SAM-Med3D

Although the Segment Anything Model (SAM) has demonstrated impressive performance in 2D natural image segmentation, its application to 3D volumetric medical images reveals significant shortcomings, namely suboptimal performance and unstable prediction, necessitating an excessive number of prompt points to attain the desired outcomes. These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information. In this paper, we introduce SAM-Med3D, the most comprehensive study to modify SAM for 3D medical images. Our approach is characterized by its comprehensiveness in two primary aspects: firstly, by comprehensively reformulating SAM to a thorough 3D architecture trained on a comprehensively processed large-scale volumetric medical dataset; and secondly, by providing a comprehensive evaluation of its performance. Specifically, we train SAM-Med3D with over 131K 3D masks and 247 categories. Our SAM-Med3D excels at capturing 3D spatial information, exhibiting competitive performance with significantly fewer prompt points than the top-performing fine-tuned SAM in the medical domain. We then evaluate its capabilities across 15 datasets and analyze it from multiple perspectives, including anatomical structures, modalities, targets, and generalization abilities. Our approach, compared with SAM, showcases pronouncedly enhanced efficiency and broad segmentation capabilities for 3D volumetric medical images. Our code is released at https://github.com/uni-medical/SAM-Med3D

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recent emergence of large-scale datasets, some important questions arise: \textbf{Can models trained on these datasets generalize well on different ones? If yes/no, how to further improve their generalizability?} To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation. We employ training sets from four large-scale public datasets: FLARE22, AMOS, WORD, and TotalSegmentator, each providing extensive labels for abdominal multi-organ segmentation. For evaluation, we incorporate the validation sets from these datasets along with the training set from the BTCV dataset, forming a robust benchmark comprising five distinct datasets. We evaluate the generalizability of various models using the A-Eval benchmark, with a focus on diverse data usage scenarios: training on individual datasets independently, utilizing unlabeled data via pseudo-labeling, mixing different modalities, and joint training across all available datasets. Additionally, we explore the impact of model sizes on cross-dataset generalizability. Through these analyses, we underline the importance of effective data usage in enhancing models' generalization capabilities, offering valuable insights for assembling large-scale datasets and improving training strategies. The code and pre-trained models are available at \href{https://github.com/uni-medical/A-Eval}{https://github.com/uni-medical/A-Eval}

SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massive labeled training data. However, directly applying SAM to medical image segmentation cannot perform well because SAM lacks medical knowledge -- it does not use medical images for training. To incorporate medical knowledge into SAM, we introduce SA-Med2D-20M, a large-scale segmentation dataset of 2D medical images built upon numerous public and private datasets. It consists of 4.6 million 2D medical images and 19.7 million corresponding masks, covering almost the whole body and showing significant diversity. This paper describes all the datasets collected in SA-Med2D-20M and details how to process these datasets. Furthermore, comprehensive statistics of SA-Med2D-20M are presented to facilitate the better use of our dataset, which can help the researchers build medical vision foundation models or apply their models to downstream medical applications. We hope that the large scale and diversity of SA-Med2D-20M can be leveraged to develop medical artificial intelligence for enhancing diagnosis, medical image analysis, knowledge sharing, and education. The data with the redistribution license is publicly available at https://github.com/OpenGVLab/SAM-Med2D

PROX1 is a transcriptional regulator of MMP14

The transcription factor PROX1 is essential for development and cell fate specification. Its function in cancer is context-dependent since PROX1 has been shown to play both oncogenic and tumour suppressive roles. Here, we show that PROX1 suppresses the transcription of MMP14, a metalloprotease involved in angiogenesis and cancer invasion, by binding and suppressing the activity of MMP14 promoter. Prox1 deletion in murine dermal lymphatic vessels in vivo and in human LECs increased MMP14 expression. In a hepatocellular carcinoma cell line expressing high endogenous levels of PROX1, its silencing increased both MMP14 expression and MMP14-dependent invasion in 3D. Moreover, PROX1 ectopic expression reduced the MMP14-dependent 3D invasiveness of breast cancer cells and angiogenic sprouting of blood endothelial cells in conjunction with MMP14 suppression. Our study uncovers a new transcriptional regulatory mechanism of cancer cell invasion and endothelial cell specification.Peer reviewe

FOXC2 controls formation and maturation of lymphatic collecting vessels through cooperation with NFATc1

