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J. Imaging
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Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges...
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Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges and Applications, 2nd Edition

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 4851

Editor

Dr. Ebrahim Karami

E-Mail Website
Guest Editor
Department of Engineering and Applied Sciences, Memorial University, St. John’s, NL A1B 3X5, Canada
Interests: machine learning; computer vision; biomedical engineering; wireless communications and networks; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the latest advancements, challenges, and applications associated with deep learning techniques in the field of biomedical image segmentation and classification. Biomedical image analysis plays a crucial role in various medical domains, enabling the accurate identification, segmentation, and classification of structures, organs, and anomalies. Deep learning, with its ability to learn complex features and patterns from large-scale datasets, has revolutionized biomedical image analysis, offering significant improvements in segmentation and classification accuracy and efficiency. This Special Issue welcomes the submission of original research papers, review articles, and case studies that present novel deep learning methodologies, architectures, and algorithms, as well as their practical applications and implications in biomedical image segmentation and classification. This collection of contributions will provide a comprehensive overview of the current state-of-the-art techniques, identify challenges and limitations, and pave the way for future research directions in this rapidly evolving field.

This Special Issue is a follow up to a previous one. We appreciate the authors’ valuable contributions to the 1st edition, which can be accessed via the following link: https://www.mdpi.com/journal/jimaging/special_issues/DZ7T60VI90.

Dr. Ebrahim Karami
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. 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

  • biomedical image segmentation
  • biomedical image classification
  • lesion detection with deep learning
  • cancer detection and classification with deep learning
  • computer-aided diagnosis
  • deep learning for MRI and FMRI imaging
  • deep learning for X-ray imaging
  • deep learning for ultrasound imaging
  • deep learning for infrared imaging
  • deep learning for biomedical object localization
  • biomedical image denoising and enhancement using deep learning
  • 3D and 4D biomedical image analysis
  • multimodal and multiscale biomedical data fusion
  • synthetic data generation and data augmentation
  • explainability, interpretability, and uncertainty quantification
  • ethics, bias, and federated learning in biomedical imaging

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Related Special Issue

Published Papers (6 papers)

