Medical Image Segmentation with Deep Learning: Models, Datasets, and Best Practices

Medical image segmentation with deep learning is the process of training neural networks to assign pixel-level or voxel-level labels to anatomical structures and pathologies in medical images, enabling automated isolation of regions of interest for diagnosis, treatment planning, and monitoring. This article provides a practical framework for researchers, students, analysts, and life-science professionals who need to select architectures, source datasets, train models, and report results with reproducibility as a primary objective. The guidance draws on peer-reviewed literature and public resources to support concrete decisions at each stage of a segmentation project.

Scope and Reader Context

Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis [5]. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options, with deep learning techniques proving effective for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis [11]. Medical image segmentation isolates regions of interest from the background, assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues [8].

This article addresses the practical decisions that determine whether a segmentation project succeeds: choosing an architecture, finding and preparing data, configuring training, measuring performance, and documenting results. The content is oriented toward readers who will implement models instead of only read about them. Each section translates published evidence into actionable workflow choices.

At a Glance

The table below summarizes the primary architecture families, their typical use cases, and the evidence-backed considerations that should inform model selection.

Core Principles of Deep Learning Segmentation

What Segmentation Models Learn

Segmentation models learn a mapping from input images to dense label maps, where each pixel or voxel receives a class assignment. Fully convolutional encoder-decoder topologies, most notably the U-Net architecture, have emerged as the premier framework for dense pixel-level classification tasks [18]. The defining characteristic of the U-Net blueprint centers on its symmetrical contracting and expanding paths, interconnected by high-resolution skip connections [18]. This geometric layout enables the network to maintain precise localization capabilities and achieve exceptional segmentation accuracy even when trained on highly constrained, low-sample annotated datasets [18].

Why Medical Images Differ from Natural Images

Medical images present unique challenges compared to natural images, including low resolution, poor contrast, inconsistency, and scattered target regions [8]. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements [8]. Deep learning approaches for tooth segmentation, for example, employ convolutional neural networks or Transformers to derive feature maps from extensive training datasets, with tooth segmentation serving as a critical prerequisite for clinical dental analysis and surgical procedures [9]. These domain-specific constraints shape every downstream decision about architecture, preprocessing, and evaluation.

The Role of Anatomical Information

Deep learning has become widely used for medical image segmentation, yet there remain problems for which DL-based segmentation fails [7]. Some approaches achieve breakthroughs by using anatomical information, which is the crucial cue for manual segmentation [7]. Anatomy-aided DL methods systematically categorize anatomical information and corresponding representation methods, addressing known and potentially solvable challenges [7]. When planning a segmentation project, consider whether explicit anatomical priors could improve performance in cases where intensity information alone is insufficient.

Public Datasets for Segmentation Research

Dataset Availability and Quality

The further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets [5]. In the past five years, many studies have focused on addressing this challenge [5]. Public datasets have shown a transformative impact on automated medical segmentation, but these datasets often have varying label quality, ranging from manual expert annotations to AI-generated pseudo-annotations [20]. There is no systematic, reliable, and automatic quality control for these datasets [20].

Widely Used Public Datasets

Several public datasets recur across the segmentation literature. The Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 are the most common public datasets for pulmonary nodule detection and segmentation [12]. The Medical Decathlon, Automated Cardiac Diagnosis Challenge, and Multi-Modality Whole Heart Segmentation datasets support cardiac and multi-organ segmentation research [15]. The Liver Tumor Segmentation Challenge, Combined Healthy Abdominal Organ Segmentation, and 3D-IRCADb-01 datasets are used for liver tumor segmentation across CT and MRI modalities [25][17]. The Brain Tumor Segmentation challenges have significantly advanced research in brain tumor segmentation, with datasets growing in size, complexity, and scope from 2012 through 2024 [21].

Dataset Selection Criteria

When selecting a dataset for a segmentation project, evaluate the following factors:

1. Annotation quality and protocol. Determine whether labels were produced by manual expert annotation or AI-generated pseudo-annotations, since quality varies substantially across datasets [20].
2. Modality and anatomy match. Confirm that the imaging modality and anatomical region align with your research question. Cross-modality transfer is possible but requires careful architecture design [17].
3. Dataset size and class balance. Consider whether the dataset provides sufficient examples for each class you intend to segment.
4. Preprocessing conventions. BraTS datasets, for example, have incorporated refined preprocessing and annotation protocols over successive challenge years [21].
5. External validation potential. The value of external test datasets is emphasized in the literature on lung nodule segmentation [12].

