Color space distortions in patients with type 2 diabetes mellitus

Color vision impairment was examined in patients with type 2 diabetes mellitus (DM2) without retinopathy. We assessed the type and degree of distortions of individual color spaces. DM2 patients (n = 32), and age-matched controls (n = 20)were tested using the Farnsworth D-15 and the Lanthony D-15d tests. In addition, subsets of caps from both tests were employed in a triadic procedure (Bimler & Kirkland, 2004). Matrices of inter-cap subjective dissimilarities were estimated from each subject’s “odd-one-out” choices, and processed using non-metric multidimensional scaling. Two-dimensional color spaces, individual and group (DM2 patients; controls), were reconstructed, with the axes interpreted as the R0G and B0Y perceptual opponent systems. Compared to controls, patient results were not significant for the D-15 and D-15d. In contrast, in the triadic procedure the residual distances were significantly different compared to controls: right eye, P 0.021, and left eye, P 0.022. Color space configurations for the DM2 patients were compressed along the B0Y and R0G dimensions. The present findings agree with earlier studies demonstrating diffuse losses in early stages of DM2. The proposed method of testing uses color spaces to represent discrimination and provides more differentiated quantitative diagnosis, which may be interpreted as the perceptual color system affected. In addition, it enables the detection of very mild color vision impairment that is not captured by the D-15d test. Along with fundoscopy, individual color spaces may serve for monitoring early functional changes and thereby to support a treatment strategy

Context-based Normalization of Histological Stains using Deep Convolutional Features

While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to achieve peak performance, especially when a limited amount of labeled data is available. This work provides a fully automated, end-to-end learning-based setup for normalizing histological stains, which considers the texture context of the tissue. We introduce Feature Aware Normalization, which extends the framework of batch normalization in combination with gating elements from Long Short-Term Memory units for normalization among different spatial regions of interest. By incorporating a pretrained deep neural network as a feature extractor steering a pixelwise processing pipeline, we achieve excellent normalization results and ensure a consistent representation of color and texture. The evaluation comprises a comparison of color histogram deviations, structural similarity and measures the color volume obtained by the different methods.Comment: In: 3rd Workshop on Deep Learning in Medical Image Analysis (DLMIA 2017