Region-adaptive probability model selection for the arithmetic coding of video texture

In video coding systems using adaptive arithmetic coding to compress texture information, the employed symbol probability models need to be retrained every time the coding process moves into an area with different texture. To avoid this inefficiency, we propose to replace the probability models used in the original coder with multiple switchable sets of probability models. We determine the model set to use in each spatial region in an optimal manner, taking into account the additional signaling overhead. Experimental results show that this approach, when applied to H. 264/AVC's context-based adaptive binary arithmetic coder (CABAC), yields significant bit-rate savings, which are comparable to or higher than those obtained using alternative improvements to CABAC previously proposed in the literature

Predicting cognitive development and early symptoms of autism spectrum disorder in preterm children : the value of temperament and sensory processing

This study was the first to longitudinally explore the extent to which early temperament and sensory processing were of predictive value for cognitive development and Autism Spectrum Disorder (ASD) symptomatology in a sample of preterm children (N= 50, 22 girls, mean gestational age 27 weeks). At the corrected ages of 10, 18, and 24 months, sensory processing and temperament were assessed, as were cognitive development and ASD symptoms at 36 months. Better cognitive development was predicted by fewer hospitalisation days at birth and by lower Activity Level at 18 months. Temperamental subscales of Negative Affect showed associations with both parent-reported and observational measures of ASD symptomatology, whereas sensory processing only had predictive value for parent-reported symptoms of ASD. The usefulness of temperament and sensory processing for prediction of ASD symptom severity and cognitive outcomes became clear in the second year of life. The results indicate that this area of research is worth additional investigation in the extreme and very preterm population, to explore in further detail whether these two concepts might be able to provide information about which preterms are more likely to develop ASD or cognitive impairments

Transcoding of H.264/AVC to SVC with motion data refinement

In this paper, we present motion-refined transcoding of H.264/AVC streams to SVC in the transform domain. By accurately taking into account both rate and distortion in the different layers on the one hand, and the SVC inter-layer motion prediction mechanisms on the other hand, the proposed transcoding architecture is able to improve rate-distortion performance over existing approaches. We propose a multilayer control mechanism that trades off performance between the different layers, resulting in 0.5 dB gains in the output SVC base layer

Dyadic spatial resolution reduction transcoding for H.264/AVC

In this paper, we examine spatial resolution downscaling transcoding for H.264/AVC video coding. A number of advanced coding tools limit the applicability of techniques, which were developed for previous video coding standards. We present a spatial resolution reduction transcoding architecture for H.264/AVC, which extends open-loop transcoding with a low-complexity compensation technique in the reduced-resolution domain. The proposed architecture tackles the problems in H.264/AVC and avoids visual artifacts in the transcoded sequence, while keeping complexity significantly lower than more traditional cascaded decoder-encoder architectures. The refinement step of the proposed architecture can be used to further improve rate-distortion performance, at the cost of additional complexity. In this way, a dynamic-complexity transcoder is rendered possible. We present a thorough investigation of the problems related to motion and residual data mapping, leading to a transcoding solution resulting in fully compliant reduced-size H.264/AVC bitstreams

Collimated backlight for liquid crystal displays

In this work, a collimated backlight design is presented. Traditionally backlights are either direct-lit in which an array of LEDs are directly behind the LC-panel, or edgelit, in which case there is a light guide behind the LC-panel and LEDs are placed at the side of this light guide. These designs have their advantages and disadvantages but in both cases they emit light under a wide angle