Data Sharing and Management

Data sharing and code sharing are important for reproducibility in medical image segmentation research [12]. The National Institutes of Health maintains a Genomic Data Sharing Policy that governs the sharing of genomic data generated with NIH funding [3]. While this policy specifically addresses genomic data, the underlying principles of responsible data management apply to medical imaging datasets that contain protected health information. The FAIR Guiding Principles provide a framework for making data findable, accessible, interoperable, and reusable [4]. The European Bioinformatics Institute offers training resources for bioinformatics data management [1], and the National Center for Biotechnology Information provides data resources for biomedical research [2]. Apply these frameworks when depositing or accessing segmentation datasets.

Architecture Selection and Tradeoffs

U-Net and Its Variants

The U-Net architecture has become the standard baseline for medical image segmentation. Over the past decade, the progressive iteration of this paradigm has crystallized into five principal architectural variants: traditional U-Nets, specialized deep networks, innovative structural permutations, hybrid networks, and ensemble frameworks [18]. Each variation introduces key modifications to foundational components, such as custom-engineered feature extraction encoders, advanced localized decoding blocks, and highly targeted optimization loss functions [18].

Attention mechanisms have demonstrated measurable improvements. In a study of lung segmentation for pneumonia diagnosis, Attention U-Net achieved 1% better mean accuracy and 2% better mean Jaccard and Dice scores than U-Net, pruned U-Net, and U-Net++ [13]. The integration of attention mechanisms and augmentation significantly improved diagnostic accuracy and reliability [13].

nnU-Net as a Self-Configuring Framework

nnU-Net is a self-configuring method for deep learning-based biomedical image segmentation [23]. The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results [24]. However, many recent claims of superior performance over the U-Net baseline fail to hold up when scrutinized for common validation shortcomings, such as the use of inadequate baselines, insufficient datasets, and neglected computational resources [24]. The recipe for state-of-the-art performance is employing CNN-based U-Net models, including ResNet and ConvNeXt variants, using the nnU-Net framework, and scaling models to modern hardware resources [24].

Transformer-Based Architectures

Vision transformers have emerged as a promising solution to improve medical image segmentation, but they require larger training datasets than convolutional neural networks [22]. Data-efficient ViTs were proposed to overcome this obstacle, yet they are typically trained using a single source of data, which overlooks valuable knowledge that could be leveraged from other available datasets [22]. Naively combining datasets from different domains can result in negative knowledge transfer, a decrease in model performance on some domains with non-negligible inter-domain heterogeneity [22]. Multi-domain ViTs with domain adapters can mitigate data hunger and combat negative knowledge transfer by adaptively exploiting knowledge in multiple small data resources [22].

Hybrid and Specialized Architectures

Hybrid architectures combine multiple design principles to address specific clinical challenges. A deformable multi-head cross-attention mechanism integrated with a 3D U-Net achieved a Dice coefficient of 91.8% on Medical Decathlon, 92.3% on Automated Cardiac Diagnosis Challenge, and 93.2% on Multi-Modality Whole Heart Segmentation datasets [15]. The Adapted Dual-Encoder Shared-Tail U-Net integrates an MRI-pretrained encoder with a CT-specific encoder trained from scratch, enabling cross-organ and cross-modality transfer learning under data-scarce conditions [17]. This model reduced false positives by 67.6% relative to training from scratch and by 40.4% compared with frozen-weight transfer [17].

For liver tumor detection, a unified Inception-U-Net hybrid gravitational optimization model achieved a segmentation accuracy of 99.93%, an AUC score of 99.89%, a Dice coefficient of 0.997, and an IoU of 0.998 across LiTS, CHAOS, and 3D-IRCADb1 datasets [25]. For spinal endoscopy, an enhanced U-Net integrating a Convolutional Block Attention Module and an Atrous Spatial Pyramid Pooling module demonstrated high and consistent segmentation performance for the ligamentum flavum, nerve roots, and intervertebral disc [19].

