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J. Imaging
Special Issues
Current Progress in Medical Image Segmentation
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Current Progress in Medical Image Segmentation

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 29 August 2025 | Viewed by 1123

Special Issue Editor

Dr. Krishna Chaitanya

E-Mail Website
Guest Editor
ETH Zürich, Zurich, Switzerland
Interests: medical image segmentation; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical image segmentation (MIS) is an essential component of modern medical imaging, enabling the precise and reliable extraction of meaningful information from medical images.

Despite significant advancements, MIS still faces numerous challenges which include the following:

  • Data-related challenges (due to limited annotated data, data heterogeneity, and class imbalance);
  • Model/algorithmic challenges (complexity and resource intensive nature);
  • Biological variability challenges (anatomical and disease variability);
  • Clinical integration challenges (workflow integration and user-acceptance);
  • Technical challenges (real-time segmentation).

These challenges pose an ever-growing need for continuous innovation to enhance the accuracy, reliability, and efficiency of MIS.

Therefore, we warmly invite you and your colleagues to submit original research articles, reviews, systematic reviews, meta-analyses, brief reports, and case reports that address issues related to medical image segmentation, specifically focusing on the development of new algorithms and techniques, addressing data imbalances, enhancing model generalization, and leveraging novel architectures.

You may choose our Joint Special Issue in Life.

Dr. Krishna Chaitanya
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 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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

  • medical image segmentation
  • deep learning
  • generative models
  • algorithm
  • clinical integration
  • biological variability
  • transformer architectures
  • interactive segmentation
  • interdisciplinary collaboration

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

20 pages, 7305 KiB  
Article
Systematic and Individualized Preparation of External Ear Canal Implants: Development and Validation of an Efficient and Accurate Automated Segmentation System
by Yanjing Luo, Mohammadtaha Kouchakinezhad, Felix Repp, Verena Scheper, Thomas Lenarz and Farnaz Matin-Mann
J. Imaging 2025, 11(8), 264; https://doi.org/10.3390/jimaging11080264 - 8 Aug 2025
Viewed by 259
Abstract
External ear canal (EEC) stenosis, often associated with cholesteatoma, carries a high risk of postoperative restenosis despite surgical intervention. While individualized implants offer promise in preventing restenosis, the high morphological variability of EECs and the lack of standardized definitions hinder systematic implant design. [...] Read more.
External ear canal (EEC) stenosis, often associated with cholesteatoma, carries a high risk of postoperative restenosis despite surgical intervention. While individualized implants offer promise in preventing restenosis, the high morphological variability of EECs and the lack of standardized definitions hinder systematic implant design. This study aimed to characterize individual EEC morphology and to develop a validated automated segmentation system for efficient implant preparation. Reference datasets were first generated by manual segmentation using 3D SlicerTM software version 5.2.2. Based on these, we developed a customized plugin capable of automatically identifying the maximal implantable region within the EEC and measuring its key dimensions. The accuracy of the plugin was assessed by comparing it with manual segmentation results in terms of shape, volume, length, and width. Validation was further performed using three temporal bone implantation experiments with 3D-Bioplotter©-fabricated EEC implants. The automated system demonstrated strong consistency with manual methods and significantly improved segmentation efficiency. The plugin-generated models enabled successful implant fabrication and placement in all validation tests. These results confirm the system’s clinical feasibility and support its use for individualized and systematic EEC implant design. The developed tool holds potential to improve surgical planning and reduce postoperative restenosis in EEC stenosis treatment. Full article
(This article belongs to the Special Issue Current Progress in Medical Image Segmentation)
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Graphical abstract

20 pages, 4292 KiB  
Article
A Novel Method for Analysing the Curvature of the Anterior Lens: Multi-Radial Scheimpflug Imaging and Custom Conic Fitting Algorithm
by María Arcas-Carbonell, Elvira Orduna-Hospital, María Mechó-García, Guisela Fernández-Espinosa and Ana Sanchez-Cano
J. Imaging 2025, 11(8), 257; https://doi.org/10.3390/jimaging11080257 - 1 Aug 2025
Viewed by 270
Abstract
This study describes and validates a novel method for assessing anterior crystalline lens curvature along vertical and horizontal meridians using radial measurements derived from Scheimpflug imaging. The aim was to evaluate whether pupil diameter (PD), anterior lens curvature, and anterior chamber depth (ACD) [...] Read more.
This study describes and validates a novel method for assessing anterior crystalline lens curvature along vertical and horizontal meridians using radial measurements derived from Scheimpflug imaging. The aim was to evaluate whether pupil diameter (PD), anterior lens curvature, and anterior chamber depth (ACD) change during accommodation and whether these changes are age-dependent. A cross-sectional study was conducted on 104 right eyes from healthy participants aged 21–62 years. Sixteen radial images per eye were acquired using the Galilei Dual Scheimpflug Placido Disk Topographer under four accommodative demands (0, 1, 3, and 5 dioptres (D)). Custom software analysed lens curvature by calculating eccentricity in both meridians. Participants were analysed as a total group and by age subgroups. Accommodative amplitude and monocular accommodative facility were inversely correlated with age. Both PD and ACD significantly decreased with higher accommodative demands and age. Relative eccentricity decreased under accommodation, indicating increased lens curvature, especially in younger participants. Significant curvature changes were detected in the horizontal meridian only, although no statistically significant differences between meridians were found overall. The vertical meridian showed slightly higher eccentricity values, suggesting that it remained less curved. By enabling detailed, meridionally stratified in vivo assessment of anterior lens curvature, this novel method provides a valuable non-invasive approach for characterizing age-related biomechanical changes during accommodation. The resulting insights enhance our understanding of presbyopia progression, particularly regarding the spatial remodelling of the anterior lens surface. Full article
(This article belongs to the Special Issue Current Progress in Medical Image Segmentation)
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