Recent Advances in Abdominal Imaging

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 791

Special Issue Editor


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Guest Editor
Department of Radiology, Medical Imaging Center, Faculty of Medicine, Semmelweis University, Korányi S. u. 2, 1083 Budapest, Hungary
Interests: medical imaging; diagnostic radiology; abdominal imaging; oncoradiology image analysis; radiomics; artificial intelligence elastography; MR relaxometry; multiparametric MR imaging
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Special Issue Information

Dear Colleagues,

This Special Issue on “Recent Advances in Abdominal Imaging” aims to collect high-quality, experimental and clinical studies, case series, reviews, and systematic meta-analyses that contribute to the evolving field of abdominal diagnostics. Special emphasis is placed on the quantitative evaluation of imaging data, supporting the transition to more precise, standardized, and biomarker-based diagnostic approaches in abdominal imaging.

Abdominal imaging has undergone rapid advancements in recent years, driven by innovations in imaging technologies such as photon-counting spectral CT, multiparametric quantitative MRI, and total-body perfusion PET/CT. These developments have significantly enhanced the visualization and characterization of abdominal diseases. The increasing integration of artificial intelligence, including radiomics and deep neural networks, is revolutionizing the field by enabling more accurate disease characterization, improving image reconstruction, reducing patient exposure, standardizing protocols, and accelerating imaging workflows. Additionally, new contrast agents and radiotracers have improved the detection of both anatomical structures and pathological changes. There is also growing interest in the development and clinical implementation of quantitative imaging biomarkers, which are steadily advancing toward routine use. All authors interested in shaping the future of abdominal imaging are cordially invited to contribute.

Dr. Pál Kaposi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • abdominal Imaging
  • photon-counting spectral CT
  • multiparametric quantitative MRI
  • total-body perfusion PET/CT

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Published Papers (1 paper)

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Research

12 pages, 5200 KB  
Article
Comparison of Manual, Semi-Automatic, and Automatic CT-Based Methods for Liver Volume Segmentation
by Berna Dogan, Sadik Bugrahan Simsek, Sefa Sonmez, Merve Nur Ozgen Sonmez, Omur Dasci and Zafer Ozmen
Diagnostics 2026, 16(5), 817; https://doi.org/10.3390/diagnostics16050817 - 9 Mar 2026
Viewed by 438
Abstract
Background/Objectives: To evaluate whether semi-automatic and automatic CT-based liver segmentation methods can provide clinically acceptable volumetric agreement compared with manual segmentation while improving processing efficiency in routine practice. Methods: CT images from 86 individuals were retrospectively analyzed. Liver volumes were calculated [...] Read more.
Background/Objectives: To evaluate whether semi-automatic and automatic CT-based liver segmentation methods can provide clinically acceptable volumetric agreement compared with manual segmentation while improving processing efficiency in routine practice. Methods: CT images from 86 individuals were retrospectively analyzed. Liver volumes were calculated using manual segmentation, RVX Semi-Automatic, RVX Deep Learning, and TotalSegmentator. Differences among methods were assessed using repeated-measures ANOVA. Agreement with manual segmentation was evaluated using a Bland–Altman analysis, while the Dice Similarity Coefficient (DICE) and Hausdorff Distance (HD) quantified spatial overlap and boundary deviation, respectively. Processing times were recorded. Results: Mean liver volumes were 1503.9 ± 356.0 cm3 (manual), 1512.6 ± 373.6 cm3 (RVX Semi-Automatic), 1549.8 ± 367.9 cm3 (RVX Deep Learning), and 1518.3 ± 365.8 cm3 (TotalSegmentator). RVX Deep Learning produced significantly higher volumes compared with manual segmentation (p < 0.001), whereas RVX Semi-Automatic and TotalSegmentator showed no significant differences (p > 0.05). DICE values were 0.911 ± 0.032, 0.946 ± 0.018, and 0.938 ± 0.021 for RVX Semi-Automatic, RVX Deep Learning, and TotalSegmentator, respectively. HD values were highest for the deep learning-based method. Processing times were shortest for RVX Deep Learning and longest for manual segmentation. Conclusions: Semi-automatic and automatic liver segmentation methods substantially reduce processing time while maintaining clinically acceptable volumetric agreement. Among the evaluated approaches, TotalSegmentator showed the closest agreement with manual segmentation, supporting its use in routine CT-based liver volumetry. Deep learning-based segmentation, although faster, tended to overestimate volume, potentially limiting its use in applications requiring high volumetric precision. Full article
(This article belongs to the Special Issue Recent Advances in Abdominal Imaging)
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