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Research

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29 pages, 24005 KB  
Article
YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs
by Gianmarco Scarano, Simone Agostinelli, Irene Amerini and Piero Papi
J. Imaging 2026, 12(6), 272; https://doi.org/10.3390/jimaging12060272 - 20 Jun 2026
Viewed by 198
Abstract
Chronic periapical periodontitis is a persistent inflammatory disease characterized by progressive bone destruction around the tooth apex. Manual radiographic detection of these lesions is subjective and time-consuming, highlighting the need for automated diagnostic tools. This paper presents a unified deep learning framework for [...] Read more.
Chronic periapical periodontitis is a persistent inflammatory disease characterized by progressive bone destruction around the tooth apex. Manual radiographic detection of these lesions is subjective and time-consuming, highlighting the need for automated diagnostic tools. This paper presents a unified deep learning framework for joint tooth segmentation and periapical lesion detection in panoramic radiographs. Our approach employs a joint process: first, a deep learning model identifies and segments individual teeth according to standard dental numbering systems, while a second one detects periapical lesions within the tooth regions obtained from the segmentation outputs in the first stage. The framework incorporates an advanced loss function (Powerful IoU v2) to improve bounding-box regression accuracy and a spatial association mechanism to map detected lesions to specific teeth based on geometric overlap analysis. Our proposed tooth segmentation model achieves an mAP@50 of 97.7% and a mean Dice coefficient of 93.5%, while the periapical lesion detector reaches an mAP@50 of 91.9%. Furthermore, our region-of-interest approach yields a 3.49× computational speedup, averaging 0.1589 s per radiograph when compared to full-image processing. Trained exclusively on open-source datasets, this reproducible framework achieves explicit tooth-to-lesion mapping, providing an efficient and practical tool for periapical lesion screening. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges and Applications, 2nd Edition)
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14 pages, 1974 KB  
Article
Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences
by Xinyuan Xiang, Wenyu Yin and Jiayue Li
J. Imaging 2026, 12(6), 271; https://doi.org/10.3390/jimaging12060271 - 18 Jun 2026
Viewed by 242
Abstract
Pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) provides an endpoint for treatment evaluation in breast cancer. Multi-sequence breast MRI can support pCR prediction, but routine examinations may lack usable T1-weighted or T2-weighted sequences. Many models merge radiomic and deep features by concatenation, [...] Read more.
Pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) provides an endpoint for treatment evaluation in breast cancer. Multi-sequence breast MRI can support pCR prediction, but routine examinations may lack usable T1-weighted or T2-weighted sequences. Many models merge radiomic and deep features by concatenation, leaving the interaction between handcrafted descriptors and learned representations weakly specified. We developed a radiomics-guided framework for pCR prediction from multi-sequence breast MRI. The model uses a multi-branch 2.5D encoder for sequence-specific features, radiomics-guided channel recalibration, and masked token fusion to aggregate available sequence tokens. We evaluated the framework on 157 patients from the I-SPY1 Trial cohort with patient-level five-fold cross-validation, fixed sequence-combination analysis, and slice-window sensitivity analysis. The full model achieved 78.4% accuracy and 0.809 AUC, compared with 75.8% accuracy and 0.788 AUC for the strongest channel-concatenation baseline. In this cohort, radiomics-guided multi-sequence learning was feasible, with external validation required before clinical interpretation. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges and Applications, 2nd Edition)
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22 pages, 6346 KB  
Article
Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology
by Dongsheng Ruan, Xiaolin Zhang, Zihan Yuan, Ziqian Lu, Ling Xia and Mingfeng Jiang
J. Imaging 2026, 12(6), 269; https://doi.org/10.3390/jimaging12060269 - 18 Jun 2026
Viewed by 261
Abstract
Myocardial scar and edema segmentation from multi-sequence cardiac magnetic resonance (MS-CMR) is important for myocardial infarction assessment, but remains challenging due to heterogeneous modal characteristics, severe class imbalance, and the small, ambiguous nature of pathological regions. To address these issues, a dynamic synergistic [...] Read more.
Myocardial scar and edema segmentation from multi-sequence cardiac magnetic resonance (MS-CMR) is important for myocardial infarction assessment, but remains challenging due to heterogeneous modal characteristics, severe class imbalance, and the small, ambiguous nature of pathological regions. To address these issues, a dynamic synergistic segmentation network (DSS-Net) is proposed for myocardial pathology segmentation. The framework adopts a coarse-to-fine strategy, in which a coarse stage first segments the myocardium to provide anatomical priors and region constraints, and a fine stage then delineates scar and edema within the myocardium-aware space. In addition, a Modality Dynamic Fusion Module (MDFM) is designed to adaptively emphasize pathology-relevant modal information, and a Stage Feature Aggregation Module (SFAM) is introduced to enhance cross-stage feature interactions and fine-grained lesion representation. Experiments on the MyoPS 2020 and MyoPS 2024 datasets demonstrate that DSS-Net achieves competitive and balanced performance, reaching Dice scores of 0.706 for scar and 0.753 for edema on MyoPS 2020. Additionally, compared with SOTA methods in the MyoPS 2020 Challenge, the proposed method attains comparable scar segmentation performance while maintaining a more balanced trade-off between sensitivity and specificity. These findings suggest that combining anatomical guidance with pathology-aware multi-modal learning is a promising strategy for robust myocardial pathology segmentation in MS-CMR images. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges and Applications, 2nd Edition)
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26 pages, 5686 KB  
Article
Cell Structure Segmentation in TEM Images of Murine Skin Melanoma Cells by Deep Learning Model
by Mikhail A. Genaev, Izabella S. Gogaeva, Iuliia S. Taskaeva, Nataliya P. Bgatova, Mikhail V. Kozhekin, Evgeniy G. Komyshev and Dmitry A. Afonnikov
J. Imaging 2026, 12(5), 215; https://doi.org/10.3390/jimaging12050215 - 18 May 2026
Viewed by 600
Abstract
Mitochondria–endoplasmic reticulum contact sites (MERCs) are known as the specialized areas that are involved in a large number of intracellular signaling pathways that regulate Ca2+ homeostasis, lipid transport, mitochondrial dynamics, cell death, and autophagy. Understanding MERC dynamics has important therapeutic implications in [...] Read more.
Mitochondria–endoplasmic reticulum contact sites (MERCs) are known as the specialized areas that are involved in a large number of intracellular signaling pathways that regulate Ca2+ homeostasis, lipid transport, mitochondrial dynamics, cell death, and autophagy. Understanding MERC dynamics has important therapeutic implications in cancer, as these contacts regulate fundamental cellular processes and MERCs represent promising targets for therapeutic interventions aimed at improving cancer treatment outcomes. Despite the accumulated data, the role of MERCs in carcinogenesis still remains unknown; thus, it seems promising to search for new tools facilitating the study of MERCs in tumor cells. The structure of MERCs can be examined in great detail using transmission electron microscopy (TEM). Currently, several hundred TEM images are required to obtain reliable data on these contacts. The speed of data processing can be significantly improved by using fast and accurate image analysis techniques based on deep learning models. In this study, five U-Net models with a ResNet34 encoder network were evaluated, including the basic U-Net-Vanilla architecture as well as models incorporating various attention blocks and blocks capturing multilevel image structure, for the segmentation of mitochondria and the endoplasmic reticulum (ER). The best performance on the test dataset was demonstrated by the U-Net-scSE network, with F1 scores of 0.872 for mitochondria and 0.744 for the ER being achieved. Two models were tested for their ability to leverage pre-training on external datasets (Lucchi++, Kasthuri++, and DeepPi-EM). Additionally, models pre-trained on the CEM500K dataset were evaluated after the parameters had been tuned on the data. It was demonstrated by the results that pre-training or the use of pre-trained networks did not lead to an improvement in the IoU and F1 metrics on the test dataset. Subsequent image analysis was conducted to assess two types of MERCs in the segmented images. Finally, the free and user-friendly UltraNet web server was developed for automated analysis of mitochondria, ER, and MERCs using TEM images. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges and Applications, 2nd Edition)
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Review