Sex Distributions in Non-ABCA4 Autosomal Macular Dystrophies

Purpose: We sought to explore whether sex imbalances are discernible in several autosomally inherited macular dystrophies. Methods: We searched the electronic patient records of our large inherited retinal disease cohort, quantifying numbers of males and females with the more common (non-ABCA4) inherited macular dystrophies (associated with BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3). BEST1 cases were subdivided into typical autosomal dominant and recessive disease. For PRPH2, only patients with variants at codons 172 or 142 were included. Recessive PROM1 and recessive RP1L1 cases were excluded because these variants give a more widespread or peripheral degeneration. The proportion of females was calculated for each condition; two-tailed binomial testing was performed. Where a significant imbalance was found, previously published cohorts were also explored. Results: Of 325 patients included, numbers for BEST1, EFEMP1, PROM1, PRPH2, RP1L1, and TIMP3 were 152, 35, 30, 50, 14, and 44, respectively. For autosomal dominant Best disease (n = 115), there were fewer females (38%; 95% confidence interval [CI], 29-48%; P = 0.015). For EFEMP1-associated disease (n = 35), there were significantly more females (77%; 95% CI, 60%-90%; P = 0.0019). No significant imbalances were seen for the other genes. When pooling our cohort with previous large dominant Best disease cohorts, the proportion of females was 37% (95% CI, 31%-43%; P = 1.2 × 10-5). Pooling previously published EFEMP1-cases with ours yielded an overall female proportion of 62% (95% CI, 54%-69%; P = 0.0023). Conclusions: This exploratory study found significant sex imbalances in two autosomal macular dystrophies, suggesting that sex could be a modifier. Our findings invite replication in further cohorts and the investigation of potential mechanisms

Validation and Clinical Applicability of Whole-Volume Automated Segmentation of Optical Coherence Tomography in Retinal Disease Using Deep Learning.

IMPORTANCE: Quantitative volumetric measures of retinal disease in optical coherence tomography (OCT) scans are infeasible to perform owing to the time required for manual grading. Expert-level deep learning systems for automatic OCT segmentation have recently been developed. However, the potential clinical applicability of these systems is largely unknown. OBJECTIVE: To evaluate a deep learning model for whole-volume segmentation of 4 clinically important pathological features and assess clinical applicability. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study used OCT data from 173 patients with a total of 15 558 B-scans, treated at Moorfields Eye Hospital. The data set included 2 common OCT devices and 2 macular conditions: wet age-related macular degeneration (107 scans) and diabetic macular edema (66 scans), covering the full range of severity, and from 3 points during treatment. Two expert graders performed pixel-level segmentations of intraretinal fluid, subretinal fluid, subretinal hyperreflective material, and pigment epithelial detachment, including all B-scans in each OCT volume, taking as long as 50 hours per scan. Quantitative evaluation of whole-volume model segmentations was performed. Qualitative evaluation of clinical applicability by 3 retinal experts was also conducted. Data were collected from June 1, 2012, to January 31, 2017, for set 1 and from January 1 to December 31, 2017, for set 2; graded between November 2018 and January 2020; and analyzed from February 2020 to November 2020. MAIN OUTCOMES AND MEASURES: Rating and stack ranking for clinical applicability by retinal specialists, model-grader agreement for voxelwise segmentations, and total volume evaluated using Dice similarity coefficients, Bland-Altman plots, and intraclass correlation coefficients. RESULTS: Among the 173 patients included in the analysis (92 [53%] women), qualitative assessment found that automated whole-volume segmentation ranked better than or comparable to at least 1 expert grader in 127 scans (73%; 95% CI, 66%-79%). A neutral or positive rating was given to 135 model segmentations (78%; 95% CI, 71%-84%) and 309 expert gradings (2 per scan) (89%; 95% CI, 86%-92%). The model was rated neutrally or positively in 86% to 92% of diabetic macular edema scans and 53% to 87% of age-related macular degeneration scans. Intraclass correlations ranged from 0.33 (95% CI, 0.08-0.96) to 0.96 (95% CI, 0.90-0.99). Dice similarity coefficients ranged from 0.43 (95% CI, 0.29-0.66) to 0.78 (95% CI, 0.57-0.85). CONCLUSIONS AND RELEVANCE: This deep learning-based segmentation tool provided clinically useful measures of retinal disease that would otherwise be infeasible to obtain. Qualitative evaluation was additionally important to reveal clinical applicability for both care management and research

