Cranial bone thickness and density anomalies quantified from CT images can identify chronic increased intracranial pressure.

PURPOSE: The diagnosis of chronic increased intracranial pressure (IIP)is often based on subjective evaluation or clinical metrics with low predictive value. We aimed to quantify cranial bone changes associated with pediatric IIP using CT images and to identify patients at risk. METHODS: We retrospectively quantified local cranial bone thickness and mineral density from the CT images of children with chronic IIP and compared their statistical differences to normative children without IIP adjusting for age, sex and image resolution. Subsequently, we developed a classifier to identify IIP based on these measurements. Finally, we demonstrated our methods to explore signs of IIP in patients with non-syndromic sagittal craniosynostosis (NSSC). RESULTS: We quantified a significant decrease of bone density in 48 patients with IIP compared to 1,018 normative subjects (P < .001), but no differences in bone thickness (P = .56 and P = .89 for age groups 0-2 and 2-10 years, respectively). Our classifier demonstrated 83.33% (95% CI: 69.24%, 92.03%) sensitivity and 87.13% (95% CI: 84.88%, 89.10%) specificity in identifying patients with IIP. Compared to normative subjects, 242 patients with NSSC presented significantly lower cranial bone density (P < .001), but no differences were found compared to patients with IIP (P = .57). Of patients with NSSC, 36.78% (95% CI: 30.76%, 43.22%) presented signs of IIP. CONCLUSION: Cranial bone changes associated with pediatric IIP can be quantified from CT images to support earlier diagnoses of IIP, and to study the presence of IIP secondary to cranial pathology such as non-syndromic sagittal craniosynostosis.

Occult Scaphocephaly: A Forme Fruste Phenotype of Sagittal Craniosynostosis.

INTRODUCTION: Latent cranial suture fusions may present with mild or absent phenotypic changes that make the clinical diagnosis challenging. Recent reports describe patients with sagittal synostosis and a normal cranial index (CI), a condition termed normocephalic sagittal craniosynostosis (NSC). The goal of this study is to evaluate the shape and intracranial volume (ICV) in a cohort of NSC patients using quantitative cranial shape analysis (CSA). METHODS: We identified 19 patients (7.5 ±â€Š2.28 years) between 2011 and 2016, who presented to our hospital with NSC. Cranial index and CSA were measured from the computed tomography image. Cranial shape analysis calculates the distances between the patient's cranial shape and its closest normal shape. Intracranial volume was measured and compared to an established age-matched normative database. RESULTS: Cranial index revealed 15 (78.9%) patients within the mesocephalic range and 4 patients (21.1%) in the brachycephalic range. Detailed CSA identified 15 (78.9%) patients with subtle phenotypic changes along the scaphocephalic spectrum (ie, subtle anterior and posterior elongation with inter-parietal narrowing) and 1 patient (5.3%) with isolated overdevelopment on the posterior part of the right parietal bone. Three patients (15.8%) had a CSA close to normal. Mean ICV was 1410.5 ±â€Š192.77cc; most patients (78.9%) fell within ±2 standard deviations. CONCLUSION: Quantitative CSA revealed that most of the patients with NSC had cranial shape abnormalities, consistent with a forme fruste scaphocephaly that could not be otherwise recognized by clinical observation or CI. Given these findings, we propose the term occult scaphocephaly to describe this condition. The associated incidence of intracranial hypertension is unknown.

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. The first CUS with IVH was identified and a slice in the coronal plane was selected. The frontal horns of the lateral ventricles were manually segmented. Automated quantitative morphological features were extracted from both lateral ventricles. Predictive models of the need of temporizing intervention were compared. RESULTS: Sixty-two newborns met inclusion criteria. Fifteen out of the 62 had a temporizing intervention. The morphological features had a better accuracy predicting temporizing interventions when compared to clinical variables: 0.94 versus 0.85, respectively; p < 0.01 for both. By considering both morphological and clinical variables, our method predicts the need of temporizing intervention with positive and negative predictive values of 0.83 and 1, respectively, and accuracy of 0.97. CONCLUSION: Early cranial ultrasound-based quantitative ventricular evaluation in premature newborns can predict the eventual use of a temporizing intervention to treat post-hemorrhagic hydrocephalus. This may be helpful for early monitoring and treatment.

