BMC Medical Imaging

Figure 1

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.

Figure 1

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.

Figure 1 (adapted)

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.

Extracellular volume-guided late gadolinium enhancement analysis for non-ischemic cardiomyopathy: The Women’s Interagency HIV Study

Authors: Yoko Kato, Jorge R. Kizer, Mohammad R. Ostovaneh, Jason Lazar, Qi Peng, Rob J. van der Geest, Joao A. C. Lima and Bharath Ambale-Venkatesh

Content type: Research article

27 July 2021
Preliminary study on the application of renal ultrasonography radiomics in the classification of glomerulopathy

Authors: Lijie Zhang, Zhengguang Chen, Lei Feng, Liwei Guo, Dong Liu, Jinjin Hai, Kai Qiao, Jian Chen, Bin Yan and Genyang Cheng

Content type: Research article

23 July 2021
Retroperitoneum ganglioneuroma: imaging features and surgical outcomes of 35 cases at a Chinese Institution

Authors: Qian-Wen Zhang, Tao Song, Pan-Pan Yang and Qiang Hao

Content type: Research

22 July 2021
An evaluation of performance measures for arterial brain vessel segmentation

Authors: Orhun Utku Aydin, Abdel Aziz Taha, Adam Hilbert, Ahmed A. Khalil, Ivana Galinovic, Jochen B. Fiebach, Dietmar Frey and Vince Istvan Madai

Content type: Research

16 July 2021
Three-stage segmentation of lung region from CT images using deep neural networks

Authors: Michael Osadebey, Hilde K. Andersen, Dag Waaler, Kristian Fossaa, Anne C. T. Martinsen and Marius Pedersen

Content type: Research article

15 July 2021

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Age and gender specific normal values of left ventricular mass, volume and function for gradient echo magnetic resonance imaging: a cross sectional study

Authors: Peter A Cain, Ragnhild Ahl, Erik Hedstrom, Martin Ugander, Ase Allansdotter-Johnsson, Peter Friberg and Hakan Arheden

Content type: Research article

21 January 2009
Is fasting a necessary preparation for abdominal ultrasound?

Authors: Tariq Sinan, Hans Leven and Mehraj Sheikh

Content type: Research article

22 July 2003
MRCP compared to diagnostic ERCP for diagnosis when biliary obstruction is suspected: a systematic review

Authors: Eva C Kaltenthaler, Stephen J Walters, Jim Chilcott, Anthony Blakeborough, Yolanda Bravo Vergel and Steven Thomas

Content type: Research article

14 August 2006
Tumor volume in subcutaneous mouse xenografts measured by microCT is more accurate and reproducible than determined by ¹⁸F-FDG-microPET or external caliper

Authors: Mette Munk Jensen, Jesper Tranekjær Jørgensen, Tina Binderup and Andreas Kjær

Content type: Research article

16 October 2008
CT features in abdominal tuberculosis: 20 years experience.

Authors: Tariq Sinan, Mehraj Sheikh, Salwa Ramadan, Sukhpal Sahwney and Abdulla Behbehani

Content type: Research article

12 November 2002

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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

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.

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

July: Open data and data sharing

Data are the cornerstones to transparency and reproducibility of research. At the BMC Series, we offer different article types to help you share your data. Explore some of our open access data publications in this focus issue and browse through the additional reading and resources to learn more about this topic.

June: Water and Sanitation.

In celebration of World Environment Day on the 5^th June, the BMC Series presents a focus issue on Water and Sanitation. We have collated open-access content from across our journals to highlight research into clean water, investigating links between water and health and our overall relationship with water.

BMC Series Blog

Protecting an investment

16 July 2021
Taking care of the patient- not just the disease

14 July 2021
Call for Papers! Introducing BMC Oral Health’s New Collection titled “Teledentistry and Distance Learning: Improving Access to Oral Health Care and Education”

12 July 2021

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BMC Medical Imaging

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

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

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

Articles

Extracellular volume-guided late gadolinium enhancement analysis for non-ischemic cardiomyopathy: The Women’s Interagency HIV Study

Preliminary study on the application of renal ultrasonography radiomics in the classification of glomerulopathy

Retroperitoneum ganglioneuroma: imaging features and surgical outcomes of 35 cases at a Chinese Institution

An evaluation of performance measures for arterial brain vessel segmentation

Three-stage segmentation of lung region from CT images using deep neural networks

Age and gender specific normal values of left ventricular mass, volume and function for gradient echo magnetic resonance imaging: a cross sectional study

Is fasting a necessary preparation for abdominal ultrasound?

MRCP compared to diagnostic ERCP for diagnosis when biliary obstruction is suspected: a systematic review

Tumor volume in subcutaneous mouse xenografts measured by microCT is more accurate and reproducible than determined by ¹⁸F-FDG-microPET or external caliper

CT features in abdominal tuberculosis: 20 years experience.

Aims and scope

Call for Content: AI in Medical Imaging

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BMC Series Focus Issues

July: Open data and data sharing

June: Water and Sanitation.

BMC Series Blog

Protecting an investment

Taking care of the patient- not just the disease

Call for Papers! Introducing BMC Oral Health’s New Collection titled “Teledentistry and Distance Learning: Improving Access to Oral Health Care and Education”

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