USFM: A Universal Ultrasound Foundation Model Generalized to Tasks and Organs towards Label Efficient Image Analysis

Inadequate generality across different organs and tasks constrains the application of ultrasound (US) image analysis methods in smart healthcare. Building a universal US foundation model holds the potential to address these issues. Nevertheless, the development of such foundational models encounters intrinsic challenges in US analysis, i.e., insufficient databases, low quality, and ineffective features. In this paper, we present a universal US foundation model, named USFM, generalized to diverse tasks and organs towards label efficient US image analysis. First, a large-scale Multi-organ, Multi-center, and Multi-device US database was built, comprehensively containing over two million US images. Organ-balanced sampling was employed for unbiased learning. Then, USFM is self-supervised pre-trained on the sufficient US database. To extract the effective features from low-quality US images, we proposed a spatial-frequency dual masked image modeling method. A productive spatial noise addition-recovery approach was designed to learn meaningful US information robustly, while a novel frequency band-stop masking learning approach was also employed to extract complex, implicit grayscale distribution and textural variations. Extensive experiments were conducted on the various tasks of segmentation, classification, and image enhancement from diverse organs and diseases. Comparisons with representative US image analysis models illustrate the universality and effectiveness of USFM. The label efficiency experiments suggest the USFM obtains robust performance with only 20% annotation, laying the groundwork for the rapid development of US models in clinical practices.Comment: Submit to MedIA, 17 pages, 11 figure

Agarose-resolvable InDel markers based on whole genome re-sequencing in cucumber

Insertion and Deletion (InDel) are common features in genomes and are associated with genetic variation. The whole-genome re-sequencing data from two parents (X1 and X2) of the elite cucumber (Cucumis sativus) hybrid variety Lvmei No.1 was used for genome-wide InDel polymorphisms analysis. Obtained sequence reads were mapped to the genome reference sequence of Chinese fresh market type inbred line ‘9930’ and gaps conforming to InDel were pinpointed. Further, the level of cross-parents polymorphism among five pairs of cucumber breeding parents and their corresponding hybrid varieties were used for evaluating hybrid seeds purity test efficiency of InDel markers. A panel of 48 cucumber breeding lines was utilized for PCR amplification versatility and phylogenetic analysis of these markers. In total, 10,470 candidate InDel markers were identified for X1 and X2. Among these, 385 markers with more than 30 nucleotide difference were arbitrary chosen. These markers were selected for experimental resolvability through electrophoresis on an Agarose gel. Two hundred and eleven (211) accounting for 54.81% of markers could be validated as single and clear polymorphic pattern while 174 (45.19%) showed unclear or monomorphic genetic bands between X1 and X2. Cross-parents polymorphism evaluation recorded 68 (32.23%) of these markers, which were designated as cross-parents transferable (CPT) InDel markers. Interestingly, the marker InDel114 presented experimental transferability between cucumber and melon. A panel of 48 cucumber breeding lines including parents of Lvmei No. 1 subjected to PCR amplification versatility using CPT InDel markers successfully clustered them into fruit and common cucumber varieties based on phylogenetic analysis. It is worth noting that 16 of these markers were predominately associated to enzymatic activities in cucumber. These agarose-based InDel markers could constitute a valuable resource for hybrid seeds purity testing, germplasm classification and marker-assisted breeding in cucumber

Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation

The segmentation of the left ventricle endocardium (LVendo) and the left ventricle epicardium (LVepi) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have been the most commonly used approach for echocardiography segmentation. However, the performance of a well-trained segmentation network may degrade in unseen domain datasets due to the distribution shift of the data. Adaptation algorithms can improve the generalization of deep neural networks to different domains. In this paper, we present a multi-space adaptation-segmentation-joint framework, named MACS, for cross-domain echocardiography segmentation. It adopts a generative adversarial architecture; the generator fulfills the segmentation task and the multi-space discriminators align the two domains on both the feature space and output space. We evaluated the MACS method on two echocardiography datasets from different medical centers and vendors, the publicly available CAMUS dataset and our self-acquired dataset. The experimental results indicated that the MACS could handle unseen domain datasets well, without requirements for manual annotations, and improve the generalization performance by 2.2% in the Dice metric

Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation

The segmentation of the left ventricle endocardium (LVendo) and the left ventricle epicardium (LVepi) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have been the most commonly used approach for echocardiography segmentation. However, the performance of a well-trained segmentation network may degrade in unseen domain datasets due to the distribution shift of the data. Adaptation algorithms can improve the generalization of deep neural networks to different domains. In this paper, we present a multi-space adaptation-segmentation-joint framework, named MACS, for cross-domain echocardiography segmentation. It adopts a generative adversarial architecture; the generator fulfills the segmentation task and the multi-space discriminators align the two domains on both the feature space and output space. We evaluated the MACS method on two echocardiography datasets from different medical centers and vendors, the publicly available CAMUS dataset and our self-acquired dataset. The experimental results indicated that the MACS could handle unseen domain datasets well, without requirements for manual annotations, and improve the generalization performance by 2.2% in the Dice metric

Leaf litter traits predominantly control litter decomposition in streams worldwide

