Purpose To propose automatic segmentation algorithm (AUS) for corneal microlayers on optical coherence tomography (OCT) images. Methods Eighty-two corneal OCT scans were obtained from 45 patients with normal and abnormal corneas. Three testing data sets totaling 75 OCT images were randomly selected. Initially, corneal epithelium and endothelium microlayers are estimated using a corneal mask and locally refined to obtain final segmentation. Flat-epithelium and flat-endothelium images are obtained and vertically projected to locate inner corneal microlayers. Inner microlayers are estimated by translating epithelium and endothelium microlayers to detected locations then refined to obtain final segmentation. Images were segmented by trained manual operators (TMOs) and by the algorithm to assess repeatability (i.e., intraoperator error), reproducibility (i.e., interoperator and segmentation errors), and running time. A random masked subjective test was conducted by corneal specialists to subjectively grade the segmentation algorithm. Results Compared with the TMOs, the AUS had significantly less mean intraoperator error (0.53 ± 1.80 vs. 2.32 ± 2.39 pixels; P < 0.0001), it had significantly different mean segmentation error (3.44 ± 3.46 vs. 2.93 ± 3.02 pixels; P < 0.0001), and it had significantly less running time per image (0.19 ± 0.07 vs. 193.95 ± 194.53 seconds; P < 0.0001). The AUS had insignificant subjective grading for microlayer-segmentation grading (4.94 ± 0.32 vs. 4.96 ± 0.24; P = 0.5081), but it had significant subjective grading for regional-segmentation grading (4.96 ± 0.26 vs. 4.79 ± 0.60; P = 0.025). Conclusions The AUS can reproduce the manual segmentation of corneal microlayers with comparable accuracy in almost real-time and with significantly better repeatability. Translational Relevance The AUS can be useful in clinical settings and can aid the diagnosis of corneal diseases by measuring thickness of segmented corneal microlayers.

Purpose. This case series demonstrate diagnostic features, treatment options, and challenges for Brittle Cornea Syndrome. Observations. Three cases presented with bluish sclera and extremely thin cornea. Genetic workup was performed and confirmed the diagnosis of Brittle Cornea Syndrome, a rare autosomal recessive disorder characterized by corneal thinning and blue sclera. Case 1 was a 4-year-old boy who developed cataract and glaucoma after undergoing right tectonic penetrating keratoplasty (PK) secondary to a spontaneous corneal rupture. Glaucoma was controlled medically. Later, the kid underwent right transcorneal lensectomy and vitrectomy with synechiolysis. After 6 weeks, he sustained graft dehiscence that was repaired using onlay patch graft. Case 2 was a 7-year-old boy who underwent PK in the right eye, then a pericardial patch graft in the left eye following spontaneous corneal rupture. Glaucoma in both eyes was controlled medically. Case 3 was the 2-year-old sister of the 2nd case. She had a pachymetry of 238 μm OD and 254 μm OS. In the 3 cases, parents were instructed to take protective measures for both eyes and to continue with follow-up visits. Also, they were instructed to have regular screenings for late-onset hearing loss, dental abnormalities, and bone deformities. Conclusions. Long-term follow-up of children diagnosed with Brittle Cornea Syndrome is paramount to minimize the morbidity of corneal rupture and late-onset extraocular conditions.

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The main aim of this paper is to propose Cubic Spline-Quantum Neural Network (CS-QNN) model for analysis and classification of Electroencephalogram (EEG) signals. Experimental data used here were taken from seven different electrodes. The work has been done in three stages, normalization of the signals, extracting the features by Cubic Spline Technique (CST) and classification using Quantum Neural Network (QNN). The simulation results showed that five types of EEG signals were classified with an average accuracy for seven electrodes that is 94.3% when training 70% of the features while with an average accuracy of 92.84% when training 50% of the features.

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