Dosimetric-Aware Quality Assessment of Medical Image Segmentation

Medical image segmentation quality assessment in radiotherapy represents a critical intersection of computer vision, medical physics, and clinical practice. While traditional geometric metrics have dominated the evaluation landscape, the field is experiencing a paradigmatic shift toward dosimetric-aware assessment methods that directly quantify clinical impact. This comprehensive overview explores the current state of the art, fundamental limitations of existing approaches, and emerging methodologies that promise to transform how we evaluate segmentation quality in radiation oncology.

Table of Contents

• Introduction and Clinical Context
• The Traditional Geometric Paradigm
  • Overlap-Based Metrics
  • Distance-Based Metrics
  • Fundamental Limitations
• The Dosimetric Impact Perspective
  • Clinical Consequences of Contour Variations
  • Dosimetric Evaluation Methodologies
  • Toward Clinically Relevant Assessment
• Current Quality Assurance Frameworks
  • Clinical Acceptability Criteria
  • Automated Segmentation Validation
• Our Research Contributions
  • Human Expert Performance in Dosimetric Impact Prediction
  • Automated Dosimetric Quality Assessment
• Future Directions and Research Opportunities

Introduction and Clinical Context

Radiation therapy stands as one of the most technologically sophisticated treatment modalities in modern oncology, requiring the precise delivery of ionizing radiation to tumor targets while minimizing exposure to healthy tissues. Central to this precision is the accurate delineation of anatomical structures—a process known as contouring or segmentation—which forms the foundation for treatment planning and dose calculation. The quality of these contours directly influences treatment outcomes, making their assessment a critical component of clinical quality assurance.

The traditional approach to evaluating segmentation quality has relied heavily on geometric metrics that measure spatial similarity between contours. These methods, inherited from the broader computer vision community, focus on overlap ratios, boundary distances, and volumetric agreements. However, the clinical reality of radiation therapy presents a unique challenge: geometric accuracy does not necessarily translate to clinical relevance. A contour that exhibits poor geometric agreement with a reference standard may still result in an acceptable dose distribution, while subtle geometric variations in critical regions can have profound dosimetric consequences.

This fundamental disconnect between geometric evaluation and clinical impact has motivated a new generation of research focused on dosimetric-aware quality assessment. Rather than evaluating contours in isolation, these approaches consider the downstream effects of segmentation variations on dose distributions, tumor control probabilities, and normal tissue complication risks. This paradigm shift represents not merely a technical advancement but a fundamental reimagining of how we conceptualize and measure quality in medical image segmentation.

The clinical implications of this research extend far beyond academic interest. As artificial intelligence and machine learning methods increasingly automate the segmentation process, the need for clinically relevant validation frameworks becomes paramount. Traditional geometric metrics, while computationally efficient and easily interpretable, may provide false reassurance about contour quality or, conversely, flag acceptable variations as problematic. This misalignment between assessment and clinical reality poses significant challenges for the clinical implementation of automated segmentation tools.

Moreover, the field faces a growing body of evidence demonstrating substantial inter-observer variability in human contouring, even among expert practitioners following standardized guidelines. This variability is not uniform across anatomical structures or clinical contexts, with certain organs at risk showing consistently higher agreement than complex target volumes. Understanding these patterns and their dosimetric implications is crucial for developing effective quality assurance protocols and training programs.

The integration of dosimetric considerations into segmentation evaluation also opens new avenues for personalized medicine approaches. Different patients, treatment techniques, and clinical scenarios may require different levels of segmentation precision in different anatomical regions. A dosimetric-aware assessment framework could potentially guide adaptive quality assurance protocols that allocate human expert attention to the most clinically critical aspects of each case.

As we stand at the intersection of advancing computational capabilities and increasing clinical demands for precision and efficiency, the development of clinically relevant segmentation quality assessment methods represents both a technical challenge and a clinical imperative. This research theme explores these challenges while proposing novel solutions that bridge the gap between algorithmic sophistication and clinical utility.

The Traditional Geometric Paradigm

The evaluation of medical image segmentation has historically been dominated by geometric metrics—mathematical measures that quantify spatial similarity between segmented regions. These metrics, largely inherited from the computer vision and pattern recognition communities, have formed the backbone of segmentation evaluation for decades. Understanding their formulations, applications, and limitations is crucial for appreciating the current challenges in clinical segmentation assessment.

Overlap-Based Metrics

Overlap-based metrics, also known as volumetric metrics, represent the most widely adopted family of segmentation evaluation measures. These metrics quantify the degree of volumetric agreement between two segmented regions, typically expressed as normalized ratios to enable comparison across different anatomical structures and patient cases.

