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BMC Medical Imaging

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Featured article: MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. In this Software article, Müller & Kramer introduce the open-source Python library MIScnn. The framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code.

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Featured article: COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet

Saood and Hatem investigate two structurally-different deep learning techniques, SegNet and U-NET, for semantically segmenting COVID-19 infected tissue regions in CT lung images. They propose that the computer-based techniques may prove as reliable detectors for infected tissue in lung CT scans.

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Featured article: Universal adversarial attacks on deep neural networks for medical image classification

Hirano et al focus on three representative deep neural network-based medical image classification tasks and investigate their vulnerability to the seven model architectures of universal adversarial perturbations.

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Aims and scope

BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.

Call for Content: AI in Medical Imaging

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BMC Medical Imaging welcomes submissions to this new collection focusing on deepening the understanding of artificial intelligence in medical imaging, highlighting its versatility and applications, and breaking down barriers that still exist in the field. 
Find more information and explore recent publications here.

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Editor

Maria Hodges, BMC series, UK

Assistant Editor

Kate Gaines, BMC series, USA

Get credit for your data!

New Content Item © © ra2studio

Valuable data often go unpublished when they could be helping to progress science. Hence, the BMC Series introduced Data notes, a short article type allowing you to describe your data and publish them to make your data easier to find, cite and share.

You can publish your data in BMC Genomic Data (genomic, transcriptomic and high-throughput genotype data) or in BMC Research Notes (data from across all natural and clinical sciences). 

More information about our unique article type can be found on the BMC Genomic Data and BMC Research Notes journal websites. 

BMC Series Focus Issues

Healthcare professionals engaging with authors

September: Patient and public involvement in research

In recognition of World Patient Safety Awareness day on 17th September, the BMC Series presents a focus issue on Patient and Public Involvement (PPI) in research.  We have collated free-to-read content from across our journals - showcasing patient perspectives, research into the study of PPI, and engaging public partners as co-authors in research.

Breastfeeding

August: Breastfeeding

In celebration of World Breastfeeding week in the beginning of August,  the BMC Series presents a focus issue on Breastfeeding.  We have  collated open-access content from across our journals to highlight research into breastfeeding behaviors and care for lactating women, infant and child health, biology of breastfeeding, and more.


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BMC Medical Imaging

ISSN: 1471-2342

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