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.

Down syndrome in diverse populations.

Down syndrome is the most common cause of cognitive impairment and presents clinically with universally recognizable signs and symptoms. In this study, we focus on exam findings and digital facial analysis technology in individuals with Down syndrome in diverse populations. Photos and clinical information were collected on 65 individuals from 13 countries, 56.9% were male and the average age was 6.6 years (range 1 month to 26 years; SD = 6.6 years). Subjective findings showed that clinical features were different across ethnicities (Africans, Asians, and Latin Americans), including brachycephaly, ear anomalies, clinodactyly, sandal gap, and abundant neck skin, which were all significantly less frequent in Africans (P < 0.001, P < 0.001, P < 0.001, P < 0.05, and P < 0.05, respectively). Evaluation using a digital facial analysis technology of a larger diverse cohort of newborns to adults (n = 129 cases; n = 132 controls) was able to diagnose Down syndrome with a sensitivity of 0.961, specificity of 0.924, and accuracy of 0.943. Only the angles at medial canthus and ala of the nose were common significant findings amongst different ethnicities (Caucasians, Africans, and Asians) when compared to ethnically matched controls. The Asian group had the least number of significant digital facial biometrics at 4, compared to Caucasians at 8 and Africans at 7. In conclusion, this study displays the wide variety of findings across different geographic populations in Down syndrome and demonstrates the accuracy and promise of digital facial analysis technology in the diagnosis of Down syndrome internationally. © 2016 Wiley Periodicals, Inc.

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.

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. Machine learning techniques were then applied to identify ultrasound based safety thresholds that agreed with the t½ for washout. A best fit model was then derived for each threshold level of t½ that would be clinically relevant at 20, 30 and 40 minutes. Receiver operating characteristic curve analysis was performed. Sensitivity, specificity and area under the receiver operating characteristic curve were determined. Improvement obtained by the quantitative imaging method compared to the Society for Fetal Urology grading system and the hydronephrosis index was statistically verified. RESULTS: For the 3 thresholds considered and at 100% sensitivity the specificities of the quantitative imaging method were 94%, 70% and 74%, respectively. Corresponding area under the receiver operating characteristic curve values were 0.98, 0.94 and 0.94, respectively. Improvement obtained by the quantitative imaging method over the Society for Fetal Urology grade and hydronephrosis index was statistically significant (p <0.05 in all cases). CONCLUSIONS: Quantitative imaging analysis of renal sonograms in children with hydronephrosis can identify thresholds of clinically significant washout times with 100% sensitivity to decrease the number of diuretic renograms in up to 62% of children.

Optimizing area under the ROC curve using semi-supervised learning.

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.

Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation.

BACKGROUND: Identification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms. METHODS AND RESULTS: We used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (P value=0.83) and a 9-feature mitral regurgitation analysis showed an area under the receiver operating characteristics curve of 0.93, precision of 0.83, recall of 0.92, and F1 score of 0.87. Our deep learning model showed an area under the receiver operating characteristics curve of 0.84, precision of 0.78, recall of 0.98, and F1 score of 0.87. CONCLUSIONS: Artificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.

CAFES: Chest X-ray Analysis using Federated Self-supervised Learning for Pediatric COVID-19 Detection.