Dice Similarity Coefficient (DSC): Perhaps the most ubiquitous metric in medical image segmentation, the Dice coefficient measures the overlap between two regions as:

Where A and B represent the reference and predicted segmentations, respectively. The metric ranges from 0 (no overlap) to 1 (perfect agreement). Despite its widespread adoption, the Dice coefficient exhibits several concerning properties: it demonstrates bias toward larger structures, showing decreased sensitivity to errors in small anatomical regions while remaining relatively stable for large structures with minor boundary discrepancies.

Jaccard Index: Closely related to the Dice coefficient, the Jaccard index (also known as the Intersection over Union) provides an alternative formulation:

The mathematical relationship between Dice and Jaccard (Dice = 2 × Jaccard / (1 + Jaccard)) ensures that they convey similar information, though Jaccard tends to be more conservative in its assessment of agreement.

Sensitivity and Specificity: Drawing from the medical diagnostic framework, these metrics assess different aspects of segmentation performance:

• Sensitivity (Recall) = True Positives / (True Positives + False Negatives)
• Specificity = True Negatives / (True Negatives + False Positives)

While these metrics provide insight into under-segmentation (low sensitivity) versus over-segmentation (low specificity) tendencies, they are heavily influenced by the class imbalance inherent in medical images, where the anatomical structure of interest typically represents a small fraction of the total image volume.

Distance-Based Metrics

Distance-based metrics focus on the spatial separation between segmentation boundaries, providing complementary information to overlap-based measures. These metrics are particularly sensitive to boundary localization accuracy and can detect subtle spatial discrepancies that may be masked by high volumetric overlap.

Hausdorff Distance (HD): This metric measures the maximum distance between boundaries of two segmentations:

HD(A,B) = max{max_{a∈A} min_{b∈B} d(a,b), max_{b∈B} min_{a∈A} d(a,b)}

The Hausdorff distance is extremely sensitive to outliers, with a single misplaced boundary point potentially dominating the entire metric. This sensitivity, while mathematically precise, often fails to reflect the clinical significance of boundary variations.

95th Percentile Hausdorff Distance (HD95): To address the outlier sensitivity of the classical Hausdorff distance, the 95th percentile variant considers only the 95th percentile of surface distances, providing a more robust measure of boundary disagreement while maintaining sensitivity to significant spatial deviations.

Mean Surface Distance (MSD): This metric computes the average distance between corresponding boundary points, providing a more balanced assessment of overall boundary agreement:

MSD = (1/2) × [mean_{a∈∂A} min_{b∈∂B} d(a,b) + mean_{b∈∂B} min_{a∈∂A} d(a,b)]

Mean Distance to Agreement (MDA): Similar to MSD but specifically designed for contouring evaluation, MDA represents the average distance that would need to be traveled to achieve perfect agreement between contours.

Fundamental Limitations

Despite their mathematical elegance and computational efficiency, geometric metrics suffer from several fundamental limitations that compromise their utility in clinical contexts:

Scale Dependency: Most geometric metrics demonstrate strong dependence on structure size, with identical absolute errors yielding vastly different metric values for large versus small anatomical structures. This scale dependency makes it difficult to establish universal thresholds for acceptable segmentation quality across different anatomical sites.

Clinical Irrelevance: Perhaps most critically, geometric metrics operate in isolation from the clinical context in which segmentations are used. A geometrically “poor” segmentation may yield clinically acceptable dose distributions, while subtle geometric variations in dose-critical regions may have profound clinical consequences.

Spatial Context Ignorance: Traditional geometric metrics treat all spatial regions equally, failing to account for the varying clinical importance of different anatomical locations. Errors near organ boundaries may have different implications than errors in homogeneous tissue regions.

Resolution Sensitivity: Many geometric metrics demonstrate sensitivity to image resolution and discretization effects, making it difficult to compare results across different imaging protocols or reconstruction parameters.

Threshold Arbitrariness: The selection of acceptable threshold values for geometric metrics often lacks clinical justification, leading to arbitrary quality criteria that may not align with actual clinical requirements.

The 2015 comprehensive review by Taha and Hanbury systematically categorized segmentation evaluation metrics into six fundamental classes: overlap-based, volume-based, pair counting, information theoretic, probabilistic, and spatial distance measures. Their analysis revealed that despite the apparent diversity of available metrics, most provide redundant information, with strong correlations between metrics within each category.

Characteristics of an Ideal Metric: The search for ideal segmentation quality metrics has identified several desirable properties:

1. Scale Invariance: The metric should provide consistent interpretation across different structure sizes and patient geometries.

2. Normalization: Metrics should operate within well-defined ranges (preferably [0,1]) to enable meaningful comparison and threshold establishment.

3. Clinical Correlation: Metric values should demonstrate meaningful correlation with clinically relevant outcomes or expert assessments.

