Infection diagnosis in hydrocephalus CT images: a domain enriched attention learning approach.

Objective. Hydrocephalus is the leading indication for pediatric neurosurgical care worldwide. Identification of postinfectious hydrocephalus (PIH) verses non-postinfectious hydrocephalus, as well as the pathogen involved in PIH is crucial for developing an appropriate treatment plan. Accurate identification requires clinical diagnosis by neuroscientists and microbiological analysis, which are time-consuming and expensive. In this study, we develop a domain enriched AI method for computerized tomography (CT)-based infection diagnosis in hydrocephalic imagery. State-of-the-art (SOTA) convolutional neural network (CNN) approaches form an attractive neural engineering solution for addressing this problem as pathogen-specific features need discovery. Yet black-box deep networks often need unrealistic abundant training data and are not easily interpreted.Approach. In this paper, a novel brain attention regularizer is proposed, which encourages the CNN to put more focus inside brain regions in its feature extraction and decision making. Our approach is then extended to a hybrid 2D/3D network that mines inter-slice information. A new strategy of regularization is also designed for enabling collaboration between 2D and 3D branches.Main results. Our proposed method achieves SOTA results on a CURE Children's Hospital of Uganda dataset with an accuracy of 95.8% in hydrocephalus classification and 84% in pathogen classification. Statistical analysis is performed to demonstrate that our proposed methods obtain significant improvements over the existing SOTA alternatives.Significance. Such attention regularized learning has particularly pronounced benefits in regimes where training data may be limited, thereby enhancing generalizability. To the best of our knowledge, our findings are unique among early efforts in interpretable AI-based models for classification of hydrocephalus and underlying pathogen using CT scans.

Preoperative risk and postoperative outcome from subdural fluid collections in African infants with postinfectious hydrocephalus.

OBJECTIVE: This study investigated the incidence of postoperative subdural collections in a cohort of African infants with postinfectious hydrocephalus. The authors sought to identify preoperative factors associated with increased risk of development of subdural collections and to characterize associations between subdural collections and postoperative outcomes. METHODS: The study was a post hoc analysis of a randomized controlled trial at a single center in Mbale, Uganda, involving infants (age < 180 days) with postinfectious hydrocephalus randomized to receive either an endoscopic third ventriculostomy plus choroid plexus cauterization or a ventriculoperitoneal shunt. Patients underwent assessment with the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III; sometimes referred to as BSID-III) and CT scans preoperatively and then at 6, 12, and 24 months postoperatively. Volumes of brain, CSF, and subdural fluid were calculated, and z-scores from the median were determined from normative curves for CSF accumulation and brain growth. Linear and logistic regression models were used to characterize the association between preoperative CSF volume and the postoperative presence and size of subdural collection 6 and 12 months after surgery. Linear regression and smoothing spline ANOVA were used to describe the relationship between subdural fluid volume and cognitive scores. Causal mediation analysis distinguished between the direct and indirect effects of the presence of a subdural collection on cognitive scores. RESULTS: Subdural collections were more common in shunt-treated patients and those with larger preoperative CSF volumes. Subdural fluid volumes were linearly related to preoperative CSF volumes. In terms of outcomes, the Bayley-III cognitive score was linearly related to subdural fluid volume. The distribution of cognitive scores was significantly different for patients with and those without subdural collections from 11 to 24 months of age. The presence of a subdural collection was associated with lower cognitive scores and smaller brain volume 12 months after surgery. Causal mediation analysis demonstrated evidence supporting both a direct (76%) and indirect (24%) effect (through brain volume) of subdural collections on cognitive scores. CONCLUSIONS: Larger preoperative CSF volume and shunt surgery were found to be risk factors for postoperative subdural collection. The size and presence of a subdural collection were negatively associated with cognitive outcomes and brain volume 12 months after surgery. These results have suggested that preoperative CSF volumes could be used for risk stratification for treatment decision-making and that future clinical trials of alternative shunt technologies to reduce overdrainage should be considered.

Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus.

As low-field MRI technology is being disseminated into clinical settings around the world, it is important to assess the image quality required to properly diagnose and treat a given disease and evaluate the role of machine learning algorithms, such as deep learning, in the enhancement of lower quality images. In this post hoc analysis of an ongoing randomized clinical trial, we assessed the diagnostic utility of reduced-quality and deep learning enhanced images for hydrocephalus treatment planning. CT images of post-infectious infant hydrocephalus were degraded in terms of spatial resolution, noise, and contrast between brain and CSF and enhanced using deep learning algorithms. Both degraded and enhanced images were presented to three experienced pediatric neurosurgeons accustomed to working in low- to middle-income countries (LMIC) for assessment of clinical utility in treatment planning for hydrocephalus. In addition, enhanced images were presented alongside their ground-truth CT counterparts in order to assess whether reconstruction errors caused by the deep learning enhancement routine were acceptable to the evaluators. Results indicate that image resolution and contrast-to-noise ratio between brain and CSF predict the likelihood of an image being characterized as useful for hydrocephalus treatment planning. Deep learning enhancement substantially increases contrast-to-noise ratio improving the apparent likelihood of the image being useful; however, deep learning enhancement introduces structural errors which create a substantial risk of misleading clinical interpretation. We find that images with lower quality than is customarily acceptable can be useful for hydrocephalus treatment planning. Moreover, low quality images may be preferable to images enhanced with deep learning, since they do not introduce the risk of misleading information which could misguide treatment decisions. These findings advocate for new standards in assessing acceptable image quality for clinical use.

Normal childhood brain growth and a universal sex and anthropomorphic relationship to cerebrospinal fluid.

OBJECTIVE: The study of brain size and growth has a long and contentious history, yet normal brain volume development has yet to be fully described. In particular, the normal brain growth and cerebrospinal fluid (CSF) accumulation relationship is critical to characterize because it is impacted in numerous conditions of early childhood in which brain growth and fluid accumulation are affected, such as infection, hemorrhage, hydrocephalus, and a broad range of congenital disorders. The authors of this study aim to describe normal brain volume growth, particularly in the setting of CSF accumulation. METHODS: The authors analyzed 1067 magnetic resonance imaging scans from 505 healthy pediatric subjects from birth to age 18 years to quantify component and regional brain volumes. The volume trajectories were compared between the sexes and hemispheres using smoothing spline ANOVA. Population growth curves were developed using generalized additive models for location, scale, and shape. RESULTS: Brain volume peaked at 10-12 years of age. Males exhibited larger age-adjusted total brain volumes than females, and body size normalization procedures did not eliminate this difference. The ratio of brain to CSF volume, however, revealed a universal age-dependent relationship independent of sex or body size. CONCLUSIONS: These findings enable the application of normative growth curves in managing a broad range of childhood diseases in which cognitive development, brain growth, and fluid accumulation are interrelated.

Brain growth after surgical treatment for infant postinfectious hydrocephalus in Sub-Saharan Africa: 2-year results of a randomized trial.