Chest X-rays (CXRs) play a pivotal role in cost-effective clinical assessment of various heart and lung related conditions. The urgency of COVID-19 diagnosis prompted their use in identifying conditions like lung opacity, pneumonia, and acute respiratory distress syndrome in pediatric patients. We propose an AI-driven solution for binary COVID-19 versus non-COVID-19 classification in pediatric CXRs. We present a Federated Self-Supervised Learning (FSSL) framework to enhance Vision Transformer (ViT) performance for COVID-19 detection in pediatric CXRs. ViT's prowess in vision-related binary classification tasks, combined with self-supervised pre-training on adult CXR data, forms the basis of the FSSL approach. We implement our strategy on the Rhino Health Federated Computing Platform (FCP), which ensures privacy and scalability for distributed data. The chest X-ray analysis using the federated SSL (CAFES) model, utilizes the FSSL-pre-trained ViT weights and demonstrated gains in accurately detecting COVID-19 when compared with a fully supervised model. Our FSSL-pre-trained ViT showed an area under the precision-recall curve (AUPR) of 0.952, which is 0.231 points higher than the fully supervised model for COVID-19 diagnosis using pediatric data. Our contributions include leveraging vision transformers for effective COVID-19 diagnosis from pediatric CXRs, employing distributed federated learning-based self-supervised pre-training on adult data, and improving pediatric COVID-19 diagnosis performance. This privacy-conscious approach aligns with HIPAA guidelines, paving the way for broader medical imaging applications.

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study.

Background: Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS). Methods: We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, T2 FLAIR) and manual segmentations from two centers of 53 (internal cohort) and 16 (external cohort) DMG patients. We pretrained a deep learning model on a public adult brain tumor dataset, and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 1-year survival from diagnosis. One model used only diagnostic tumor features and the other used both diagnostic and post-RT features. Results: For segmentation, Dice score (mean [median]±SD) was 0.91 (0.94)±0.12 and 0.74 (0.83)±0.32 for TC, and 0.88 (0.91)±0.07 and 0.86 (0.89)±0.06 for WT for internal and external cohorts, respectively. For OS prediction, accuracy was 77% and 81% at time of diagnosis, and 85% and 78% post-RT for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS. Conclusions: Machine learning analysis of MRI radiomics has potential to accurately and non-invasively predict which pediatric patients with DMG will survive less than one year from the time of diagnosis to provide patient stratification and guide therapy.

Whole-lesion assessment of volume and signal changes after sclerotherapy of extremity venous malformations.

PURPOSE: To investigate quantitative changes in MRI signal intensity (SI) and lesion volume that indicate treatment response and correlate these changes with clinical outcomes after percutaneous sclerotherapy (PS) of extremity venous malformations (VMs). METHODS: VMs were segmented manually on pre- and post-treatment T2-weighted MRI using 3D Slicer to assess changes in lesion volume and SI. Clinical outcomes were scored on a 7-point Likert scale according to patient perception of symptom improvement; treatment response (success or failure) was determined accordingly. RESULTS: Eighty-one patients with VMs underwent 125 PS sessions. Treatment success occurred in 77 patients (95 %). Mean (±SD) changes were -7.9 ± 24 cm3 in lesion volume and -123 ± 162 in SI (both, P <.001). Mean reduction in lesion volume was greater in the success group (-9.4 ± 24 cm3) than in the failure group (21 ± 20 cm3) (P =.006). Overall, lesion volume correlated with treatment response (ρ = -0.3, P =.004). On subgroup analysis, volume change correlated with clinical outcomes in children (ρ = -0.3, P =.03), in sodium tetradecyl sulfate-treated lesions (ρ = -0.5, P =.02), and in foot lesions (ρ = -0.6, P =.04). SI change correlated with clinical outcomes in VMs treated in 1 PS session (ρ = -0.3, P =.01) and in bleomycin-treated lesions (ρ = -0.4, P =.04). CONCLUSIONS: Change in lesion volume is a reliable indicator of treatment response. Lesion volume and SI correlate with clinical outcomes in specific subgroups.

Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.

The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Keywords: Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.

Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: A two-center study.

