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.)
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.
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/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 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 superresolution 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.
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