OBJECTIVE: Hydrocephalus in infants, particularly that with a postinfectious etiology, is a major public health burden in Sub-Saharan Africa. The authors of this study aimed to determine whether surgical treatment of infant postinfectious hydrocephalus in Uganda results in sustained, long-term brain growth and improved cognitive outcome. METHODS: The authors performed a trial at a single center in Mbale, Uganda, involving infants (age < 180 days old) with postinfectious hydrocephalus randomized to endoscopic third ventriculostomy plus choroid plexus cauterization (ETV+CPC; n = 51) or ventriculoperitoneal shunt (VPS; n = 49). After 2 years, they assessed developmental outcome with the Bayley Scales of Infant Development, Third Edition (BSID-III), and brain volume (raw and normalized for age and sex) with CT scans. RESULTS: Eighty-nine infants were assessed for 2-year outcome. There were no significant differences between the two surgical treatment arms in terms of BSID-III cognitive score (p = 0.17) or brain volume (p = 0.36), so they were analyzed together. Raw brain volumes increased between baseline and 2 years (p < 0.001), but this increase occurred almost exclusively in the 1st year (p < 0.001). The fraction of patients with a normal brain volume increased from 15.2% at baseline to 50.0% at 1 year but then declined to 17.8% at 2 years. Substantial normalized brain volume loss was seen in 21.3% patients between baseline and year 2 and in 76.7% between years 1 and 2. The extent of brain growth in the 1st year was not associated with the extent of brain volume changes in the 2nd year. There were significant positive correlations between 2-year brain volume and all BSID-III scores and BSID-III changes from baseline. CONCLUSIONS: In Sub-Saharan Africa, even after successful surgical treatment of infant postinfectious hydrocephalus, early posttreatment brain growth stagnates in the 2nd year. While the reasons for this finding are unclear, it further emphasizes the importance of primary infection prevention and mitigation strategies along with optimizing the child's environment to maximize brain growth potential.

Deep Retinal Image Segmentation with Regularization Under Geometric Priors.

Vessel segmentation of retinal images is a key diagnostic capability in ophthalmology. This problem faces several challenges including low contrast, variable vessel size and thickness, and presence of interfering pathology such as micro-aneurysms and hemorrhages. Early approaches addressing this problem employed hand-crafted filters to capture vessel structures, accompanied by morphological post-processing. More recently, deep learning techniques have been employed with significantly enhanced segmentation accuracy. We propose a novel domain enriched deep network that consists of two components: 1) a representation network that learns geometric features specific to retinal images, and 2) a custom designed computationally efficient residual task network that utilizes the features obtained from the representation layer to perform pixel-level segmentation. The representation and task networks are jointly learned for any given training set. To obtain physically meaningful and practically effective representation filters, we propose two new constraints that are inspired by expected prior structure on these filters: 1) orientation constraint that promotes geometric diversity of curvilinear features, and 2) a data adaptive noise regularizer that penalizes false positives. Multi-scale extensions are developed to enable accurate detection of thin vessels. Experiments performed on three challenging benchmark databases under a variety of training scenarios show that the proposed prior guided deep network outperforms state of the art alternatives as measured by common evaluation metrics, while being more economical in network size and inference time.

Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors.

High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware and processing constraints. Recently, deep learning methods have been shown to produce compelling state-of-the-art results for image enhancement/super-resolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image super-resolution (SR). Our contributions are then incorporating these priors in an analytically tractable fashion as well as towards a novel prior guided network architecture that accomplishes the super-resolution task. This is particularly challenging for the low rank prior since the rank is not a differentiable function of the image matrix (and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. As a key extension, we modify the fixed feedback (Laplacian) layer by learning a new set of training data driven filters that are optimized for enhanced sharpness. Experiments performed on publicly available MR brain image databases and comparisons against existing state-of-the-art methods show that the proposed prior guided network offers significant practical gains in terms of improved SNR/image quality measures. Because our priors are on output images, the proposed method is versatile and can be combined with a wide variety of existing network architectures to further enhance their performance.

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.

DEEP MR IMAGE SUPER-RESOLUTION USING STRUCTURAL PRIORS.

High resolution magnetic resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware, cost and processing constraints. Recently, deep learning methods have been shown to produce compelling state of the art results for image superresolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image superresolution. Our contributions are then incorporating these priors in an analytically tractable fashion in the learning of a convolutional neural network (CNN) that accomplishes the super-resolution task. This is particularly challenging for the low rank prior, since the rank is not a differentiable function of the image matrix (and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. Experiments performed on two publicly available MR brain image databases exhibit promising results particularly when training imagery is limited.

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 .).