Background: Diffuse midline gliomas (DMG) are aggressive pediatric brain tumors that are diagnosed and monitored through MRI. We developed an automatic pipeline to segment subregions of DMG and select radiomic features that predict patient overall survival (OS). Methods: We acquired diagnostic and post-radiation therapy (RT) multisequence MRI (T1, T1ce, T2, and T2 FLAIR) and manual segmentations from 2 centers: 53 from 1 center formed the internal cohort and 16 from the other center formed the external cohort. We pretrained a deep learning model on a public adult brain tumor data set (BraTS 2021), and finetuned it to automatically segment tumor core (TC) and whole tumor (WT) volumes. PyRadiomics and sequential feature selection were used for feature extraction and selection based on the segmented volumes. Two machine learning models were trained on our internal cohort to predict patient 12-month survival from diagnosis. One model used only data obtained at diagnosis prior to any therapy (baseline study) and the other used data at both diagnosis and post-RT (post-RT study). Results: Overall survival prediction accuracy was 77% and 81% for the baseline study, and 85% and 78% for the post-RT study, for internal and external cohorts, respectively. Homogeneous WT intensity in baseline T2 FLAIR and larger post-RT TC/WT volume ratio indicate shorter OS. Conclusions: Machine learning analysis of MRI radiomics has potential to accurately and noninvasively predict which pediatric patients with DMG will survive less than 12 months from the time of diagnosis to provide patient stratification and guide therapy.

Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface.

An accurate classification of upper limb movements using electroencephalogram (EEG) signals is gaining significant importance in recent years due to the prevalence of brain-computer interfaces. The upper limbs in the human body are crucial since different skeletal segments combine to make a range of motions that helps us in our trivial daily tasks. Decoding EEG-based upper limb movements can be of great help to people with spinal cord injury (SCI) or other neuro-muscular diseases such as amyotrophic lateral sclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This can manifest in a loss of sensory and motor function, which could make a person reliant on others to provide care in day-to-day activities. We can detect and classify upper limb movement activities, whether they be executed or imagined using an EEG-based brain-computer interface (BCI). Toward this goal, we focus our attention on decoding movement execution (ME) of the upper limb in this study. For this purpose, we utilize a publicly available EEG dataset that contains EEG signal recordings from fifteen subjects acquired using a 61-channel EEG device. We propose a method to classify four ME classes for different subjects using spectrograms of the EEG data through pre-trained deep learning (DL) models. Our proposed method of using EEG spectrograms for the classification of ME has shown significant results, where the highest average classification accuracy (for four ME classes) obtained is 87.36%, with one subject achieving the best classification accuracy of 97.03%.Clinical relevance- This research shows that movement execution of upper limbs is classified with significant accuracy by employing a spectrogram of the EEG signals and a pre-trained deep learning model which is fine-tuned for the downstream task.

Erratum: Toward quantitative assessment of deformational plagiocephaly and brachycephaly at the point-of-care.

[This corrects the article DOI: 10.1117/1.JMI.8.2.024504.].

Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.

BACKGROUND AND OBJECTIVE: Accurate and repeatable detection of craniofacial landmarks is crucial for automated quantitative evaluation of head development anomalies. Since traditional imaging modalities are discouraged in pediatric patients, 3D photogrammetry has emerged as a popular and safe imaging alternative to evaluate craniofacial anomalies. However, traditional image analysis methods are not designed to operate on unstructured image data representations such as 3D photogrammetry. METHODS: We present a fully automated pipeline to identify craniofacial landmarks in real time, and we use it to assess the head shape of patients with craniosynostosis using 3D photogrammetry. To detect craniofacial landmarks, we propose a novel geometric convolutional neural network based on Chebyshev polynomials to exploit the point connectivity information in 3D photogrammetry and quantify multi-resolution spatial features. We propose a landmark-specific trainable scheme that aggregates the multi-resolution geometric and texture features quantified at every vertex of a 3D photogram. Then, we embed a new probabilistic distance regressor module that leverages the integrated features at every point to predict landmark locations without assuming correspondences with specific vertices in the original 3D photogram. Finally, we use the detected landmarks to segment the calvaria from the 3D photograms of children with craniosynostosis, and we derive a new statistical index of head shape anomaly to quantify head shape improvements after surgical treatment. RESULTS: We achieved an average error of 2.74 ± 2.70 mm identifying Bookstein Type I craniofacial landmarks, which is a significant improvement compared to other state-of-the-art methods. Our experiments also demonstrated a high robustness to spatial resolution variability in the 3D photograms. Finally, our head shape anomaly index quantified a significant reduction of head shape anomalies as a consequence of surgical treatment. CONCLUSION: Our fully automated framework provides real-time craniofacial landmark detection from 3D photogrammetry with state-of-the-art accuracy. In addition, our new head shape anomaly index can quantify significant head phenotype changes and can be used to quantitatively evaluate surgical treatment in patients with craniosynostosis.

