Computed tomography (CT) is an imaging modality widely used for medical diagnosis and treatment. CT images are often corrupted by undesirable artifacts when metallic implants are carried by patients, which creates the problem of metal artifact reduction (MAR). Existing methods for reducing the artifacts due to metallic implants are inadequate for two main reasons. First, metal artifacts are structured and non-local so that simple image domain enhancement approaches would not suffice. Second, the MAR approaches which attempt to reduce metal artifacts in the X-ray projection (sinogram) domain inevitably lead to severe secondary artifact due to sinogram inconsistency. To overcome these difficulties, we propose an end-to-end trainable Dual Domain Network (DuDoNet) to simultaneously restore sinogram consistency and enhance CT images. The linkage between the sinogram and image domains is a novel Radon inversion layer that allows the gradients to back-propagate from the image domain to the sinogram domain during training. Extensive experiments show that our method achieves significant improvements over other single domain MAR approaches. To the best of our knowledge, it is the first end-to-end dual-domain network for MAR.

The bidirectional texture function (BTF) is a 6D function that can describe textures arising from both spatially-variant surface reflectance and surface mesostructures. In this paper, we present an algorithm for synthesizing the BTF on an arbitrary surface from a sample BTF. A main challenge in surface BTF synthesis is the requirement of a consistent mesostructure on the surface, and to achieve that we must handle the large amount of data in a BTF sample. Our algorithm performs BTF synthesis based on surface textons, which extract essential information from the sample BTF to facilitate the synthesis. We also describe a general search strategy, called the -coherent search, for fast BTF synthesis using surface textons. A BTF synthesized using our algorithm not only looks similar to the BTF sample in all viewing/lighthing conditions but also exhibits a consistent mesostructure when viewing and lighting directions change. Moreover, the synthesized BTF fits the target surface naturally and seamlessly. We demonstrate the effectiveness of our algorithm with sample BTFs from various sources, including those measured from real-world textures.

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Background Acquired resistance remains a limitation of the clinical use of 5-fluorouracil (5-FU). Because exosomes, are important vesicles participating in intercellular communication, their contribution to the development of acquired 5-FU resistance needs to be elucidated. In this study, we aimed to examine the underlying mechanisms of exosomes from 5-FU resistant cells (RKO/R) in sustaining acquired 5-FU resistance in sensitive cells (RKO/P). Methods Exosomes from a 5-FU-resistant cell line (RKO/R) and its parental cell line RKO/P were isolated and co-cultured with 5-FU-sensitive cells. Real-time cellular analysis (RTCA) and FACS analysis were used to examine cell viability and apoptosis. Exosomal protein profiling was performed using shotgun proteomics. Inhibitors and siRNAs were applied to study the involvement of selected proteins in 5-FU resistance. The effect of exosomal p-STAT3 (Tyr705) on the caspase cascade was examined by western blotting (WB) and high content analysis. Xenograft models were established to determine whether exosomal p-STAT3 can induce 5-FU resistance in vivo. Results Our results indicated that exosomes from RKO/R cells significantly promoted cell survival during 5-FU treatment. Proteomics and WB analysis results indicated that GSTP1 and p-STAT3 (Tyr705) were enriched in exosomes from RKO/R cells. Inhibition of p-STAT3 re-sensitized RKO/P cells to 5-FU via caspase cascade. Furthermore, p-STAT3 packaged by exosomes from RKO/R cells increased resistance of tumor cells to 5-FU in vivo. Conclusions Our results reveal a novel mechanism by which p-STAT3-containing exosomes contribute to acquired 5-FU resistance in CRC. This study suggests a new option for potentiating the 5-FU response and finding biomarkers for chemotherapy resistance. Electronic supplementary material The online version of this article (10.1186/s13046-019-1314-9) contains supplementary material, which is available to authorized users.

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We propose a novel framework for rapid and accurate segmentation of a cohort of organs. First, it integrates local and global image context through a product rule to simultaneously detect multiple landmarks on the target organs. The global posterior integrates evidence over all volume patches, while the local image context is modeled with a local discriminative classifier. Through non-parametric modeling of the global posterior, it exploits sparsity in the global context for efficient detection. The complete surface of the target organs is then inferred by robust alignment of a shape model to the resulting landmarks and finally deformed using discriminative boundary detectors. Using our approach, we demonstrate efficient detection and accurate segmentation of liver, kidneys, heart, and lungs in challenging low-resolution MR data in less than one second, and of prostate, bladder, rectum, and femoral heads in CT scans, in roughly one to three seconds and in both cases with accuracy fairly close to inter-user variability.

Abstract. Simple algorithms for segmenting healthy lung parenchyma in CT are unable to deal with high density tissue common in pulmonary diseases. To overcome this problem, we propose a multi-stage learningbased approach that combines anatomical information to predict an initialization of a statistical shape model of the lungs. The initialization first detects the carina of the trachea, and uses this to detect a set of automatically selected stable landmarks on regions near the lung (e.g., ribs, spine). These landmarks are used to align the shape model, which is then refined through boundary detection to obtain fine-grained segmentation. Robustness is obtained through hierarchical use of discriminative classifiers that are trained on a range of manually annotated data of diseased and healthy lungs. We demonstrate fast detection (35s per volume on average) and segmentation of 2 mm accuracy on challenging data.

Figure 1. Above we illustrate the range of motions typical of a single pose cluster, or local linear model. A mean pose, shown as the center of each progression and four orthogonal axes of variation specify this model. The range of motion along each axis is illustrated in both directions from the mean. ABSTRACTWe demonstrate a data-driven approach for representing, compressing, and indexing human-motion databases. Our modeling approach is based on piecewise-linear components that are determined via a divisive clustering method. Selection of the appropriate linear model is determined automatically via a classifier using a subspace of the most significant, or principle features (markers). We show that, after offline training, our model can accurately estimate and classify human motions. We can also construct indexing structures for motion sequences according to their transition trajectories through these linear components. Our method not only provides indices for whole and/or partial motion sequences, but also serves as a compressed representation for the entire motion database. Our method also tends to be immune to temporal variations, and thus avoids the expense of time-warping.