Phenotyping of ABCA4 Retinopathy by Machine Learning Analysis of Full-Field Electroretinography

PURPOSE: Biallelic pathogenic variants in ABCA4 are the commonest cause of monogenic retinal disease. The full-field electroretinogram (ERG) quantifies severity of retinal dysfunction. We explored application of machine learning in ERG interpretation and in genotype–phenotype correlations. METHODS: International standard ERGs in 597 cases of ABCA4 retinopathy were classified into three functional phenotypes by human experts: macular dysfunction alone (group 1), or with additional generalized cone dysfunction (group 2), or both cone and rod dysfunction (group 3). Algorithms were developed for automatic selection and measurement of ERG components and for classification of ERG phenotype. Elastic-net regression was used to quantify severity of specific ABCA4 variants based on effect on retinal function. RESULTS: Of the cohort, 57.6%, 7.4%, and 35.0% fell into groups 1, 2, and 3 respectively. Compared with human experts, automated classification showed overall accuracy of 91.8% (SE, 0.169), and 96.7%, 39.3%, and 93.8% for groups 1, 2, and 3. When groups 2 and 3 were combined, the average holdout group accuracy was 93.6% (SE, 0.142). A regression model yielded phenotypic severity scores for the 47 commonest ABCA4 variants. CONCLUSIONS: This study quantifies prevalence of phenotypic groups based on retinal function in a uniquely large single-center cohort of patients with electrophysiologically characterized ABCA4 retinopathy and shows applicability of machine learning. Novel regression-based analyses of ABCA4 variant severity could identify individuals predisposed to severe disease. Translational Relevance: Machine learning can yield meaningful classifications of ERG data, and data-driven scoring of genetic variants can identify patients likely to benefit most from future therapies

Spectrum of genetic variants in the commonest genes causing inherited retinal disease in a large molecularly characterised UK cohort

PURPOSE: Inherited retinal disease (IRD) is a leading cause of blindness. Recent advances in gene-directed therapies highlight the importance of understanding the genetic basis of these disorders. This study details the molecular spectrum in a large UK IRD patient cohort. DESIGN: Retrospective study of electronic patient records. PARTICIPANTS: Patients with IRD who have attended the Genetics Service at Moorfields Eye Hospital between 2003 and July 2020, in whom a molecular diagnosis has been identified. METHODS: Genetic testing was undertaken via a combination of single-gene testing, gene panel testing, whole exome sequencing, and more recently, whole genome sequencing. Likely disease-causing variants were identified from entries within the genetics module of the hospital electronic patient record (OpenEyes Electronic Medical Record, UK). Analysis was restricted to only genes listed in the Genomics England PanelApp R32 Retinal disorders panel (Version 3.24), which includes 412 genes associated with IRD. Manual curation ensured consistent variant annotation and included only plausible disease-associated variants. MAIN OUTCOME MEASURES: Detailed analysis was performed for variants in the five most frequent genes (ABCA4, USH2A, RPGR, PRPH2, BEST1), as well as for the commonest variants encountered in the IRD study cohort. RESULTS: We identified 4415 individuals from 3953 families with molecularly diagnosed IRD (variants in 166 genes). 42.7% of families had variants in one of the five commonest IRD genes. Complex disease alleles contributed to disease in 16.9% of affected families with ABCA4-associated retinopathy. USH2A exon 13 variants were identified in 43% of affected individuals with USH2A-associated IRD. 71% of RPGR variants were clustered in the ORF15 region. PRPH2 and BEST1 variants were associated with a range of dominant and recessive IRD phenotypes. Of the 20 most prevalent variants identified, five were not in the commonest genes; these included founder variants in CNGB3, BBS1, TIMP3, EFEMP1 and RP1. CONCLUSIONS: We describe the commonest pathogenic IRD alleles in a large single-center multi-ethnic UK cohort, and the burden of disease, in terms of families affected, attributable to these variants. Our findings will inform IRD diagnoses in future patients and helps delineate the cohort of patients eligible for gene-directed therapies under development