Unusual presentation of Sturge-Weber syndrome: Progressive megalencephaly with bilateral cutaneous and cortical involvement

The Sturge Weber syndrome is characterized by developmental delay, seizures in infancy, unilateral cutaneous lesions with ipsilateral leptomeningeal enhancement. We report an unusual presentation of Sturge Weber syndrome with bilateral port wine nevus on the trunk and face along with bilateral cortical involvement in a developmentally normal child with progressive megalencephaly.     

Determination of Non-Recrystallization Temperature for Niobium Microalloyed Steel

In the present investigation, the non-recrystallization temperature (T_NR) of niobium-microalloyed steel is determined to plan rolling schedules for obtaining the desired properties of steel. The value of T_NR is based on both alloying elements and deformation parameters. In the literature, T_NR equations have been developed and utilized. However, each equation has certain limitations which constrain its applicability. This study was completed using laboratory-grade low-carbon Nb-microalloyed steels designed to meet the API X-70 specification. Nb- microalloyed steel is processed by the melting and casting process, and the composition is found by optical emission spectroscopy (OES). Multiple-hit deformation tests were carried out on a Gleeble^® 3500 system in the standard pocket-jaw configuration to determine T_NR. Cuboidal specimens (10 (L) × 20 (W) × 20 (T) mm³) were taken for compression test (multiple-hit deformation tests) in gleeble. Microstructure evolutions were carried out by using OM (optical microscopy) and SEM (scanning electron microscopy). The value of T_NR determined for 0.1 wt.% niobium bearing microalloyed steel is ~ 951 °C. Nb- microalloyed steel rolled at T_NR produce partially recrystallized grain with ferrite nucleation. Hence, to verify the TNR value, a rolling process is applied with the finishing rolling temperature near TNR (~951 °C). The microstructure is also revealed in the pancake shape, which confirms T_NR.     

Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis

Glaucoma is a progressive and deteriorating optic neuropathy that leads to visual field defects. The damage occurs as glaucoma is irreversible, so early and timely diagnosis is of significant importance. The proposed system employs the convolution neural network (CNN) for automatic segmentation of the retinal layers. The inner limiting membrane (ILM) and retinal pigmented epithelium (RPE) are used to calculate cup-to-disc ratio (CDR) for glaucoma diagnosis. The proposed system uses structure tensors to extract candidate layer pixels, and a patch across each candidate layer pixel is extracted, which is classified using CNN. The proposed framework is based upon VGG-16 architecture for feature extraction and classification of retinal layer pixels. The output feature map is merged into SoftMax layer for classification and produces probability map for central pixel of each patch and decides whether it is ILM, RPE, or background pixels. Graph search theory refines the extracted layers by interpolating the missing points, and these extracted ILM and RPE are finally used to compute CDR value and diagnose glaucoma. The proposed system is validated using a local dataset of optical coherence tomography images from 196 patients, including normal and glaucoma subjects. The dataset contains manually annotated ILM and RPE layers; manually extracted patches for ILM, RPE, and background pixels; CDR values; and eventually final finding related to glaucoma. The proposed system is able to extract ILM and RPE with a small absolute mean error of 6.03 and 5.56, respectively, and it finds CDR value within average range of ± 0.09 as compared with glaucoma expert. The proposed system achieves average sensitivity, specificity, and accuracies of 94.6, 94.07, and 94.68, respectively.