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31 pages, 2226 KB  
Review
Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models
by Diego Martí-Pérez, Valery Naranjo and Adrián Colomer
J. Imaging 2026, 12(7), 297; https://doi.org/10.3390/jimaging12070297 - 2 Jul 2026
Viewed by 101
Abstract
Cell segmentation plays a key role in a wide range of biomedical imaging applications, from single-cell analysis to pathology assessment. While classical deep learning architectures such as U-Net, StarDist, and HoVer-Net have set strong baselines, their reliance on domain-specific training limits generalization across [...] Read more.
Cell segmentation plays a key role in a wide range of biomedical imaging applications, from single-cell analysis to pathology assessment. While classical deep learning architectures such as U-Net, StarDist, and HoVer-Net have set strong baselines, their reliance on domain-specific training limits generalization across diverse microscopy modalities. The emergence of foundation models, particularly the Segment Anything Model (SAM) and its derivatives, has introduced a paradigm shift toward more universal and adaptable segmentation frameworks. In this review, we summarize key advances in microscopy cell segmentation, highlighting both traditional methods and recent foundation model-based approaches. Beyond surveying the literature, we present an experimental comparison of four representative models—our proposed YOLO-SAM, along with CellSAM, Cellpose-SAM, and StarDist—tested on both fluorescence and brightfield microscopy spanning diverse cell populations and shapes. Our findings illustrate trade-offs between accuracy, robustness, and adaptability, with foundation-based models showing particular promise for cross-domain performance. By combining a comprehensive review with systematic benchmarking, this work provides practical guidance for researchers and outlines current challenges and future opportunities in developing robust, generalizable cell segmentation methods for microscopy. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges and Applications, 2nd Edition)
21 pages, 1453 KB  
Review
Current Trends and Future Opportunities of AI-Based Analysis in Mesenchymal Stem Cell Imaging: A Scoping Review
by Maksim Solopov, Elizaveta Chechekhina, Viktor Turchin, Andrey Popandopulo, Dmitry Filimonov, Anzhelika Burtseva and Roman Ishchenko
J. Imaging 2025, 11(10), 371; https://doi.org/10.3390/jimaging11100371 - 18 Oct 2025
Cited by 2 | Viewed by 2598
Abstract
This scoping review explores the application of artificial intelligence (AI) methods for analyzing mesenchymal stem cells (MSCs) images. The aim of this study was to identify key areas where AI-based image processing techniques are utilized for MSCs analysis, assess their effectiveness, and highlight [...] Read more.
This scoping review explores the application of artificial intelligence (AI) methods for analyzing mesenchymal stem cells (MSCs) images. The aim of this study was to identify key areas where AI-based image processing techniques are utilized for MSCs analysis, assess their effectiveness, and highlight existing challenges. A total of 25 studies published between 2014 and 2024 were selected from six databases (PubMed, Dimensions, Scopus, Google Scholar, eLibrary, and Cochrane) for this review. The findings demonstrate that machine learning algorithms outperform traditional methods in terms of accuracy (up to 97.5%), processing speed and noninvasive capabilities. Among AI methods, convolutional neural networks (CNNs) are the most widely employed, accounting for 64% of the studies reviewed. The primary applications of AI in MSCs image analysis include cell classification (20%), segmentation and counting (20%), differentiation assessment (32%), senescence analysis (12%), and other tasks (16%). The advantages of AI methods include automation of image analysis, elimination of subjective biases, and dynamic monitoring of live cells without the need for fixation and staining. However, significant challenges persist, such as the high heterogeneity of the MSCs population, the absence of standardized protocols for AI implementation, and limited availability of annotated datasets. To advance this field, future efforts should focus on developing interpretable and multimodal AI models, creating standardized validation frameworks and open-access datasets, and establishing clear regulatory pathways for clinical translation. Addressing these challenges is crucial for accelerating the adoption of AI in MSCs biomanufacturing and enhancing the efficacy of cell therapies. Full article
(This article belongs to the Special Issue Deep Learning in Biomedical Image Segmentation and Classification: Advancements, Challenges and Applications, 2nd Edition)
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