Sliding at first order: Higher-order momentum distributions for discontinuous image registration

In this paper, we propose a new approach to deformable image registration that captures sliding motions. The large deformation diffeomorphic metric mapping (LDDMM) registration method faces challenges in representing sliding motion since it per construction generates smooth warps. To address this issue, we extend LDDMM by incorporating both zeroth- and first-order momenta with a non-differentiable kernel. This allows to represent both discontinuous deformation at switching boundaries and diffeomorphic deformation in homogeneous regions. We provide a mathematical analysis of the proposed deformation model from the viewpoint of discontinuous systems. To evaluate our approach, we conduct experiments on both artificial images and the publicly available DIR-Lab 4DCT dataset. Results show the effectiveness of our approach in capturing plausible sliding motion

Domain Adaptive Semantic Segmentation by Optimal Transport

Scene segmentation is widely used in the field of autonomous driving for environment perception, and semantic scene segmentation (3S) has received a great deal of attention due to the richness of the semantic information it contains. It aims to assign labels to pixels in an image, thus enabling automatic image labeling. Current approaches are mainly based on convolutional neural networks (CNN), but they rely on a large number of labels. Therefore, how to use a small size of labeled data to achieve semantic segmentation becomes more and more important. In this paper, we propose a domain adaptation (DA) framework based on optimal transport (OT) and attention mechanism to address this issue. Concretely, first we generate the output space via CNN due to its superiority of feature representation. Second, we utilize OT to achieve a more robust alignment of source and target domains in output space, where the OT plan defines a well attention mechanism to improve the adaptation of the model. In particular, with OT, the number of network parameters has been reduced and the network has been better interpretable. Third, to better describe the multi-scale property of features, we construct a multi-scale segmentation network to perform domain adaptation. Finally, in order to verify the performance of our proposed method, we conduct experimental comparison with three benchmark and four SOTA methods on three scene datasets, and the mean intersection-over-union (mIOU) has been significant improved, and visualization results under multiple domain adaptation scenarios also show that our proposed method has better performance than compared semantic segmentation methods

CoBigICP: Robust and Precise Point Set Registration using Correntropy Metrics and Bidirectional Correspondence

In this paper, we propose a novel probabilistic variant of iterative closest point (ICP) dubbed as CoBigICP. The method leverages both local geometrical information and global noise characteristics. Locally, the 3D structure of both target and source clouds are incorporated into the objective function through bidirectional correspondence. Globally, error metric of correntropy is introduced as noise model to resist outliers. Importantly, the close resemblance between normal-distributions transform (NDT) and correntropy is revealed. To ease the minimization step, an on-manifold parameterization of the special Euclidean group is proposed. Extensive experiments validate that CoBigICP outperforms several well-known and state-of-the-art methods.Comment: 6 pages, 4 figures. Accepted to IROS202

Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifacts. It negatively impacts subsequent multi-modal image analysis. To address this issue, we propose an end-to-end deep learning framework that utilizes T1-weighted images (T1WIs) as auxiliary modalities to expedite T2WIs' acquisitions. While image pre-processing is capable of mitigating misalignment, improper parameter selection leads to adverse pre-processing effects, requiring iterative experimentation and adjustment. To overcome this shortage, we employ Optimal Transport (OT) to synthesize T2WIs by aligning T1WIs and performing cross-modal synthesis, effectively mitigating spatial misalignment effects. Furthermore, we adopt an alternating iteration framework between the reconstruction task and the cross-modal synthesis task to optimize the final results. Then, we prove that the reconstructed T2WIs and the synthetic T2WIs become closer on the T2 image manifold with iterations increasing, and further illustrate that the improved reconstruction result enhances the synthesis process, whereas the enhanced synthesis result improves the reconstruction process. Finally, experimental results from FastMRI and internal datasets confirm the effectiveness of our method, demonstrating significant improvements in image reconstruction quality even at low sampling rates

WristSketcher: Creating Dynamic Sketches in AR with a Sensing Wristband

Restricted by the limited interaction area of native AR glasses (e.g., touch bars), it is challenging to create sketches in AR glasses. Recent works have attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures to expand the interactive spaces and can work as the 2D/3D sketching input interfaces for AR glasses. Between them, mobile devices allow for accurate sketching but are often heavy to carry, while sketching with bare hands is zero-burden but can be inaccurate due to arm instability. In addition, mid-air bare-hand sketching can easily lead to social misunderstandings and its prolonged use can cause arm fatigue. As a new attempt, in this work, we present WristSketcher, a new AR system based on a flexible sensing wristband for creating 2D dynamic sketches, featuring an almost zero-burden authoring model for accurate and comfortable sketch creation in real-world scenarios. Specifically, we have streamlined the interaction space from the mid-air to the surface of a lightweight sensing wristband, and implemented AR sketching and associated interaction commands by developing a gesture recognition method based on the sensing pressure points on the wristband. The set of interactive gestures used by our WristSketcher is determined by a heuristic study on user preferences. Moreover, we endow our WristSketcher with the ability of animation creation, allowing it to create dynamic and expressive sketches. Experimental results demonstrate that our WristSketcher i) faithfully recognizes users' gesture interactions with a high accuracy of 96.0%; ii) achieves higher sketching accuracy than Freehand sketching; iii) achieves high user satisfaction in ease of use, usability and functionality; and iv) shows innovation potentials in art creation, memory aids, and entertainment applications

