Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation

Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets. Previous attempts most conduct global-only/random augmentation. Their augmented samples are usually insufficient in diversity and informativeness, thus failing to cover the possible target domain distribution. In this paper, we rethink the data augmentation strategy for SDG in medical image segmentation. Motivated by the class-level representation invariance and style mutability of medical images, we hypothesize that unseen target data can be sampled from a linear combination of $C$ (the class number) random variables, where each variable follows a location-scale distribution at the class level. Accordingly, data augmented can be readily made by sampling the random variables through a general form. On the empirical front, we implement such strategy with constrained B$\acute{\rm e}$zier transformation on both global and local (i.e. class-level) regions, which can largely increase the augmentation diversity. A Saliency-balancing Fusion mechanism is further proposed to enrich the informativeness by engaging the gradient information, guiding augmentation with proper orientation and magnitude. As an important contribution, we prove theoretically that our proposed augmentation can lead to an upper bound of the generalization risk on the unseen target domain, thus confirming our hypothesis. Combining the two strategies, our Saliency-balancing Location-scale Augmentation (SLAug) exceeds the state-of-the-art works by a large margin in two challenging SDG tasks. Code is available at https://github.com/Kaiseem/SLAug

A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation

We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality medical image segmentation, aiming to perform segmentation on the unannotated target domain (e.g. MRI) with the help of labeled source domain (e.g. CT). Previous UDA methods in medical image analysis usually suffer from two challenges: 1) they focus on processing and analyzing data at 2D level only, thus missing semantic information from the depth level; 2) one-to-one mapping is adopted during the style-transfer process, leading to insufficient alignment in the target domain. Different from the existing methods, in our work, we conduct a first of its kind investigation on multi-style image translation for complete image alignment to alleviate the domain shift problem, and also introduce 3D segmentation in domain adaptation tasks to maintain semantic consistency at the depth level. In particular, we develop an unsupervised domain adaptation framework incorporating a novel quartet self-attention module to efficiently enhance relationships between widely separated features in spatial regions on a higher dimension, leading to a substantial improvement in segmentation accuracy in the unlabeled target domain. In two challenging cross-modality tasks, specifically brain structures and multi-organ abdominal segmentation, our model is shown to outperform current state-of-the-art methods by a significant margin, demonstrating its potential as a benchmark resource for the biomedical and health informatics research community

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation

Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most of these techniques have been validated either on private datasets or on small publicly available datasets. Moreover, these datasets mostly addressed single-class problems. To tackle these limitations, the Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2021). CrossMoDA is the first large and multi-class benchmark for unsupervised cross-modality DA. The challenge's goal is to segment two key brain structures involved in the follow-up and treatment planning of vestibular schwannoma (VS): the VS and the cochleas. Currently, the diagnosis and surveillance in patients with VS are performed using contrast-enhanced T1 (ceT1) MRI. However, there is growing interest in using non-contrast sequences such as high-resolution T2 (hrT2) MRI. Therefore, we created an unsupervised cross-modality segmentation benchmark. The training set provides annotated ceT1 (N=105) and unpaired non-annotated hrT2 (N=105). The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137). A total of 16 teams submitted their algorithm for the evaluation phase. The level of performance reached by the top-performing teams is strikingly high (best median Dice - VS:88.4%; Cochleas:85.7%) and close to full supervision (median Dice - VS:92.5%; Cochleas:87.7%). All top-performing methods made use of an image-to-image translation approach to transform the source-domain images into pseudo-target-domain images. A segmentation network was then trained using these generated images and the manual annotations provided for the source image.Comment: Submitted to Medical Image Analysi

Study on flow noise characteristics of Bionic cylinder based on acoustic analogy

The drag and noise reduction of the flow around a cylinder is one of the important topics in hydrodynamics and acoustics. In this paper, three typical bionic cylinders are designed based on the serrated structure on the surface of shark skin. Using Large eddy turbulence model and Lighthill’s acoustic analogy method, the flow noise characteristics of smooth cylinder and three kinds of bionic cylinders at different Reynolds numbers were compared, and the structure of cylinder surface was optimized. The results show that the main source of the flow noise around a cylinder is dipole noise, which is caused by the periodic fluctuating pressure on the cylinder surface.The bionic cylinder can reduce the amplitude of the fluctuating pressure, improve the wake flow field and reduce the wake vorticity, so as to reduce the noise. Among the three kinds of bionic cylinder, V-shaped bionic cylinder has the best noise reduction effect, and the critical value of S/H of V-shaped cylinder is about 2.5. When s / h > 2.5, V-shaped bionic cylinder has no effect of noise reduction

Research on Flow Stability and Vibration of an Industrial Hydraulic Turbine

Industrial hydraulic turbines, a kind of small-scaled turbine in a more compact and flexible application, are frequently used in hydrogen cracking, synthesis ammonia, and circulating water field. Besides the energy recovery efficiency, the working stability at variable speed situations is a critical issue, since its rotation speed changes with the flow parameters of the upstream. In this paper, a conventional turbine was numerically investigated under three different rotation speeds and its best efficiency points (BEPs). The velocity profiles, blade load, pressure fluctuation, and vibration features were discussed to form a comprehensive evaluation of turbine stability. The numerical results were validated through turbine external characteristic and vibration tests. The results indicate that the pressure pulsation and vibrations increase when it deviates from the rated rotation speed, but the relatively low flowrate point behaves better than the large point in the aspect of turbine stability; the main reasons are the angle of incidence and rotor circumferential vortex. The conclusions can provide significant reference for turbine hydraulic optimization and engineering application