Aim Leaf litter decomposition in freshwater ecosystems is a vital process linking ecosystem nutrient cycling, energy transfer and trophic interactions. In comparison to terrestrial ecosystems, in which researchers find that litter traits predominantly regulate litter decomposition worldwide, the dominant factors controlling its decomposition in aquatic ecosystems are still debated, with global patterns not well documented. Here, we aimed to explore general patterns and key drivers (e.g., litter traits, climate and water characteristics) of leaf litter decomposition in streams worldwide. Location Global. Time period 1977-2018. Major taxa studied Leaf litter. Methods We synthesized 1,707 records of litter decomposition in streams from 275 studies. We explored variations in decomposition rates among climate zones and tree functional types and between mesh size groups. Regressions were performed to identify the factors that played dominant roles in litter decomposition globally. Results Litter decomposition rates did not differ among tropical, temperate and cold climate zones. Decomposition rates of litter from evergreen conifer trees were much lower than those of deciduous and evergreen broadleaf trees, attributed to the low quality of litter from evergreen conifers. No significant differences were found between decomposition rates of litter from deciduous and evergreen broadleaf trees. Additionally, litter decomposition rates were much higher in coarse- than in fine-mesh bags, which controled the entrance of decomposers of different body sizes. Multiple regressions showed that litter traits (including lignin, C:N ratio) and elevation were the most important factors in regulating leaf litter decomposition. Main conclusions Litter traits predominantly control leaf litter decomposition in streams worldwide. Although further analyses are necessary to explore whether commonalities of the predominant role of litter traits in decomposition exist in both aquatic and terrestrial ecosystems, our findings could contribute to the use of trait-based approaches in modelling the decomposition of litter in streams globally and exploring mechanisms of land-water-atmosphere carbon fluxes

Data from: Leaf litter traits predominantly control litter decomposition in streams worldwide

Aim Leaf litter decomposition in freshwater ecosystems is a vital process linking ecosystem nutrient cycling, energy transfer, and trophic interactions. In comparison to terrestrial ecosystems, in which researchers find that litter traits predominantly regulate its decomposition worldwide, the dominant factors controlling litter decomposition in aquatic ecosystems are still debated with global patterns not well documented. Here, we aimed to explore general patterns and key drivers (e.g. litter traits, climate, and water characteristics) of leaf litter decomposition in streams worldwide. Location Global Time period 1977-2018 Leaf litter Methods We synthesized 1707 records of litter decomposition in streams from 275 studies. We explored variations in decomposition rates among climate zones, tree functional types, and between mesh size groups. Regressions were performed to identify the factors that play dominant roles in litter decomposition globally. Results Litter decomposition rates did not differ among tropical, temperate, and cold climate zones. Decomposition rates of litter from evergreen conifer trees (EC) were much lower than those of deciduous and evergreen broad-leaf trees (DB and EB), attributed to the low quality of litter from EC. No significant differences were found between decomposition rates of DB and EB. Additionally, litter decomposition rates were much higher in coarse- than in fine-mesh bags, which control the entrance of decomposers of different body sizes. Multiple regressions showed that litter traits (including lignin, C:N ratio) and altitude were the most important factors in regulating leaf litter decomposition. Main conclusions Litter traits predominantly control leaf litter decomposition in streams worldwide. While further analyses are necessary to explore whether commonalities of litter traits’ predominant role in decomposition exist in both aquatic and terrestrial ecosystems, our findings could contribute to using trait-based approaches in modeling the decomposition of litter in streams globally and exploring mechanisms of land-water-atmosphere carbon fluxes

IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution

Reliable detection of the media-adventitia border (MAB) and the lumen-intima border (LIB) in intravascular ultrasound (IVUS) images remains a challenging task that is of high clinical interest. In this paper, we propose a superpixel-wise fuzzy clustering technique modified by edges, followed by level set evolution (SFCME-LSE), for automatic border extraction in 40 MHz IVUS images. The contributions are three-fold. First, the usage of superpixels suppresses the influence of speckle noise in ultrasound images on the clustering results. Second, we propose a region of interest (ROI) assignment scheme to prevent the segmentation from being distracted by pathological structures and artifacts. Finally, the contour is converged towards the target boundary through LSE with an appropriately improved edge indicator. Quantitative evaluations on two IVUS datasets by the Jaccard measure (JM), the percentage of area difference (PAD), and the Hausdorff distance (HD) demonstrate the effectiveness of the proposed SFCME-LSE method. SFCME-LSE achieves the minimal HD of 1.20 ± 0.66 mm and 1.18 ± 0.70 mm for the MAB and LIB, respectively, among several state-of-the-art methods on a publicly available dataset

Multidisciplinary Treatment on a Case of ROSAH Syndrome

A 15-year-old female was referred to the hospital with intermittent fever, where multiple systemic abnormalities were found, such as splenomegaly, secondary hypersplenism, retinitis pigmentosa, and ectodermal dysplasia. Medical history revealed that she had suffered recurrent respiratory infections, blurred vision at night, and dysplasia of teeth and nail beds since childhood. Then she was suspected to be experiencing ROSAH syndrome, a rare disease newly recognized in recent years, which was finally confirmed by gene sequencing results. During a course of treatment with tumor necrosis factor inhibitors, recurrent fever with elevated inflammatory markers reappeared, and the child developed headaches. To guide the comprehensive treatment and improve the patient's quality of life, the multidisciplinary team in Peking Union Medical College Hospital discussed together and directed the following treatment