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The long-term storage of nuclear spent fuel in geological repositories has introduced the need to develop new technologies for safeguarding the fuel. Continuity of knowledge about the fuel's location must be maintained during transport of the copper canisters that contain the fuel, from the encapsulation plant to the final repository. Among the possible different containment and surveillance measures are identification and authentication of each canister. The authors propose an innovative solution for this purpose which is based on the ultrasonic acquisition of two fingerprints an artificial code realized by chamfers machined in the inner surface of the copper lid (identification) and a natural signature due to the weld between the lid and the tube (authentication). Several prototypes of ultrasonic acquisition devices have been developed and tested on real copper samples to validate the identification and the authentication concepts. This paper describes the design of a new, optimized version of the ultrasonic acquisition device. The aim of this new design is to provide a solution for the identification and authentication of copper canisters using a portable device that is cost-effective and easy to use, and which does not require the presence of an inspector in the field. The mechanical design of the reader has been upgraded with the introduction of a stepper motor and a new probe holder which includes a beam splitter to acquire two fingerprints simultaneously. The new device has been tested on copper samples, both with and without chamfers, and the results are reported in the paper. The implementation of the Seal Fingerprint Acquisition Device (SFAD) within the new portable acquisition system is discussed at the end of the paper with the goal of improving the electronic performance of the acquisition device in the field.Automated prostate segmentation in MRI is highly demanded for computer-assisted diagnosis. [https://www.selleckchem.com/products/trastuzumab-emtansine-t-dm1-.html selleck chemicals llc] Recently, a variety of deep learning methods have achieved remarkable progress in this task, usually relying on large amounts of training data. Due to the nature of scarcity for medical images, it is important to effectively aggregate data from multiple sites for robust model training, to alleviate the insufficiency of single-site samples. However, the prostate MRIs from different sites present heterogeneity due to the differences in scanners and imaging protocols, raising challenges for effective ways of aggregating multi-site data for network training. In this paper, we propose a novel multisite network (MS-Net) for improving prostate segmentation by learning robust representations, leveraging multiple sources of data. To compensate for the inter-site heterogeneity of different MRI datasets, we develop Domain-Specific Batch Normalization layers in the network backbone, enabling the network to estimate statistics and perform feature normalization for each site separately. Considering the difficulty of capturing the shared knowledge from multiple datasets, a novel learning paradigm, i.e., Multi-site-guided Knowledge Transfer, is proposed to enhance the kernels to extract more generic representations from multi-site data. Extensive experiments on three heterogeneous prostate MRI datasets demonstrate that our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.Precise characterization and analysis of corneal nerve fiber tortuosity are of great importance in facilitating examination and diagnosis of many eye-related diseases. In this paper we propose a fully automated method for image-level tortuosity estimation, comprising image enhancement, exponential curvature estimation, and tortuosity level classification. The image enhancement component is based on an extended Retinex model, which not only corrects imbalanced illumination and improves image contrast in an image, but also models noise explicitly to aid removal of imaging noise. Afterwards, we take advantage of exponential curvature estimation in the 3D space of positions and orientations to directly measure curvature based on the enhanced images, rather than relying on the explicit segmentation and skeletonization steps in a conventional pipeline usually with accumulated pre-processing errors. The proposed method has been applied over two corneal nerve microscopy datasets for the estimation of a tortuosity level for each image. The experimental results show that it performs better than several selected state-of-the-art methods. Furthermore, we have performed manual gradings at tortuosity level of four hundred and three corneal nerve microscopic images, and this dataset has been released for public access to facilitate other researchers in the community in carrying out further research on the same and related topics.Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases related to lungs and heart. The literature in this field of research reports many interesting studies dealing with the challenging tasks of bone suppression and organ segmentation but performed separately, limiting any learning that comes with the consolidation of parameters that could optimize both processes. This study, and for the first time, introduces a multitask deep learning model that generates simultaneously the bone-suppressed image and the organ-segmented image, enhancing the accuracy of tasks, minimizing the number of parameters needed by the model and optimizing the processing time, all by exploiting the interplay between the network parameters to benefit the performance of both tasks. The architectural design of this model, which relies on a conditional generative adversarial network, reveals the process on how the wellestablished pix2pix network (image-to-image network) is modified to fit the need for multitasking and extending it to the new image-to-images architecture. The developed source code of this multitask model is shared publicly on Github as the first attempt for providing the two-task pix2pix extension, a supervised/paired/aligned/registered image-to-images translation which would be useful in many multitask applications. Dilated convolutions are also used to improve the results through a more effective receptive field assessment. The comparison with state-of-the-art al-gorithms along with ablation study and a demonstration video1 are provided to evaluate the efficacy and gauge the merits of the proposed approach.