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Bioengineering
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Recent Advances in Biomedical Imaging, Third Edition
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Recent Advances in Biomedical Imaging, Third Edition

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 984

Editor

Prof. Dr. Cuneyt M. Alper

E-Mail Website
Guest Editor
1. Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
2. Department of Pediatric Otolaryngology, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA
Interests: imaging; biomedical imaging; otitis media; ear conditions; respiratory tract infections
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biomedical imaging has arguably demonstrated the most rapid advancements in the entire biomedical field in the past decade. Besides the expansion of established imaging instrumentation into broader applications in tissue, cellular, and molecular diagnostic imaging, there have been substantial modifications in the imaging protocols that have advanced the capabilities of these existing imaging modalities. Technological advancements are stimulating further novel approaches in diagnosis and measuring as well as monitoring the outcomes of treatments. The adaptation of innovations in imaging technologies, methods, and protocols for broader applications is often limited by the inability to share an innovation with investigators outside the likely narrow field in which it originated. Therefore, it is crucial to facilitate the sharing of such advances in biomedical imaging occurring in one field with other fields. A broader vision with which to explore the full potential of an innovation often requires adding a new, perhaps outside, perspective. This Special Issue of Bioengineering aims to serve as a medium for such interdisciplinary exchange and to stimulate the expansion of applications of innovations, perhaps by facilitating new collaborations between various fields and investigators. The next big breakthrough in biomedical imaging may come from diverse areas of expertise coming together and finding new ways forward.

This is the third volume of our Special Issue, "Recent Advances in Biomedical Imaging". Please feel free to download and read it freely via the following link:
https://www.mdpi.com/journal/bioengineering/special_issues/24F01PJ541
https://www.mdpi.com/journal/bioengineering/special_issues/71V481672J

Prof. Dr. Cuneyt M. Alper
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.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering 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 2700 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

  • imaging innovations
  • advances in imaging CT
  • scans MRI
  • ultrasound nuclear medicine
  • PET fluoroscopy
  • interventional radiology
  • combined imaging modalities
  • automated segmentation
  • machine learning

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Published Papers (2 papers)

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20 pages, 17378 KB  
Article
Concordance of Cataract Biometry Measurements Obtained via Swept-Source Optical Coherence Tomography Versus Optical Biometry
by Ramadhan Ahmed, Kamini Narendra Reddy, Matthew Zeng, Mahad Mohamed and Nakul Shekhawat
Bioengineering 2026, 13(8), 874; https://doi.org/10.3390/bioengineering13080874 - 28 Jul 2026
Abstract
Purpose: To examine concordance in biometry, keratometry, and intraocular lens (IOL) calculation between the Heidelberg Anterion Cataract App and the Zeiss IOLMaster 700 in a US population presenting for cataract evaluation. Methods: In this retrospective cross-sectional study, 64 eyes of 40 [...] Read more.
Purpose: To examine concordance in biometry, keratometry, and intraocular lens (IOL) calculation between the Heidelberg Anterion Cataract App and the Zeiss IOLMaster 700 in a US population presenting for cataract evaluation. Methods: In this retrospective cross-sectional study, 64 eyes of 40 patients presenting for cataract evaluation underwent imaging with both devices. Agreement was assessed for axial length (AL), anterior chamber depth (ACD), lens thickness (LT), white-to-white distance (WTW), central corneal thickness (CCT), pupil diameter (PD), and anterior, posterior, and total keratometry using Lin’s concordance correlation coefficient (CCC), Bland–Altman analysis and generalized estimating equations. Non-toric and toric IOL calculations were performed with the Barrett Universal II and Barrett True-K Toric formulas, respectively. Results: Concordance was excellent for AL, ACD, LT, and CCT (CCC > 0.980), with no significant difference in AL. Compared with IOLMaster 700, Anterion measured larger PD (+0.56 mm), deeper ACD (+0.07 mm), thicker LT (+0.07 mm), thinner CCT (−3.19 μm), and smaller WTW (−0.22 mm; all p < 0.01). Anterior keratometry showed excellent concordance (CCC ≥ 0.980) despite small flatter offsets in average K, K1, K2 and difference in (Δ)K (−0.13, −0.08, −0.16 D, −0.10). Concordance was poor for posterior keratometry (average K: CCC = 0.529; Anterion steeper by 0.34 D), fairly good for total keratometry (average K: CCC = 0.891, Anterion flatter by 0.68 D), and these systematic inter-device offsets may be clinically relevant. For non-toric calculations, unrounded spherical IOL power agreed within 0.50 D in 88.88% (N = 56/63) of eyes and predicted residual refractive error within 0.25 D in 95.24% (N = 60/63). For toric calculations, predicted residual refractive error and residual astigmatism agreed within 0.25 D in 95.16% (N = 59/62) and 85.48% (N = 53/62) of eyes, respectively. Conclusions: Anterion and IOLMaster 700 showed strong agreement in core biometry, anterior keratometry, and predicted refractive outcomes, though posterior and total keratometry differed systematically. Postoperative refractive outcome studies are needed before the devices can be considered interchangeable in routine practice. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Imaging, Third Edition)
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9 pages, 735 KB  
Article
Importance of the Quality of Annotation: Impact of Simulated Inter-Observer Variability on Deep Neural Network Automated Segmentation Model Performance
by Dominic LaBella, Michaela Kop, Xuan Qi, Hunter Stecko, Baris Turkbey, Hannah Scanlon and Thomas Sanford
Bioengineering 2026, 13(6), 691; https://doi.org/10.3390/bioengineering13060691 - 17 Jun 2026
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Abstract
Background: Deep neural network based prostate segmentation depends on manual annotations, yet the effect of annotation variability on model performance remains underexplored. Methods: Prostate contours were manually delineated by an expert clinician on 119 T2-weighted MR images from the PROSTATEx Challenge 2017 training [...] Read more.
Background: Deep neural network based prostate segmentation depends on manual annotations, yet the effect of annotation variability on model performance remains underexplored. Methods: Prostate contours were manually delineated by an expert clinician on 119 T2-weighted MR images from the PROSTATEx Challenge 2017 training dataset, and slice-wise synthetic radial modifications of 1–10 mm were applied to create 10 modified training datasets plus an unmodified baseline. Identical SegResNet models were trained with Auto3DSeg/MONAI and evaluated against unmodified validation and test sets using the Dice similarity coefficient (DSC). Results: Mean test DSC decreased from 0.917 for the baseline model to 0.856 at 10 mm modification. Models trained with small annotation perturbations of 1–5 mm maintained DSC values of at least 0.90, whereas performance declined significantly beyond 5 mm. Pairwise DSC agreement across modified annotations also fell as modification amplitude increased. Conclusions: Prostate segmentation models tolerated modest annotation variability but degraded substantially when variability exceeded 5 mm, underscoring the importance of annotation quality when training and benchmarking DNN-based automated segmentation models. Full article
(This article belongs to the Special Issue Recent Advances in Biomedical Imaging, Third Edition)
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