Abstract
Chest radiography is one of the most common imaging exams, but its interpretation is often challenging and timeconsuming, which has motivated the development of automated tools for pathology/abnormality detection. Deep learning models trained on large-scale chest X-ray datasets have shown promising results but are highly dependent on the quality of the data. However, these datasets often contain incorrect metadata and non-compliant or corrupted images. These inconsistencies are ultimately incorporated in the training process, impairing the validity of the results. In this study, a novel approach to detect non-compliant images based on deep features extracted from a patient position classification model and a pre-trained VGG16 model are proposed. This method is applied to CheXpert, a widely used public dataset. From a pool of 100 images, it is shown that the deep feature-based methods based on a patient position classification model are able to retrieve a larger number of non-compliant images (up to 81% of non-compliant images), when compared to the same methods but based on a pretrained VGG16 (up to 73%) and the state of the art uncertainty-based method (50%).

Abstract
The importance of X-Ray imaging analysis is paramount for healthcare institutions since it is the main imaging modality for patient diagnosis, and deep learning can be used to aid clinicians in image diagnosis or structure segmentation. In recent years, several articles demonstrate the capability that deep learning models have in classifying and segmenting chest x-ray images if trained in an annotated dataset. Unfortunately, for segmentation tasks, only a few relatively small datasets have annotations, which poses a problem for the training of robust deep learning strategies. In this work, a semi-supervised approach is developed which consists of using available information regarding other anatomical structures to guide the segmentation when the groundtruth segmentation for a given structure is not available. This semi-supervised is compared with a fully-supervised approach for the tasks of lung segmentation and for multi-structure segmentation (lungs, heart and clavicles) in chest x-ray images. The semi-supervised lung predictions are evaluated visually and show relevant improvements, therefore this approach could be used to improve performance in external datasets with missing groundtruth. The multi-structure predictions show an improvement in mean absolute and Hausdorff distances when compared to a fully supervised approach and visual analysis of the segmentations shows that false positive predictions are removed. In conclusion, the developed method results in a new strategy that can help solve the problem of missing annotations and increase the quality of predictions in new datasets.

Abstract
Chitosan (Ch) porous matrices were investigated regarding their ability to be colonized by human microvascular endothelial cells (HPMEC-ST1.6R cell line) and macrovascular endothelial cells namely HUVECs. Specifically we assessed if previous incubation of Ch in a fibronectin (FN) solution was effective in promoting endothelial cell (EC) adhesion to Ch matrices with different degrees of acetylation (DAs). Upon FN physiadsorption, marked differences were found between the two DAs investigated, namely DA 4% and 15%. While cell adhesion was impaired on Ch with DA 15%, ECs were able to not only adhere to Ch with DA 4%, but also to spread and colonize the scaffolds, with retention of the EC phenotype and angiogenic potential. To explain the observed differences between the two DAs, protein adsorption studies using (125)I-FN and immunofluorescent labelling of FN cell-binding domains were carried out. in agreement with the higher cell numbers found, scaffolds with DA 4% revealed a higher number of exposed FN cell-binding domains as well as greater ability to adsorb FN and to retain and exchange adsorbed FN in the presence of competitive proteins. These findings suggest that the DA is a key parameter modulating EC adhesion to FN-coated Ch by influencing the adsorbed protein layer.

Abstract

Abstract

Abstract
In vivo observation of cells in the Arabidopsis thaliana root, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of individual cells. Automating the process of cell tracking is an important step to create tools which will facilitate the analysis of cells' evolution through time. Here we introduce a confocal tracking system designed in two stages. At the image acquisition stage, we track the area under analysis based on point-to-point correspondences and motion estimation. After image acquisition, we compute cell-to-cell correspondences through time. The final result is a temporal structured information about each cell.