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BMC Medical Imaging is a peer-reviewed journal published by BMC (BioMed Central), covering Radiomics and Machine Learning in Medical Imaging, MRI in cancer diagnosis, Advanced X-ray and CT Imaging. Over its lifetime it has published 2,774 papers, which have received 39,385 citations.
| Publisher | BMC (BioMed Central) |
|---|---|
| Reference style | Numbered (Vancouver) Numbered — [1], [2] in the text 1. Smith A, Jones B, Lee C. A representative article title. BMC Medical Imaging. 2023;12(3):45–58.
Formats any DOI in BMC Medical Imaging style. No sign-up. |
| Publishes research in | Radiomics and Machine Learning in Medical Imaging MRI in cancer diagnosis Advanced X-ray and CT Imaging Cardiac Imaging and Diagnostics AI in cancer detection |
| ISSN | 1471-2342 |
| Citation impact (2-yr) | 3.68 |
| h-index | 73 |
| i10-index | 961 |
| Article processing charge | $2,072 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Capital Medical University |
| Journal website | link.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
BACKGROUND: Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric…
BACKGROUND: Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could…
BACKGROUND: Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning…
BACKGROUND: In animal studies tumor size is used to assess responses to anticancer therapy. Current standard for volumetric measurement of xenografted tumors is by external caliper, a method often affected by error. The aim of the present study was to evaluate if microCT gives more accurate and reproducible measures of tumor size in mice compared…
BACKGROUND: Magnetic particle imaging (MPI) is a new tomographic imaging technique capable of imaging magnetic tracer material at high temporal and spatial resolution. Image reconstruction requires solving a system of linear equations, which is characterized by a "system function" that establishes the relation between spatial tracer position and frequency response. This paper for the first…
