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Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: (1) a model that generates synthetic structural errors, and (2) a label appearance simulation network that produces segmentations with synthetic errors that are similar in appearance to the real initial segmentations. Using these segmentations with synthetic errors and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net, four previous label refinement methods, and a U-Net trained with a loss tailored for tubular structures. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Shuai Chen reports financial support was provided by Chinese Scholarship Council. Shuai Chen reports a relationship with Erasmus Medical Center that includes: employment. Antonio Garcia-Uceda reports a relationship with Erasmus Medical Center that includes: employment. Jiahang Su reports a relationship with Erasmus Medical Center that includes: employment. Gijs van Tulder reports a relationship with Radboud University that includes: employment. Lennard Wolf reports a relationship with Erasmus Medical Center that includes: employment. Theo van Walsum reports a relationship with Erasmus Medical Center that includes: employment. Marleen de Bruijne reports a relationship with Erasmus Medical Center that includes: employment., (Copyright © 2024. Published by Elsevier B.V.)

Introduction: Imaging biomarkers, such as the collateral score as determined from Computed Tomography Angiography (CTA) images, play a role in treatment decision making for acute stroke patients. In this manuscript, we present an end-to-end learning approach for automatic determination of a collateral score from a CTA image. Our aim was to investigate whether such end-to-end learning approaches can be used for this classification task, and whether the resulting classification can be used in existing outcome prediction models., Methods: The method consists of a preprocessing step, where the CTA image is aligned to an atlas and divided in the two hemispheres: the affected side and the healthy side. Subsequently, a VoxResNet based convolutional neural network is used to extract features at various resolutions from the input images. This is done by using a Siamese model, such that the classification is driven by the comparison between the affected and healthy using a unique set of features for both hemispheres. After masking the resulting features for both sides with the vascular region and global average pooling (per hemisphere) and concatenation of the resulting features, a fully connected layer is used to determine the categorized collateral score., Experiments: Several experiments have been performed to optimize the model hyperparameters and training procedure, and to validate the final model performance. The hyperparameter optimization and subsequent model training was done using CTA images from the MR CLEAN Registry, a Dutch multi-center multi-vendor registry of acute stroke patients that underwent endovascular treatment. A separate set of images, from the MR CLEAN Trial, served as an external validation set, where collateral scoring was assessed and compared with both human observers and a recent more traditional model. In addition, the automated collateral scores have been used in an existing functional outcome prediction model that uses both imaging and non-imaging clinical parameters., Conclusion: The results show that end-to-end learning of collateral scoring in CTA images is feasible, and does perform similar to more traditional methods, and the performance also is within the inter-observer variation. Furthermore, the results demonstrate that the end-to-end classification results also can be used in an existing functional outcome prediction model., Competing Interests: VF was employed by Quantib BV. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) WZ and RB declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Fortunati, Su, Wolff, van Doormaal, Hofmeijer, Martens, Bokkers, van Zwam, van der Lugt and van Walsum.)

In this study, an AgBr/Ag 3 PO 4 (ABAP) photocatalyst has been prepared via a facile one-pot anion-exchange method. SEM, XRD, XPS and UV-Vis DRS characterization techniques are carried out to study the structural and physicochemical characteristics of the AgBr/Ag 3 PO 4 composites. The ABAP photocatalyst exhibited outstanding photocatalytic capability for the photodegradation of rhodamine B (RhB) under visible light irradiation. The optimal ABAP-48% composite displayed the highest photocatalytic activity; a complete degradation was attained in 25 min under visible light irradiation. The excellent stability and reusability of ABAP catalysts were examined by five subsequent runs. A probable degradation mechanism of ABAP composites was carefully surveyed. Furthermore, radical trapping experiments confirmed that the ˙O 2 - radical was the main active species in the photodegradation reaction., Competing Interests: We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “One-pot synthesis of AgBr/Ag3PO4 composite photocatalyst for enhancing visible photocatalytic degradation of RhB”., (This journal is © The Royal Society of Chemistry.)