ReviewSurgical and radiologic anatomy : SRA2026
Recent trends in medical image segmentation and volumetric analysis in human studies (2000-2025): a bibliometric review.
Review in Surgical and radiologic anatomy : SRA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis bibliometric review examined trends in the digital analysis of anatomical structures, focusing on volumetric analysis and segmentation studies from 2000 to 2025. The aim was to identify research patterns, collaborations, and emerging themes, and to evaluate the influence of advances in artificial intelligence and medical imaging.
methodsArticles indexed in the Web of Science Core Collection were analyzed using VOSviewer and Web of Science Analytics. A total of 3172 publications were assessed for trends in output, citations, authors, institutions, countries, and keyword networks.
resultsThe rise in Artificial Intelligence and deep learning keywords after 2020 coincides with increasing publication output. The most cited authors were Ashburner, J. and Friston, K.J., and the most cited work was “A Survey on Deep Learning in Medical Image Analysis” by Litjens et al. (Med Image Anal 42:60–88, 2017. https://doi.org/10.1016/j.media.2017.07.005 ). Neuroanatomical research dominated, with the hippocampus and the brain as the most-studied structures. MRI, deep learning, and segmentation were leading keywords. Harvard University and the University of Johns Hopkins University ranked highest in productivity, while Neuroimage and IEEE Transactions on Medical Imaging ranked highest in influence. The USA led in both publication output and citations.
conclusionArtificial Intelligence-driven digital technologies are increasingly shaping anatomical research. Despite a strong focus on neuroscience, peripheral organs and non-neurological applications remain underexplored, offering opportunities for future work in fields such as anatomy, orthopedics, and dentistry.
Indexed as
Identifiers
41824057What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.