The mechanisms of blood vessel maturation into distinct parts of the blood vasculature such as arteries, veins, and capillaries have been the subject of intense investigation over recent years. In contrast, our knowledge of lymphatic vessel maturation is still fragmentary. In this study, we provide a molecular and morphological characterization of the major steps in the maturation of the primary lymphatic capillary plexus into collecting lymphatic vessels during development and show that forkhead transcription factor Foxc2 controls this process. We further identify transcription factor NFATc1 as a novel regulator of lymphatic development and describe a previously unsuspected link between NFATc1 and Foxc2 in the regulation of lymphatic maturation. We also provide a genome-wide map of FOXC2-binding sites in lymphatic endothelial cells, identify a novel consensus FOXC2 sequence, and show that NFATc1 physically interacts with FOXC2-binding enhancers. As damage to collecting vessels is a major cause of lymphatic dysfunction in humans, our results suggest that FOXC2 and NFATc1 are potential targets for therapeutic intervention

The Antiviral Effect of Baicalin on Enterovirus 71 In Vitro

Baicalin is a flavonoid compound extracted from Scutellaria roots that has been reported to possess antibacterial, anti-inflammatory, and antiviral activities. However, the antiviral effect of baicalin on enterovirus 71 (EV71) is still unknown. In this study, we found that baicalin showed inhibitory activity on EV71 infection and was independent of direct virucidal or prophylactic effect and inhibitory viral absorption. The expressions of EV71/3D mRNA and polymerase were significantly blocked by baicalin treatment at early stages of EV71 infection. In addition, baicalin could decrease the expressions of FasL and caspase-3, as well as inhibit the apoptosis of EV71-infected human embryonal rhabdomyosarcoma (RD) cells. Altogether, these results indicate that baicalin exhibits potent antiviral effect on EV71 infection, probably through inhibiting EV71/3D polymerase expression and Fas/FasL signaling pathways

The CSES Global Geomagnetic Field Model (CGGM): An IGRF type global geomagnetic field model based on data from the China Seismo-Electromagnetic Satellite.

International audienceUsing magnetic field data from the China Seismo-Electromagnetic Satellite (CSES) mission, we derive a global geomagnetic field model, which we call the CSES Global Geomagnetic Field Model (CGGM). This model describes the Earth’s magnetic main field and its linear temporal evolution over the time period between March 2018 and September 2019. As the CSES mission was not originally designed for main field modelling, we carefully assess the ability of the CSES orbits and data to provide relevant data for such a purpose. A number of issues are identified, and an appropriate modelling approach is found to mitigate these. The resulting CGGM model appearing to be of high enough quality, it is next used as a parent model to produce a main field model extrapolated to epoch 2020.0, which was eventually submitted on October 1, 2019 as one of the IGRF-13 2020 candidate models. This CGGM candidate model, the first ever produced by a Chinese led team, is also the only one relying on a data set completely independent from that used by all other candidate models. A successful validation of this candidate model is performed by comparison with the final (now published) IGRF-13 2020 model and all other candidate models. Comparisons of the secular variation predicted by the CGGM parent model with the final IGRF-13 2020-2025 predictive secular variation also reveals a remarkable agreement. This shows that despite their current limitations, CSES magnetic data can already be used to produce useful IGRF 2020 and 2020-2025 secular variation candidate models to contribute to the official IGRF-13 2020 and predictive secular variation models for the coming 2020-2025 time period. These very encouraging results show that additional efforts to improve the CSES magnetic data quality could make these data very useful for long-term monitoring of the main field and possibly other magnetic field sources, in complement to the data provided by missions such as the ESA Swarm mission

PROX1 Promotes Metabolic Adaptation and Fuels Outgrowth of Wnt(high) Metastatic Colon Cancer Cells

The Wnt pathway is abnormally activated in the majority of colorectal cancers, and significant knowledge has been gained in understanding its role in tumor initiation. However, the mechanisms of metastatic outgrowth in colorectal cancer remain a major challenge. We report that autophagy-dependent metabolic adaptation and survival of metastatic colorectal cancer cells is regulated by the target of oncogenic Wnt signaling, homeobox transcription factor PROX1, expressed by a subpopulation of colon cancer progenitor/stem cells. We identify direct PROX1 target genes and show that repression of a pro-apoptotic member of the BCL2 family, BCL2L15, is important for survival of PROX1(+) cells under metabolic stress. PROX1 inactivation after the establishment of metastases prevented further growth of lesions. Furthermore, autophagy inhibition efficiently targeted metastatic PROX1(+) cells, suggesting a potential therapeutic approach. These data identify PROX1 as a key regulator of the transcriptional network contributing to metastases outgrowth in colorectal cancer