Data-driven cranial suture growth model enables predicting phenotypes of craniosynostosis.

We present the first data-driven pediatric model that explains cranial sutural growth in the pediatric population. We segmented the cranial bones in the neurocranium from the cross-sectional CT images of 2068 normative subjects (age 0-10 years), and we used a 2D manifold-based cranial representation to establish local anatomical correspondences between subjects guided by the location of the cranial sutures. We designed a diffeomorphic spatiotemporal model of cranial bone development as a function of local sutural growth rates, and we inferred its parameters statistically from our cross-sectional dataset. We used the constructed model to predict growth for 51 independent normative patients who had longitudinal images. Moreover, we used our model to simulate the phenotypes of single suture craniosynostosis, which we compared to the observations from 212 patients. We also evaluated the accuracy predicting personalized cranial growth for 10 patients with craniosynostosis who had pre-surgical longitudinal images. Unlike existing statistical and simulation methods, our model was inferred from real image observations, explains cranial bone expansion and displacement as a consequence of sutural growth and it can simulate craniosynostosis. This pediatric cranial suture growth model constitutes a necessary tool to study abnormal development in the presence of cranial suture pathology.

Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding.

Image segmentation, labeling, and landmark detection are essential tasks for pediatric craniofacial evaluation. Although deep neural networks have been recently adopted to segment cranial bones and locate cranial landmarks from computed tomography (CT) or magnetic resonance (MR) images, they may be hard to train and provide suboptimal results in some applications. First, they seldom leverage global contextual information that can improve object detection performance. Second, most methods rely on multi-stage algorithm designs that are inefficient and prone to error accumulation. Third, existing methods often target simple segmentation tasks and have shown low reliability in more challenging scenarios such as multiple cranial bone labeling in highly variable pediatric datasets. In this paper, we present a novel end-to-end neural network architecture based on DenseNet that incorporates context regularization to jointly label cranial bone plates and detect cranial base landmarks from CT images. Specifically, we designed a context-encoding module that encodes global context information as landmark displacement vector maps and uses it to guide feature learning for both bone labeling and landmark identification. We evaluated our model on a highly diverse pediatric CT image dataset of 274 normative subjects and 239 patients with craniosynostosis (age 0.63 ± 0.54 years, range 0-2 years). Our experiments demonstrate improved performance compared to state-of-the-art approaches.

Automatic Visual Acuity Loss Prediction in Children with Optic Pathway Gliomas using Magnetic Resonance Imaging.

Children with optic pathway gliomas (OPGs), a low-grade brain tumor associated with neurofibromatosis type 1 (NF1-OPG), are at risk for permanent vision loss. While OPG size has been associated with vision loss, it is unclear how changes in size, shape, and imaging features of OPGs are associated with the likelihood of vision loss. This paper presents a fully automatic framework for accurate prediction of visual acuity loss using multi-sequence magnetic resonance images (MRIs). Our proposed framework includes a transformer-based segmentation network using transfer learning, statistical analysis of radiomic features, and a machine learning method for predicting vision loss. Our segmentation network was evaluated on multi-sequence MRIs acquired from 75 pediatric subjects with NF1-OPG and obtained an average Dice similarity coefficient of 0.791. The ability to predict vision loss was evaluated on a subset of 25 subjects with ground truth using cross-validation and achieved an average accuracy of 0.8. Analyzing multiple MRI features appear to be good indicators of vision loss, potentially permitting early treatment decisions.Clinical relevance- Accurately determining which children with NF1-OPGs are at risk and hence require preventive treatment before vision loss remains challenging, towards this we present a fully automatic deep learning-based framework for vision outcome prediction, potentially permitting early treatment decisions.