Architecture Selection Decision Framework

Use the following criteria to select an architecture for a new project:

1. Dataset size. If you have fewer than a few hundred annotated images, start with a standard U-Net or nnU-Net configuration instead of a transformer-based model [22][18].
2. Modality and dimensionality. For 3D volumes such as CT and MRI, use 3D architectures or nnU-Net's automated configuration [23][24].
3. Computational budget. Binarized U-Nets retain high clinical accuracy with dramatically reduced computational cost, enabling deployment in wearable and point-of-care devices [14].
4. Domain shift. If you plan to transfer across modalities or organs, consider dual-encoder architectures that separate transferable and domain-specific features [17].
5. Baseline rigor. Compare any novel architecture against a properly configured nnU-Net baseline before claiming improvement [24].

Practical Training Workflow

Data Preprocessing

Preprocessing significantly affects segmentation performance. A study of dental caries segmentation in panoramic radiographs applied a pipeline including resizing, bilateral filtering, CLAHE contrast enhancement, unsharp masking, and normalization [16]. Models lacking augmentation or preprocessing performed substantially worse, with ResNet50 without augmentation showing severe degradation with a Dice score of 0.0068 [16]. The combined configuration also yielded the lowest validation loss and improved stability across folds [16].

Standard preprocessing steps include:

1. Resampling to a consistent voxel spacing for 3D data.
2. Intensity normalization to a standard range or distribution.
3. Cropping or padding to a consistent input size.
4. Artifact reduction appropriate to the modality.

Data Augmentation

Data augmentation expands the effective training set and improves generalization. In the dental caries study, augmentation including rotation, shifting, shearing, zooming, and horizontal flipping expanded the dataset from 500 to 1,000 images [16]. The Xception-based U-Net with both augmentation and preprocessing achieved the highest performance with a Dice score of 0.9517 and IoU of 0.9079, while the standard U-Net achieved a Dice score of 0.7380 under the same configuration [16].

Augmentation choices should respect anatomical constraints. For example, horizontal flipping is appropriate for symmetric structures but may be inappropriate for organs with consistent left-right positioning. Elastic deformations can simulate realistic tissue variation but should be applied with caution.

Training Configuration

Training configuration includes loss function selection, optimizer choice, learning rate scheduling, and early stopping criteria. Common loss functions in medical image segmentation include Dice loss, cross-entropy loss, and combinations thereof [8]. The choice of loss function should reflect the evaluation metric and the class imbalance characteristics of the dataset.

For small datasets, transfer learning can improve performance. The ADEST-U-Net study demonstrated that adapting representations learned from brain tumor segmentation in MRI to liver tumor segmentation in CT under data-scarce conditions achieved a Dice coefficient of 0.7189 and a precision of 0.7727 [17]. This approach enabled controlled interaction between transferable and domain-specific features [17].

Validation Strategy

Rigorous validation is essential for credible results. The dental caries segmentation study used five-fold cross-validation to assess model stability [16]. The spinal endoscopy study assessed model stability via five-fold cross-validation and evaluated final performance on an independent test set of 100 images [19]. Statistical significance was assessed with paired t-tests [19].

Common validation shortcomings in the literature include inadequate baselines, insufficient datasets, and neglected computational resources [24]. To avoid these pitfalls:

1. Use a properly configured nnU-Net as a baseline for 3D segmentation tasks [24].
2. Report the computational resources used for training and inference [24].
3. Validate on external test datasets when available [12].
4. Report confidence intervals or standard deviations across cross-validation folds [16][19].

Performance Evaluation and Metrics

Standard Segmentation Metrics

The Dice similarity coefficient and Intersection over Union are the most widely reported segmentation metrics. The Dice coefficient measures the overlap between predicted and ground truth segmentations, while IoU measures the intersection divided by the union. Performance evaluation in lung nodule segmentation covers multiple metrics, including sensitivity and the Dice coefficient [12].

Accuracy, precision, recall, and F1-score provide additional perspectives on model performance. The dental caries segmentation study evaluated models using Dice coefficient, IoU, accuracy, precision, recall, F1-score, and AUC-ROC [16]. The caries detection systematic review found that image, object, or pixel accuracy ranged between 68% and 99% across studies [10].