Interactions between silver-palladium alloy and silicon carbide cladding layer in TRISO fuel: An in-depth analysis

High-temperature gas-cooled reactors (HTGRs) have garnered considerable interest due to their superior efficiency and inherent safety. This study systematically investigates the interaction between silver-palladium (Ag-Pd) alloy and silicon carbide (SiC) coating in tri-structural isotropic (TRISO) particles of HTGRs, focusing on the potential release of radioactive Ag-110 m through SiC cladding. Operating temperatures ranging from 800–1100 °C were examined to understand the mechanisms of Ag-Pd migration in SiC. Advanced techniques like transmission electron microscopy (TEM), focused ion beam (FIB) coupled with 3D visualization and scanning precession electron diffraction (SPED) integrated with machine learning were employed to identify two pathways of Pd-assisted Ag migration: (1) (Pd,Ag) 2Si and (Pd,Ag) 3Si acting as migration channels, and (2) Pd-rich phases increasing SiC disorder, enhancing Ag diffusion. Significantly, a carbon barrier formation above 1000 °C was observed, which inhibits Ag-Pd/SiC interaction. These findings offering new insights for predicting nuclear reactor lifespan and data support to optimize TRISO particles' structural design.</p

Understanding Ag liquid migration in SiC through ex-situ and in-situ Ag-Pd/SiC interaction studies

The effective containment of fission products (FPs) within tri-structural isotropic (TRISO) fuel particles is crucial for the safety and efficiency of High Temperature Gas-cooled Reactors (HTGRs). Combining multi-scale (µm to nm) and multi-dimensional (2D and 3D) analysis, this study focuses on the migration mechanisms of silver (Ag), for which the Ag-110 m FP isotope is radiotoxic, facilitated by reactions with palladium (Pd) alloys and the silicon carbide (SiC) layer at elevated temperatures (1300–1500 °C). Three primary pathways for Ag migration are identified: (1) diffusion in solid palladium silicide to form (Pd,Ag)2Si, (2) infiltration through cracks and pores in the carbon phase, and (3) liquid phase migration along SiC grain boundaries. Especially at temperatures above the melting point of Pd2Si (1404 °C), a ‘dissolution-recrystallisation’ mechanism is proposed of the Ag-Pd-Si liquid phase migrating along SiC grain boundaries. The study employs state-of-the-art in-situ heating transmission electron microscopy (TEM) techniques to directly observe the dynamic migration processes of Pd silicide within SiC, providing unprecedented insights into the liquid behaviour in TRISO fuel. These findings highlight the significant role of liquid phases in determining the transport of FPs through the TRISO-SiC which is vital for developing strategies that enhance the safety and efficacy of HTGRs

Liraglutide Improves the Angiogenic Capability of EPC and Promotes Ischemic Angiogenesis in Mice under Diabetic Conditions through an Nrf2-Dependent Mechanism

The impairment in endothelial progenitor cell (EPC) functions results in dysregulation of vascular homeostasis and dysfunction of the endothelium under diabetic conditions. Improving EPC function has been considered as a promising strategy for ameliorating diabetic vascular complications. Liraglutide has been widely used as a therapeutic agent for diabetes. However, the effects and mechanisms of liraglutide on EPC dysfunction remain unclear. The capability of liraglutide in promoting blood perfusion and angiogenesis under diabetic conditions was evaluated in the hind limb ischemia model of diabetic mice. The effect of liraglutide on the angiogenic function of EPC was evaluated by cell scratch recovery assay, tube formation assay, and nitric oxide production. RNA sequencing was performed to assess the underlying mechanisms. Liraglutide enhanced blood perfusion and angiogenesis in the ischemic hindlimb of db/db mice and streptozotocin-induced type 1 diabetic mice. Additionally, liraglutide improved tube formation, cell migration, and nitric oxide production of high glucose (HG)-treated EPC. Assessment of liraglutide target pathways revealed a network of genes involved in antioxidant activity. Further mechanism study showed that liraglutide decreased the production of reactive oxygen species and increased the activity of nuclear factor erythroid 2-related factor 2 (Nrf2). Nrf2 deficiency attenuated the beneficial effects of liraglutide on improving EPC function and promoting ischemic angiogenesis under diabetic conditions. Moreover, liraglutide activates Nrf2 through an AKT/GSK3&beta;/Fyn pathway, and inhibiting this pathway abolished liraglutide-induced Nrf2 activation and EPC function improvement. Overall, these results suggest that Liraglutide represents therapeutic potential in promoting EPC function and ameliorating ischemic angiogenesis under diabetic conditions, and these beneficial effects relied on Nrf2 activation