Relationship between Central Arterial Stiffness and Insulin Resistance in Chinese Community-Dwelling Population without Diabetes Mellitus

Objective. Insulin resistance (IR) is a pathological condition present not only in patients with type 2 diabetes mellitus (DM), but also in community-dwelling population without DM. Both central arterial stiffness and IR are closely correlated with cardiovascular morbidity and mortality. The relationship between central arterial stiffness and IR has not been described in Chinese community-dwelling population without DM. The current analysis was designed to investigate the relationship between central arterial stiffness and IR in Chinese community-dwelling population without DM. Methods. There were 1150 participants fully assessed for not only homeostasis model assessment of insulin resistance (HOMA-IR) but also carotid-femoral pulse wave velocity (cfPWV). Results. Median age was 39 (18–80) years, and 69.7% were men. Bivariate correlation analysis showed that cfPWV was significantly related to HOMA-IR (P<0.05). Logistic regression analysis indicated that cfPWV was independently associated with HOMA-IR (P<0.05). Conclusions. This community-based analysis testified that the relationship between central arterial stiffness and IR was evident as early as during nondiabetic stage. Early interventions in Chinese community-dwelling population without DM to improve the IR are also important in the prevention of cardiovascular diseases

Hierarchical unbiased graph shrinkage (HUGS): A novel groupwise registration for large data set

Normalizing all images in a large data set into a common space is a key step in many clinical and research studies, e.g., for brain development, maturation, and aging. Recently, groupwise registration has been developed for simultaneous alignment of all images without selecting a particular image as template, thus potentially avoiding bias in the registration. However, most conventional groupwise registration methods do not explore the data distribution during the image registration. Thus, their performance could be affected by large inter-subject variations in the data set under registration. To solve this potential issue, we propose to use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both infant and adult data sets, by also comparing with the conventional group-mean based registration and the ABSORB methods. All experimental results show that our proposed method can achieve better performance in terms of registration accuracy and robustness

Keratin 18 phosphorylation as a progression marker of chronic hepatitis B

BACKGROUND: The intermediate filament proteins keratins 18 (K18) and 8 (K8) polymerize to form the cytoskeletal network in the mature hepatocytes. It has been shown that the phosphorylation of K18 at two serine residues, 33 and 52, correlates with the progression of hepatitis C, but little is known of chronic hepatitis B (CHB). In this study, we examined K18 phosphorylation in relation to CHB. RESULTS: Site-specific phosphorylation of K18 was determined in livers of twelve healthy donors, and non-cirrhosis (n = 40) and cirrhosis (n = 21) patients. On average, progressively higher level of Ser52 phosphorylation was observed in non-cirrhotic and cirrhotic livers, while elevated Ser33 phosphorylation was detected in both livers but no significant difference. Progressive increase of Ser33 and Ser52 phosphorylation correlated with the elevation of both histological lesions and enzymatic activities of alanine aminotransferase in non-cirrhotic livers. In the hepatocytes of an inactive HBV carrier, strong signals of Ser33 phosphorylation were co-localized with viral infection, while only basal level of Ser52 phosphorylation was detected in infected cells. CONCLUSION: Assuming all obtained data, our data suggest that K18 phosphorylation is a progression marker for CHB

Paternal chromosome elimination of inducer triggers induction of double haploids in Brassica napus

A synthetic octoploid rapeseed, Y3380, induces maternal doubled haploids when used as a pollen donor to pollinate plant. However, the mechanism underlying doubled haploid formation remains elusive. We speculated that double haploid induction occurs as the inducer line’s chromosomes pass to the maternal egg cell, and the zygote is formed through fertilization. In the process of zygotic mitosis, the paternal chromosome is specifically eliminated. Part of the paternal gene might have infiltrated the maternal genome through homologous exchange during the elimination process. Then, the zygote haploid genome doubles (early haploid doubling, EH phenomenon), and the doubled zygote continues to develop into a complete embryo, finally forming doubled haploid offspring. To test our hypothesis, in the current study, the octoploid Y3380 line was back bred with the 4122-cp4-EPSPS exogenous gene used as a marker into hexaploid Y3380-cp4-EPSPS as paternal material to pollinate three different maternal materials. The fertilization process of crossing between the inducer line and the maternal parent was observed 48 h after pollination, and the fertilization rate reached 97.92% and 98.72%. After 12 d of pollination, the presence of cp4-EPSPS in the embryo was detected by in situ PCR, and at 13–23 d after pollination, the probability of F1 embryos containing cp4-EPSPS gene was up to 97.27%, but then declined gradually to 0% at 23–33 d. At the same time, the expression of cp4-EPSPS was observed by immunofluorescence in the 3rd to 29th day embryo. As the embryos developed, cp4-EPSPS marker genes were constantly lost, accompanied by embryonic death. After 30 d, the presence of cp4-EPSPS was not detected in surviving embryos. Meanwhile, SNP detection of induced offspring confirmed the existence of double haploids, further indicating that the induction process was caused by the loss of specificity of the paternal chromosome. The tetraploid-induced offspring showed infiltration of the induced line gene loci, with heterozygosity and homozygosity. Results indicated that the induced line chromosomes were eliminated during embryonic development, and the maternal haploid chromosomes were synchronously doubled in the embryo. These findings support our hypothesis and lay a theoretical foundation for further localization or cloning of functional genes involved in double haploid induction in rapeseed