Metric Selection by Task

Different tasks require different metric priorities:

1. Detection tasks. Sensitivity and specificity are critical for identifying lesions [12].
2. Segmentation tasks. Dice coefficient and IoU measure spatial overlap [16].
3. Classification tasks. Accuracy and AUC-ROC measure discrimination [10].
4. Clinical utility. Consider task-specific metrics such as false positive reduction, which is particularly important in surgical navigation [17][19].

Reporting Standards

Standardized reporting enables comparison across studies. The lung nodule segmentation systematic review underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency [12]. Report the following for every segmentation experiment:

1. Dataset characteristics, including size, modality, and annotation protocol.
2. Preprocessing and augmentation pipeline.
3. Architecture configuration and parameter count.
4. Training hyperparameters and computational resources.
5. Validation strategy and metric definitions.
6. Results with measures of variability.

Label Quality Assessment and Control

The Label Quality Problem

Label quality varies substantially across public medical segmentation datasets [20]. A regression model called Quality Sentinel was trained on over 4 million image-label pairs to estimate label quality compared with manual annotations [20]. The predicted label quality quantified by Dice similarity coefficient shares a strong correlation with ground truth quality, with a positive correlation coefficient of 0.902 [20]. Quality Sentinel can predict the label quality of 142 body structures [20].

Practical Quality Control Measures

Implement the following quality control measures in a segmentation project:

1. Visual inspection. Review a random sample of segmentation masks against source images before training.
2. Automated quality estimation. Use tools such as Quality Sentinel to identify poorly annotated labels in public datasets [20].
3. Annotation cost optimization. Quality Sentinel identified and corrected poorly annotated labels, achieving one-third reduction in annotation costs with optimal budgeting on TotalSegmentator [20].
4. Pseudo-label filtering. Focusing on high-quality pseudo labels resulted in a 33% to 88% performance boost over entropy-based methods, with a cost of 31% time and 4.5% memory [20].

Annotation Protocols

When creating new annotations, establish clear protocols for:

1. Anatomical boundary definitions.
2. Handling ambiguous cases.
3. Multi-rater agreement assessment.
4. Expert validation of annotations [16].

The dental caries segmentation study used manually annotated, expert-validated segmentation masks [16]. The spinal endoscopy study constructed a large-scale, expert-annotated dataset of 1,000 spinal endoscopic images [19].

Common Failure Patterns and Mitigation

Overfitting on Small Datasets

Deep learning models require large training datasets, and the lack of large-sized and well-annotated datasets is a major bottleneck [5]. Overfitting manifests as high training performance with poor validation performance. Mitigation strategies include data augmentation, transfer learning, and regularization [16][17].

Negative Knowledge Transfer in Multi-Domain Settings

Combining datasets from different domains can result in negative knowledge transfer, decreasing model performance on some domains with non-negligible inter-domain heterogeneity [22]. Multi-domain architectures with domain adapters can mitigate this problem [22].

Inadequate Baselines

Many claims of superior performance over the U-Net baseline fail to hold up when scrutinized for common validation shortcomings [24]. Always compare against a properly configured nnU-Net baseline before claiming architectural improvement [24].

Poor Generalization Across Institutions

Models trained on data from one institution may not generalize to data from another due to differences in acquisition protocols and patient populations. External test datasets are valuable for assessing generalization [12].

Class Imbalance

Medical images often have severe class imbalance, with background regions dominating foreground structures. Loss functions such as Dice loss and focal loss can address this issue [8].

Limitations and Interpretation Boundaries

Performance Variability Across Studies

The caries detection systematic review found a wide range of performance metrics across studies, with accuracy ranging from 68% to 99% depending on imaging modality and task [10]. Meta-analysis was not performed due to heterogeneity in study methods and performance measurements [10]. This variability underscores the importance of task-specific evaluation instead of relying on published performance claims.

Heterogeneity in Reporting

For detection and segmentation models in caries detection, heterogeneity in reporting did not allow useful pooling [10]. Standardized reporting is necessary for meaningful comparison across studies [12].

Computational Constraints

Deployment in wearable ultrasound patches demands on-device processing to preserve patient privacy and enable operation beyond clinical facilities [14]. A fully-integrated U-Net processor in 28nm achieved 13.4 frames per second and 23 microjoules per frame, enabling real-time autonomous monitoring in wearable medical devices [14]. When planning clinical deployment, consider the computational constraints of the target hardware.

