Analysis and compensation for the effect of the catheter position on image intensities in intravascular optical coherence tomography

Cardiovascular Aspects of Radiolog

Hierarchical prediction of registration misalignment using a convolutional LSTM: application to chest CT scans

In this paper we propose a supervised method to predict registration misalignment using convolutional neural networks (CNNs). This task is casted to a classification problem with multiple classes of misalignment: "correct" 0-3 mm, "poor" 3-6 mm and "wrong" over 6 mm. Rather than a direct prediction, we propose a hierarchical approach, where the prediction is gradually refined from coarse to fine. Our solution is based on a convolutional Long Short-Term Memory (LSTM), using hierarchical misalignment predictions on three resolutions of the image pair, leveraging the intrinsic strengths of an LSTM for this problem. The convolutional LSTM is trained on a set of artificially generated image pairs obtained from artificial displacement vector fields (DVFs). Results on chest CT scans show that incorporating multi-resolution information, and the hierarchical use via an LSTM for this, leads to overall better F1 scores, with fewer misclassifications in a well-tuned registration setup. The final system yields an accuracy of 87.1%, and an average F1 score of 66.4% aggregated in two independent chest CT scan studies.Radiolog

Multifunctional, Multivalent PIC Polymer Scaffolds for Targeting Antigen-Specific, Autoreactive B Cells

Multivalent scaffolds that carry multiple molecules with immunophenotyping or immunomodulatory properties areinvaluable tools for studying and modulating specific functions ofhuman immune responses. So far, streptavidin-biotin-basedtetramers have been widely used for B-cell immunophenotypingpurposes. However, the utility of these tetramers is limited by theirtetravalency, the inherent immunogenicity of streptavidin (abacterial protein that can potentially be recognized by B cells),and the limited feasibility to functionalize these reagents. This has rendered tetramers suboptimal for studying rare, in particular,antigen-specific B-cell populations in the context of clinical applications. Here, we used polyisocyanopeptides (PICs), multivalentpolymeric scaffolds functionalized with around 50 peptide antigens, to detect autoreactive B cells in the peripheral blood of patientswith rheumatoid arthritis. To explore the potential immunomodulatory functionalities, we functionalized PICs with autoantigenicpeptides and a trisaccharide CD22 ligand to inhibit autoreactive B-cell activation through interference with the B-cell receptoractivation pathway, as evidenced by reduced phospho-Syk expression upon PIC binding. Given the possibilities to functionalizePICs, our data demonstrate that the modular and versatile character of PIC scaffolds makes them promising candidates for futureclinical applications in B-cell-mediated diseasesPathophysiology and treatment of rheumatic disease

Articulated Whole-Body Atlases for Small Animal Image Analysis: Construction and Applications

Bone and mineral researc

An objective comparison of cell-tracking algorithms

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge