Dataset Information

Texture segmentation influences the spatial profile of presaccadic attention.

ABSTRACT: Attention is important for selecting targets for action. Several studies have shown that attentional selection precedes eye movements to a target, and results in an enhanced sensitivity at the saccade goal. Typically these studies have used isolated targets on blank backgrounds, which are rare in real-world situations. Here, we examine the spatial profile of sensitivity around a saccade target on a textured background and how the influence of the surrounding context develops over time. We used two textured backgrounds: a uniform texture, and a concentric arrangement of an inner and an outer texture with orthogonal orientations. For comparison, we also measured sensitivity around the target on a blank background. The spatial profile of sensitivity was measured with a brief, dim, probe flashed around the saccade target. When the target was on a blank or a uniformly textured background, spatial sensitivity peaked near the target location around 350 ms after cue onset and declined with distance from the target. However, when the background was made up of an inner and outer texture, sensitivity to the inner texture was uniformly high, peaking at about 350 ms after cue onset, suggesting that the entire inner texture was selected along with the target. The enhancement of sensitivity on the inner texture was much smaller when observers attended the target covertly and performed the probe-detection task. Thus, our results suggest that the surface representation around the target is taken into account when an observer actively plans to interact with the target.

SUBMITTER: Ghahghaei S

PROVIDER: S-EPMC5338474 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Texture segmentation influences the spatial profile of presaccadic attention.

Ghahghaei Saeideh S Verghese Preeti P

Journal of vision 20170201 2

Attention is important for selecting targets for action. Several studies have shown that attentional selection precedes eye movements to a target, and results in an enhanced sensitivity at the saccade goal. Typically these studies have used isolated targets on blank backgrounds, which are rare in real-world situations. Here, we examine the spatial profile of sensitivity around a saccade target on a textured background and how the influence of the surrounding context develops over time. We used t ...[more]

PMID: 28245491

Similar Datasets

Project description:IntroductionPolymer materials used in medical devices and treatments invariably encounter cellular networks. For the device to succeed in tissue engineering applications, the polymer must promote cellular interactions through adhesion and proliferation. To predict how a polymer will behave in vitro, these material-cell interactions need to be well understood.MethodsTo study polymer structure-property relationships, microparticles of four chemically distinct tyrosol-derived poly(ester-arylate) polymers and a commercially available poly(lactic acid-co-glycolic acid) (PLGA) copolymer were prepared and their interactions with cells investigated. Cell loading concentration was optimized and cell adhesion and proliferation evaluated. Particles were also tested for their ability to adsorb bone morphogenetic protein-2 (BMP-2) and differentiate a myoblast cell line towards an osteoblast lineage through BMP-2 loading and release.ResultsWhile cell adhesion was observed on all particles after 24 h of incubation, the highest degree of cell adhesion occurred on polymers with smaller crystallites. At longer incubation times, cells proliferated on all particle formulations, regardless of the differences in polymer properties. High BMP-2 loading was achieved for all particle formulations and all formulations showed a burst release. Even with the burst release, cells cultured on all formulations showed an upregulation in alkaline phosphatase (ALP) activity, a measure of osteoblast differentiation.ConclusionsAs with cell adhesion, the polymer with the smaller crystallite showed the most ALP activity. We suggest that smaller crystallites serve as a proxy for topographical roughness to elicit the observed responses from cells. Furthermore, we have drawn a correlation between the polymer crystallite with the hydration potential using surface analysis techniques.Graphical abstractSupplementary informationThe online version contains supplementary material available at 10.1007/s12195-022-00729-9.

Project description:Coronavirus disease (COVID-19) broke out at the end of 2019, and has resulted in an ongoing global pandemic. Segmentation of pneumonia infections from chest computed tomography (CT) scans of COVID-19 patients is significant for accurate diagnosis and quantitative analysis. Deep learning-based methods can be developed for automatic segmentation and offer a great potential to strengthen timely quarantine and medical treatment. Unfortunately, due to the urgent nature of the COVID-19 pandemic, a systematic collection of CT data sets for deep neural network training is quite difficult, especially high-quality annotations of multi-category infections are limited. In addition, it is still a challenge to segment the infected areas from CT slices because of the irregular shapes and fuzzy boundaries. To solve these issues, we propose a novel COVID-19 pneumonia lesion segmentation network, called Spatial Self-Attention network (SSA-Net), to identify infected regions from chest CT images automatically. In our SSA-Net, a self-attention mechanism is utilized to expand the receptive field and enhance the representation learning by distilling useful contextual information from deeper layers without extra training time, and spatial convolution is introduced to strengthen the network and accelerate the training convergence. Furthermore, to alleviate the insufficiency of labeled multi-class data and the long-tailed distribution of training data, we present a semi-supervised few-shot iterative segmentation framework based on re-weighting the loss and selecting prediction values with high confidence, which can accurately classify different kinds of infections with a small number of labeled image data. Experimental results show that SSA-Net outperforms state-of-the-art medical image segmentation networks and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage. Meanwhile, our semi-supervised iterative segmentation model can improve the learning ability in small and unbalanced training set and can achieve higher performance.

Project description:The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24% to 43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments, notably, quantitative metrics based on image-processing are not used. The authors propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention.In this study with 20 clinically acquired CT scans on postoperative patients, the authors demonstrated a novel approach to geometric classification of the abdominal. The authors' approach uses a texture analysis based on Gabor filters to extract feature vectors and follows a fuzzy c-means clustering method to estimate voxelwise probability memberships for eight clusters. The memberships estimated from the texture analysis are helpful to identify anatomical structures with inhomogeneous intensities. The membership was used to guide the level set evolution, as well as to derive an initial start close to the abdominal wall.Segmentation results on abdominal walls were both quantitatively and qualitatively validated with surface errors based on manually labeled ground truth. Using texture, mean surface errors for the outer surface of the abdominal wall were less than 2 mm, with 91% of the outer surface less than 5 mm away from the manual tracings; errors were significantly greater (2-5 mm) for methods that did not use the texture.The authors' approach establishes a baseline for characterizing the abdominal wall for improving VH care. Inherent texture patterns in CT scans are helpful to the tissue classification, and texture analysis can improve the level set segmentation around the abdominal region.

Dataset Information

Texture segmentation influences the spatial profile of presaccadic attention.

Publications

Texture segmentation influences the spatial profile of presaccadic attention.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets