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The Total Medical Imaging Review

The Total Medical Imaging Review contains an extensive array of articles that discuss the latest advances in radiology. The articles are organized into two groups: intra and inter-modality. Each subgroup contains articles related to one or more imaging modalities and their clinical applications. Here are some of the highlights: Radiomics, Pre-screening, Full-text review, Segmentation, and Radiomics.


Radiomics is a method for quantitative description of medical images. The process involves a variety of factors. Quality of radiomic studies should be ensured by using checklists and guidelines. Moreover, open-source data can help improve reproducibility. This article reviews the use of radiomics in medical imaging review.

The study evaluated 553 original articles published in 61 peer-reviewed journals between 2013 and 2018. The articles were analyzed by identifying the topic area, radiological subspecialty, study design, statistical result, software used for feature calculation, and funding declaration. In addition, we included articles from journals that are online-only, citing the corresponding databases. The selected articles were categorized by topic and quality using machine learning techniques.

Different radiomic features are affected by different imaging settings, patient demographics, and image pre-processing. This variability can mask the true radiomic signature. In addition, it is important to understand that the radiomic signature is a composite of many features. To achieve reproducibility, these features should be standardized across datasets.


Pre-screening for medical problems before imaging can reduce the number of aborted or incomplete exams and save the institution time and money. However, few tools are available for diagnostic radiology settings. An integrated literature review was conducted to better understand the effectiveness of pre-screening for medical conditions.

A structured report will improve the reproducibility, comparability and repeatability of the findings. However, a standard structured report has not yet been developed for the general population screening setting. For cancer screening, it would be helpful to implement a classification system based on five categories (categories 1 and 2) ranging from normal findings to those with increasing oncological relevance. This classification would allow the use of different strategies for screening different subgroups of patients.

Full-text review

Most medical imaging journals use a single-blinded peer review system to evaluate submitted articles. While this type of review may not be as rigorous as double-blind peer review, it can still help in assessing the quality of research published in the field. It is common for medical imaging journals with higher impact factors to use this method. It is also common for lower-ranked journals, especially those in subspecialties.

The peer review process is an important part of the publishing process. It involves a formal process in which manuscripts are reviewed by experts who are not involved in the creation of the manuscript. The review process is considered important for the quality of published research. In medical imaging, this process is applied to all journals listed in Journal Citation Reports. However, this system is not without its flaws. Insufficient reviewer quality can degrade the quality of published research.

One way to address this problem is to ensure that the peer review process is blind. Most medical imaging journals employ a system of double-blind peer review. However, in some cases, single-blinded peer review is used. This type of review process is more expensive and complex than double-blinded or triple-blinded systems.


Thousands of algorithms are published every year in the field of biomedical image segmentation. In order to develop an effective algorithm, it is important to understand the basic architecture of the imaging system. The segmentation process requires an analysis of the image to identify the specific organ of interest. Image segmentation tasks require the computer to recognize small differences in gray values in voxels within the organ of interest and surrounding objects.

One of the most challenging areas of medical image segmentation is skin lesion segmentation. This is a very challenging task because the image contains many features such as different types of skin lesions, skin lines, hairs, air bubbles, and multicolored areas within the lesion. Moreover, the contrast between the lesion and neighboring areas is usually poor. For this reason, the use of a neutrosophic set is essential in skin lesion segmentation.

Once the first phase of the challenge is complete, teams are asked to download three more data sets and apply their previously developed algorithm on them. The teams were not allowed to change their method or submit the results until they had trained it with the new data sets.


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