reduction of cerebral perfusion pressure and subsequent brain ischemia. Objective The objective of this article is to employ a ratio of CSF volume to brain volume for longitudinal assessment of space-volume relationships in patients with extra-axial hematoma and to determine variability of the ratio among patients with different types and stages of hematoma. Patients and methods In our retrospective study, we reviewed 113 patients with surgical extra-axial hematomas. We included 28 patients (age 61.7 +/- 17.7 years; 19 males, nine females) with an acute epidural hematoma (EDH) ( n = 5) and subacute/chronic subdural hematoma (SDH) ( n = 23). We excluded 85 patients, in order, due to acute SDH ( n = 76), concurrent intraparenchymal pathology ( n = 6), and bilateral pathology ( n = 3). Noncontrast CT images of the head were obtained using a CT scanner (2004 GE LightSpeed VCT CT system, tube voltage 140 kVp, tube current 310 mA, 5 mm section thickness) preoperatively, postoperatively (3.8 ± 5.8 hours from surgery), and at follow-up clinic visit (48.2 ± 27.7 days after surgery). Each CT scan was loaded into an OsiriX (Pixmeo, Switzerland) workstation to segment pixels based on radiodensity properties measured in Hounsfield units (HU). Based on HU values from -30 to 100, brain, CSF spaces, vascular structures, hematoma, and/or postsurgical fluid were segregated from bony structures, and subsequently hematoma and/or postsurgical fluid were manually selected and removed from the images. The remaining images represented overall brain volume-containing only CSF spaces, vascular structures, and brain parenchyma. Thereafter, the ratio between the total number of voxels representing CSF volume (based on values between 0 and 15 HU) to the total number of voxels representing overall brain volume was calculated. Results CSF/brain volume ratio varied significantly during the course of the disease, being the lowest preoperatively, 0.051 ± 0.032; higher after surgical evacuation of hematoma, 0.067 ± 0.040; and highest at follow-up visit, 0.083 ± 0.040 ( p < 0.01). Using a repeated regression analysis, we found a significant association ( p < 0.01) of the ratio with age (odds ratio, 1.019; 95% CI, 1.009-1.029) and type of hematoma (odds ratio, 0.405; 95% CI, 0.303-0.540). Conclusion CSF/brain volume ratio calculated from CT images has potential to reflect dynamics of intracranial volume changes in patients with space-occupying mass.

Hydrocephalus in aqueductal stenosis--a retrospective outcome analysis and proposal of subtype classification

Treatment of aqueductal stenosis (AQS) has undergone several paradigm shifts during the past decades. Currently, endoscopic ventriculostomy (ETV) is recommended as treatment of choice. Several authors have addressed the issue of variable ETV success rates depending on age and pathogenetic factors. However, success rates have usually been defined as "ETV non-failure." The aim of the study was a retrospective analysis of radiological and neurological treatment response after ETV or VP-shunting (VPS) in age-dependent subtypes of AQS.

PATIENTS AND METHODS

Eighty patients (median age 12.0 years, range 0-79 years) have been treated for MRI-proven aqueductal stenosis. Neurological treatment success was defined by neurological improvement and, in childhood, head circumference. Radiological response was measured as Evan's index in follow-up MRI. Initial signs and symptoms, type of surgery, and complications were analyzed.

RESULTS

Four types of AQS have been defined with distinct age ranges and symptomatology: congenital type I (n = 24), chronic progressive (tectal tumor-like) type II (n = 23), acute type III (n = 10), and adult chronic (normal-pressure hydrocephalus-like) type IV (n = 23). Retrospective analysis of neurological and radiological outcome suggested that congenital type I (<1 years of age) may be more successfully treated with VPS than with ETV (81 vs. 50 %). Treatment of chronic juvenile type II (age 2-15) by ETV 19 % compared to 57 % after VP-shunt, but similar neurological improvement (>80 %). There has been no influence of persistent ventriculomegaly in type II after ETV in contrast to VPS therapy for neurological outcome. Adult acute type III (age > 15 years) responded excellent to ETV. Chronic type IV (iNPH-like) patients (age > 21) responded neurologically in 70 % after ETV and VPS, but radiological response was low (5 %).

