Joint Segmentation and Uncertainty Visualization of Retinal Layers in Optical Coherence Tomography Images using Bayesian Deep Learning

Optical coherence tomography (OCT) is commonly used to analyze retinal layers for assessment of ocular diseases. In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated uncertainty map can be used to identify erroneously segmented image regions which is useful in downstream analysis. We have validated our method on a dataset of 1487 images obtained from 15 subjects (OCT volumes) and compared it against the state-of-the-art segmentation algorithms that does not take uncertainty into account. The proposed uncertainty based segmentation method results in comparable or improved performance, and most importantly is more robust against noise

Bio-inspired Attentive Segmentation of Retinal OCT Imaging

Albeit optical coherence imaging (OCT) is widely used to assess ophthalmic pathologies, localization of intra-retinal boundaries suffers from erroneous segmentations due to image artifacts or topological abnormalities. Although deep learning-based methods have been effectively applied in OCT imaging, accurate automated layer segmentation remains a challenging task, with the flexibility and precision of most methods being highly constrained. In this paper, we propose a novel method to segment all retinal layers, tailored to the bio-topological OCT geometry. In addition to traditional learning of shift-invariant features, our method learns in selected pixels horizontally and vertically, exploiting the orientation of the extracted features. In this way, the most discriminative retinal features are generated in a robust manner, while long-range pixel dependencies across spatial locations are efficiently captured. To validate the effectiveness and generalisation of our method, we implement three sets of networks based on different backbone models. Results on three independent studies show that our methodology consistently produces more accurate segmentations than state-of-the-art networks, and shows better precision and agreement with ground truth. Thus, our method not only improves segmentation, but also enhances the statistical power of clinical trials with layer thickness change outcomes

The Cell Cycle Time of CD8+ T Cells Responding In Vivo Is Controlled by the Type of Antigenic Stimulus

A hallmark of cells comprising the mammalian adaptive immune system is the requirement for these rare naïve T (and B) lymphocytes directed to a specific microorganism to undergo proliferative expansion upon first encounter with this antigen. In the case of naïve CD8+ T cells the ability of these rare quiescent lymphocytes to rapidly activate and expand into effector T cells in numbers sufficient to control viral and certain bacterial infections can be essential for survival. In this report we examined the activation, cell cycle time and initial proliferative response of naïve murine CD8+ T cells responding in vivo to Influenza and Vaccinia virus infection or vaccination with viral antigens. Remarkably, we observed that CD8+ T cells could divide and proliferate with an initial cell division time of as short as 2 hours. The initial cell cycle time of responding CD8+ T cells is not fixed but is controlled by the antigenic stimulus provided by the APC in vivo. Initial cell cycle time influences the rate of T cell expansion and the numbers of effector T cells subsequently accumulating at the site of infection. The T cell cycle time varies with duration of the G1 phase of the cell cycle. The duration of G1 is inversely correlated with the phosphorylation state of the retinoblastoma (Rb) protein in the responding T cells. The implication of these findings for the development of adaptive immune responses and the regulation of cell cycle in higher eukaryotic cells is discussed

Novel strategies in tendon and ligament tissue engineering: Advanced biomaterials and regeneration motifs

Tendon and ligaments have poor healing capacity and when injured often require surgical intervention. Tissue replacement via autografts and allografts are non-ideal strategies that can lead to future problems. As an alternative, scaffold-based tissue engineering strategies are being pursued. In this review, we describe design considerations and major recent advancements of scaffolds for tendon/ligament engineering. Specifically, we outline native tendon/ligament characteristics critical for design parameters and outcome measures, and introduce synthetic and naturally-derived biomaterials used in tendon/ligament scaffolds. We will describe applications of these biomaterials in advanced tendon/ligament engineering strategies including the utility of scaffold functionalization, cyclic strain, growth factors, and interface considerations. The goal of this review is to compile and interpret the important findings of recent tendon/ligament engineering research in an effort towards the advancement of regenerative strategies

Automatic Choroid Layer Segmentation from Optical Coherence Tomography Images Using Deep Learning

The choroid layer is a vascular layer in human retina and its main function is to provide oxygen and support to the retina. Various studies have shown that the thickness of the choroid layer is correlated with the diagnosis of several ophthalmic diseases. For example, diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. Despite contemporary advances, automatic segmentation of the choroid layer remains a challenging task due to low contrast, inhomogeneous intensity, inconsistent texture and ambiguous boundaries between the choroid and sclera in Optical Coherence Tomography (OCT) images. The majority of currently implemented methods manually or semi-automatically segment out the region of interest. While many fully automatic methods exist in the context of choroid layer segmentation, more effective and accurate automatic methods are required in order to employ these methods in the clinical sector. This paper proposed and implemented an automatic method for choroid layer segmentation in OCT images using deep learning and a series of morphological operations. The aim of this research was to segment out Bruch’s Membrane (BM) and choroid layer to calculate the thickness map. BM was segmented using a series of morphological operations, whereas the choroid layer was segmented using a deep learning approach as more image statistics were required to segment accurately. Several evaluation metrics were used to test and compare the proposed method against other existing methodologies. Experimental results showed that the proposed method greatly reduced the error rate when compared with the other state-of-the art methods

Increased mitochondrial DNA diversity in ancient Columbia River basin Chinook salmon Oncorhynchus tshawytscha

The Columbia River and its tributaries provide essential spawning and rearing habitat for many salmonid species, including Chinook salmon (Oncorhynchus tshawytscha). Chinook salmon were historically abundant throughout the basin and Native Americans in the region relied heavily on these fish for thousands of years. Following the arrival of Europeans in the 1800s, salmon in the basin experienced broad declines linked to overfishing, water diversion projects, habitat destruction, connectivity reduction, introgression with hatchery-origin fish, and hydropower development. Despite historical abundance, many native salmonids are now at risk of extinction. Research and management related to Chinook salmon is usually explored under what are termed “the four H’s”: habitat, harvest, hatcheries, and hydropower; here we explore a fifth H, history. Patterns of prehistoric and contemporary mitochondrial DNA variation from Chinook salmon were analyzed to characterize and compare population genetic diversity prior to recent alterations and, thus, elucidate a deeper history for this species. A total of 346 ancient and 366 contemporary samples were processed during this study. Species was determined for 130 of the ancient samples and control region haplotypes of 84 of these were sequenced. Diversity estimates from these 84 ancient Chinook salmon were compared to 379 contemporary samples. Our analysis provides the first direct measure of reduced genetic diversity for Chinook salmon from the ancient to the contemporary period, as measured both in direct loss of mitochondrial haplotypes and reductions in haplotype and nucleotide diversity. However, these losses do not appear equal across the basin, with higher losses of diversity in the mid-Columbia than in the Snake subbasin. The results are unexpected, as the two groups were predicted to share a common history as parts of the larger Columbia River Basin, and instead indicate that Chinook salmon in these subbasins may have divergent demographic histories.Ye