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

Multi-frame-based Cross-domain Image Denoising for Low-dose Computed Tomography

Computed tomography (CT) has been used worldwide for decades as one of the most important non-invasive tests in assisting diagnosis. However, the ionizing nature of X-ray exposure raises concerns about potential health risks such as cancer. The desire for lower radiation dose has driven researchers to improve the reconstruction quality, especially by removing noise and artifacts. Although previous studies on low-dose computed tomography (LDCT) denoising have demonstrated the effectiveness of learning-based methods, most of them were developed on the simulated data collected using Radon transform. However, the real-world scenario significantly differs from the simulation domain, and the joint optimization of denoising with modern CT image reconstruction pipeline is still missing. In this paper, for the commercially available third-generation multi-slice spiral CT scanners, we propose a two-stage method that better exploits the complete reconstruction pipeline for LDCT denoising across different domains. Our method makes good use of the high redundancy of both the multi-slice projections and the volumetric reconstructions while avoiding the collapse of information in conventional cascaded frameworks. The dedicated design also provides a clearer interpretation of the workflow. Through extensive evaluations, we demonstrate its superior performance against state-of-the-art methods

Bibliometric Analysis of Bioscience Trends Journal (2007-2017): Knowledge dynamics and visualization

BioScience Trends (BST) is a peer-reviewed journal belongs to the International Research and Cooperation Association for Bio & Socio-Sciences Advancement (IRCA-BSSA) Group of Japan. Despite a decade of existence, no study was performed to measure the bibliometric profile of the journal. The objective of this study was to investigate the bibliometric characteristic of BST. A bibliometric analysis will specifically measure: 1) growth rate of the scientific publications, 2) dynamics of authorship and collaboration pattern; 3) core research themes of articles that have been published, and 4) citation pattern of BST. Bibliographical archives of BST were obtained from the Core Collection database of the Web of Science (WoS). We divided the dataset into three interval periods, 2007-2010, 2011-2014 and 2015-2017 respectively. Data processing and analysis was performed using Bibliometrix, a bibliometric analysis package in R software, VOSViewer 1.66, Orange 3.15 and CitNetExplorer. Within one decade of scientific production, BST continues to attract global researchers in life sciences. However, it is still dominated by authors from China and Japan. Annual journal growth of BST is 12.83 %. Reaching the end of the first decade, number of first author and the country origin multiplied, 20 and 5 times respectively, compared to the first-year. Research themes are consistent with the Aims and Scope of the Journal with strong emphasizes on molecular biology, biochemistry, and clinical research. Entering the second decade, strategies to promote and enlarge authors participation from countries that are not in the current list are encouraged

The Role of Endocarditis, Myocarditis and Pericarditis in Qualitative and Quantitative Data Analysis

The current study is the first scientometric analysis of research activity and output in the field of inflammatory disorders of the heart (endo-, myo- and pericarditis). Scientometric methods are used to compare scientific performance on national and on international scale to identify single areas of research interest. Interest and research productivity in inflammatory diseases of the heart have increased since 1990. The majority of publications about inflammatory heart disorders were published in Western Europe and North America. The United States of America had a leading position in terms of research productivity and quality; half of the most productive authors in this study came from American institutions. The analysis of international cooperation revealed research activity in countries that are less established in the field of inflammatory heart disorder research, such as Brazil, Saudi Arabia and Tunisia. These results indicate that future research of heart inflammation may no longer be influenced predominantly by a small number of countries. Furthermore, this study revealed weaknesses in currently established scientometric parameters (i.e., h-index, impact factor) that limit their suitability as measures of research quality. In this respect, self-citations should be generally excluded from calculations of h-index and impact factor

SYMBIOmatics: Synergies in Medical Informatics and Bioinformatics – exploring current scientific literature for emerging topics

Background: The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to http://www.symbiomatics.org). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. Results: This paper presents results ofa systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000-2005 ("recent") and 1990-1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. Conclusion: We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science

Computational modelling of the cerebral cortical microvasculature: Effect of x-ray microbeams versus broad beam irradiation.

Microbeam Radiation Therapy is an innovative pre-clinical strategy which uses arrays of parallel, tens of micrometres wide kilo-voltage photon beams to treat tumours. These x-ray beams are typically generated on a synchrotron source. It was shown that these beam geometries allow exceptional normal tissue sparing from radiation damage while still being effective in tumour ablation. A final biological explanation for this enhanced therapeutic ratio has still not been found, some experimental data support an important role of the vasculature. In this work, the effect of microbeams on a normal microvascular network of the cerebral cortex was assessed in computer simulations and compared to the effect of homogeneous, seamless exposures at equal energy absorption. The anatomy of a cerebral microvascular network and the inflicted radiation damage were simulated to closely mimic experimental data using a novel probabilistic model of radiation damage to blood vessels. It was found that the spatial dose fractionation by microbeam arrays significantly decreased the vascular damage. The higher the peak-to-valley dose ratio, the more pronounced the sparing effect. Simulations of the radiation damage as a function of morphological parameters of the vascular network demonstrated that the distribution of blood vessel radii is a key parameter determining both the overall radiation damage of the vasculature and the dose-dependent differential effect of microbeam irradiation

Medical image registration using unsupervised deep neural network: A scoping literature review

In medicine, image registration is vital in image-guided interventions and other clinical applications. However, it is a difficult subject to be addressed which by the advent of machine learning, there have been considerable progress in algorithmic performance has recently been achieved for medical image registration in this area. The implementation of deep neural networks provides an opportunity for some medical applications such as conducting image registration in less time with high accuracy, playing a key role in countering tumors during the operation. The current study presents a comprehensive scoping review on the state-of-the-art literature of medical image registration studies based on unsupervised deep neural networks is conducted, encompassing all the related studies published in this field to this date. Here, we have tried to summarize the latest developments and applications of unsupervised deep learning-based registration methods in the medical field. Fundamental and main concepts, techniques, statistical analysis from different viewpoints, novelties, and future directions are elaborately discussed and conveyed in the current comprehensive scoping review. Besides, this review hopes to help those active readers, who are riveted by this field, achieve deep insight into this exciting field