CONCLUSION

AQS can be divided into four distinct age groups and types in regards of clinical course and symptomatology. Depending on the AQS type, ETV cannot be unequivocally recommended. Congenital type I AQS may have a better neurological outcome with VP-shunt whereas acute type III offers excellent ETV results. Chronic progressive type II still requires prospective investigation of long-term ETV outcome, especially when ventriculomegaly persists. Late chronic type IV seems to result in similar outcome after VP-shunt and ETV.

Validated automatic brain extraction of head CT images

BACKGROUND

X-ray computed tomography (CT) imaging of the brain is commonly used in diagnostic settings. Although CT scans are primarily used in clinical practice, they are increasingly used in research. A fundamental processing step in brain imaging research is brain extraction - the process of separating the brain tissue from all other tissues. Methods for brain extraction have either been 1) validated but not fully automated, or 2) fully automated and informally proposed, but never formally validated.

AIM

To systematically analyze and validate the performance of FSL's brain extraction tool (BET) on head CT images of patients with intracranial hemorrhage. This was done by comparing the manual gold standard with the results of several versions of automatic brain extraction and by estimating the reliability of automated segmentation of longitudinal scans. The effects of the choice of BET parameters and data smoothing is studied and reported.

METHODS

All images were thresholded using a 0-100 Hounsfield unit (HU) range. In one variant of the pipeline, data were smoothed using a 3-dimensional Gaussian kernel (σ=1mm(3)) and re-thresholded to 0-100HU; in the other, data were not smoothed. BET was applied using 1 of 3 fractional intensity (FI) thresholds: 0.01, 0.1, or 0.35 and any holes in the brain mask were filled. For validation against a manual segmentation, 36 images from patients with intracranial hemorrhage were selected from 19 different centers from the MISTIE (Minimally Invasive Surgery plus recombinant-tissue plasminogen activator for Intracerebral Evacuation) stroke trial. Intracranial masks of the brain were manually created by one expert CT reader. The resulting brain tissue masks were quantitatively compared to the manual segmentations using sensitivity, specificity, accuracy, and the Dice Similarity Index (DSI). Brain extraction performance across smoothing and FI thresholds was compared using the Wilcoxon signed-rank test. The intracranial volume (ICV) of each scan was estimated by multiplying the number of voxels in the brain mask by the dimensions of each voxel for that scan. From this, we calculated the ICV ratio comparing manual and automated segmentation: ICVautomated/ICVmanual. To estimate the performance in a large number of scans, brain masks were generated from the 6 BET pipelines for 1095 longitudinal scans from 129 patients. Failure rates were estimated from visual inspection. ICV of each scan was estimated and an intraclass correlation (ICC) was estimated using a one-way ANOVA.

RESULTS

Smoothing images improves brain extraction results using BET for all measures except specificity (all p<0.01, uncorrected), irrespective of the FI threshold. Using an FI of 0.01 or 0.1 performed better than 0.35. Thus, all reported results refer only to smoothed data using an FI of 0.01 or 0.1. Using an FI of 0.01 had a higher median sensitivity (0.9901) than an FI of 0.1 (0.9884, median difference: 0.0014, p<0.001), accuracy (0.9971 vs. 0.9971; median difference: 0.0001, p<0.001), and DSI (0.9895 vs. 0.9894; median difference: 0.0004, p<0.001) and lower specificity (0.9981 vs. 0.9982; median difference: -0.0001, p<0.001). These measures are all very high indicating that a range of FI values may produce visually indistinguishable brain extractions. Using smoothed data and an FI of 0.01, the mean (SD) ICV ratio was 1.002 (0.008); the mean being close to 1 indicates the ICV estimates are similar for automated and manual segmentation. In the 1095 longitudinal scans, this pipeline had a low failure rate (5.2%) and the ICC estimate was high (0.929, 95% CI: 0.91, 0.945) for successfully extracted brains.

CONCLUSION

BET performs well at brain extraction on thresholded, 1mm(3) smoothed CT images with an FI of 0.01 or 0.1. Smoothing before applying BET is an important step not previously discussed in the literature. Analysis code is provided.