Clinical Validation Requirements

Deep learning models may help practitioners increase accuracy and reliability, but clinical adoption requires rigorous validation [10]. The minority of studies in the caries detection systematic review showed a low risk of bias in all domains [10]. Before clinical deployment, models should undergo prospective validation in the target clinical setting.

Reproducibility and Reporting Standards

Code and Data Sharing

The lung nodule segmentation systematic review emphasizes the importance of standardized data processing, code and data sharing, and external test datasets [12]. Sharing code and data enables other researchers to reproduce results and build upon existing work.

Documentation Requirements

Document the following for every segmentation project:

1. Dataset version and access date.
2. Preprocessing code and parameters.
3. Architecture definition and implementation details.
4. Training configuration, including random seed.
5. Hardware and software environment.
6. Evaluation code and metric definitions.

FAIR Principles

The FAIR Guiding Principles provide a framework for making data findable, accessible, interoperable, and reusable [4]. Apply these principles to both datasets and code repositories. The National Institutes of Health Genomic Data Sharing Policy provides a model for responsible data sharing [3]. Training resources from the European Bioinformatics Institute [1] and data resources from the National Center for Biotechnology Information [2] support these efforts.

Professional Escalation Criteria

When to Seek Expert Consultation

Escalate to a medical imaging expert or clinical collaborator when:

1. The segmentation task involves anatomical structures with ambiguous boundaries that require clinical expertise to annotate.
2. The model will be used for clinical decision-making and requires regulatory approval.
3. The dataset contains protected health information that requires institutional review board approval.
4. The segmentation results will be used for treatment planning or surgical navigation.

When to Reconsider the Approach

Reconsider the technical approach when:

1. The model fails to achieve acceptable performance on a validation set after reasonable hyperparameter tuning.
2. The dataset is too small for the chosen architecture, and transfer learning or multi-domain strategies are not feasible [22][17].
3. The computational resources required for training exceed available infrastructure.
4. The label quality of the dataset cannot be verified [20].

When to Report Negative Results

Report negative results when:

1. A novel architecture fails to outperform a properly configured nnU-Net baseline [24].
2. Transfer learning does not improve performance on the target domain.
3. Data augmentation does not resolve overfitting.

Negative results contribute to the field by preventing wasted effort on ineffective approaches.

Frequently Asked Questions

What is the best architecture for medical image segmentation with limited data?

For limited data, a standard U-Net or nnU-Net configuration is the most reliable starting point. The U-Net architecture achieves exceptional segmentation accuracy even when trained on highly constrained, low-sample annotated datasets [18]. nnU-Net provides automated configuration that adapts to the dataset characteristics [23]. Transformer-based models require larger training datasets than CNNs and may underperform with limited data [22].

How do I choose between 2D and 3D segmentation models?

Choose 3D models for volumetric data such as CT and MRI when the anatomical structures have meaningful depth information. The 3D FCNN model was found to be the best method for the segmentation of brain tumors [6]. nnU-Net automatically determines the appropriate dimensionality based on the dataset properties [23]. For 2D modalities such as panoramic radiographs and endoscopic images, 2D models are appropriate [16][19].

What public datasets are available for medical image segmentation research?

Common public datasets include the Lung Image Database Consortium and Lung Nodule Analysis 2016 for pulmonary nodules [12], the Medical Decathlon, Automated Cardiac Diagnosis Challenge, and Multi-Modality Whole Heart Segmentation for cardiac imaging [15], the Liver Tumor Segmentation Challenge, Combined Healthy Abdominal Organ Segmentation, and 3D-IRCADb-01 for liver tumors [25][17], and the Brain Tumor Segmentation challenge datasets for brain tumors [21].

How important is data augmentation for segmentation performance?

Data augmentation is critical for segmentation performance, particularly with limited data. In the dental caries segmentation study, models lacking augmentation or preprocessing performed substantially worse, with ResNet50 without augmentation showing severe degradation with a Dice score of 0.0068 [16]. Augmentation including rotation, shifting, shearing, zooming, and horizontal flipping expanded the dataset and improved performance [16].

What metrics should I report for segmentation evaluation?

Report Dice similarity coefficient and Intersection over Union as primary segmentation metrics [16]. Include accuracy, precision, recall, F1-score, and AUC-ROC when relevant to the task [16]. For detection tasks, report sensitivity and specificity [12]. Report measures of variability such as standard deviation across cross-validation folds [16][19].

How do I handle label quality issues in public datasets?

Assess label quality before training by visually inspecting segmentation masks and using automated quality estimation tools. Quality Sentinel can predict label quality for 142 body structures with a strong correlation to ground truth quality [20]. Focus on high-quality pseudo labels to improve training efficiency and performance [20].

When should I use a transformer-based model instead of a CNN?

Use transformer-based models when you need to capture long-range dependencies and global context that CNNs may miss, and when you have sufficient training data. Vision transformers require larger training datasets than convolutional neural networks [22]. Multi-domain transformers with domain adapters can mitigate data hunger by leveraging knowledge from multiple datasets [22].

How do I ensure my segmentation results are reproducible?

Share code and data, document the preprocessing and training pipeline, report computational resources, and use standardized evaluation protocols [12]. Apply the FAIR Guiding Principles to make data findable, accessible, interoperable, and reusable [4]. Use a properly configured nnU-Net baseline for comparison and report validation strategy and metric definitions [24].

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References and Further Reading

• EMBL-EBI Training. European Bioinformatics Institute.
• NCBI Data Resources. National Center for Biotechnology Information.
• Genomic Data Sharing Policy. National Institutes of Health.
• The FAIR Guiding Principles. Scientific Data.
• Recent advances and clinical applications of deep learning in medical image analysis.. Medical image analysis, 2022.
• Review on Deep Learning Methodologies in Medical Image Restoration and Segmentation.. Current medical imaging, 2023.
• Anatomy-aided deep learning for medical image segmentation: a review.. Physics in medicine and biology, 2021.
• Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods.. Tomography (Ann Arbor, Mich.), 2025.
• Deep learning-based tooth segmentation methods in medical imaging: A review.. Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, 2024.
• Deep learning for caries detection: A systematic review.. Journal of dentistry, 2022.
• A Review Paper about Deep Learning for Medical Image Analysis.. Computational and mathematical methods in medicine, 2023.
• Deep learning in pulmonary nodule detection and segmentation: a systematic review.. European radiology, 2025.
• COVLIAS 3.5: integration of attention-based segmentation technique with fuzzy dilated convolutional neural networks for improved classification of chest X-ray scans for multiclass pneumonia diagnosis.. 2026.
• A 23-µJ-per-Frame All-on-Chip TinyML U-Net Processor for Real-Time Autonomous Image Segmentation in Miniaturized Ultrasound Devices.. 2026.
• Deformable multi-head cross attention-based 3D U-net for context-aware multi-modal cardiac image segmentation with uncertainty-aware maps.. 2026.
• Impact of data augmentation and backbone architecture selection on dental caries segmentation in panoramic radiographs: a comparative deep learning study using pre-trained U-Net models.. 2026.
• ADEST-U-Net: A Dual-Encoder Architecture for Cross-Organ and Cross-Modality Transfer Learning in Data-Scarce Medical Image Segmentation. 2026.
• Design of U-Net Architectures for Medical Image Segmentation using AI/ML Model. 2026.
• Intelligent recognition and segmentation of anatomical structures in spinal endoscopy: a deep learning approach with 1000 annotated images.. 2026.
• Quality Sentinel: Estimating Label Quality and Errors in Medical Segmentation Datasets. arXiv.org, 2024.
• Advancing Precision: A Comprehensive Review of MRI Segmentation Datasets from BraTS Challenges (2012-2025). Italian National Conference on Sensors, 2025.
• MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023.
• nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 2020.
• nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2024.
• A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection. Scientific Reports, 2025.
• Comparative Review on Traditional and Deep Learning Methods for Medical Image Segmentation. 2023 IEEE 14th Control and System Graduate Research Colloquium Icsgrc 2023 Conference Proceeding, 2023.
• A review of deep-learning-based medical image segmentation methods. Sustainability Switzerland, 2021.
• A Review of Deep Learning Approaches for Medical Image Segmentation. 2025 IEEE International Conference on Advances in Computing Research on Science Engineering and Technology Acroset 2025, 2025.
• Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches. Bioengineering, 2024.
• A comprehensive review of deep learning for medical image segmentation. Neurocomputing, 2025.

This article is educational and does not replace validated analysis plans, institutional policy, clinical interpretation, or specialist review.