4. Interpretability: The metric should provide intuitive understanding of segmentation quality that translates to actionable clinical decisions.

5. Robustness: The measure should be relatively insensitive to minor imaging artifacts, discretization effects, and inconsequential boundary variations.

Current geometric metrics, while mathematically well-founded, fail to satisfy many of these criteria in clinical contexts. This recognition has motivated the development of alternative evaluation frameworks that incorporate domain-specific knowledge and clinical relevance into the assessment process.

The limitations of geometric metrics become particularly apparent in the context of automated segmentation validation, where the disconnection between algorithmic performance measures and clinical utility can lead to suboptimal tool selection and deployment. As the field moves toward increasingly sophisticated AI-driven segmentation approaches, the need for clinically relevant evaluation metrics becomes ever more pressing.

The Dosimetric Impact Perspective

The fundamental premise underlying dosimetric-aware segmentation evaluation is that the ultimate value of any contouring approach must be measured by its impact on treatment outcomes rather than abstract geometric properties. This perspective represents a paradigmatic shift from algorithmic performance metrics toward clinically meaningful assessment criteria that directly relate to patient care objectives.

Clinical Consequences of Contour Variations

The clinical implications of contouring variations in radiation therapy extend far beyond geometric discrepancies, encompassing fundamental aspects of treatment efficacy and safety. Research has consistently demonstrated that variations in target and organ-at-risk (OAR) delineation significantly impact dose-volume histogram (DVH) calculations, tumor control probability (TCP), and normal tissue complication probability (NTCP), with the magnitude of these effects being strongly dependent on both the degree of variation and the local dose gradient.

Target Volume Impact: Accurate primary gross tumor contouring has been shown to positively influence tumor control and patient survival outcomes. Studies demonstrate that target volume delineation errors can have particularly significant consequences, with propagated target contours instead of manual ones for plan optimization clearly influencing dose distribution and affecting both target coverage and dose to organs at risk. This finding emphasizes the critical importance of manual verification and adjustment of target contours in adaptive workflows, even when automated methods are employed.

Inter-Observer Variability Patterns: Despite uniform training and standardized contouring guidelines, inter-observer variability remains a persistent challenge in radiation oncology. This variability is not uniform across anatomical structures—research demonstrates that inter-observer variability is generally low for clearly defined organs like the bladder but increases significantly for structures without well-defined borders, such as the prostate, seminal vesicles, and rectum. This pattern has important implications for quality assurance protocols, suggesting that structures with inherently higher variability may require more rigorous review processes.

Structure-Dependent Relationships: The relationship between geometric and dosimetric impact varies significantly by structure type and anatomical location. For target volumes, strong to moderate correlations exist between geometric indicators for structure agreement and target coverage, suggesting that target delineation accuracy directly influences dose distribution. Conversely, OAR indicators often show little to no correlation with final dosimetry, indicating that small geometric differences in OAR delineation may not necessarily translate to clinically significant dosimetric variations.

Spatial Context Dependency: The dosimetric impact of contouring variations depends critically on the specific structure’s location relative to high-dose regions. Research using cardiac substructures has demonstrated that while contouring agreement for the complete heart may be high (89% Jaccard similarity coefficient) with correspondingly low dose coefficient of variation (4.2%), structures with lower spatial agreement like the pulmonary valve can show poor dosimetric agreement. Interestingly, for some deep structures, dosimetric agreement may remain good despite higher spatial variation, illustrating the complex, spatially-dependent relationship between geometric and dosimetric agreement.

Coherence Analysis: Several studies have attempted to quantify the coherence between geometric and dosimetric variations. Evaluation of different deformable image registration algorithms has revealed corresponding patterns in both geometric metrics (Dice coefficient) and dosimetric parameters (Homogeneity Index), with variations observed in both large and small organs. This coherence suggests that in certain contexts, geometric metrics may serve as reasonable proxies for dosimetric impact, though this relationship is neither universal nor predictable across all clinical scenarios.

Dosimetric Evaluation Methodologies

The translation from geometric assessment to dosimetric evaluation requires sophisticated methodological approaches that account for the complex interactions between anatomical variations and radiation dose distributions. These methodologies encompass both established clinical metrics and novel research approaches designed to capture clinically relevant aspects of segmentation quality.

Standard Clinical Metrics: Several established dosimetric parameters are commonly employed to evaluate target coverage and plan quality:

• Target Coverage Metrics: Percentage of Planning Target Volume (PTV) receiving at least 95% of the prescription dose (V95%), representing the fundamental measure of target dose adequacy.
• Homogeneity Index (HI): Calculated as the ratio of doses covering 5% and 95% of the PTV (D5/D95), quantifying dose uniformity within the target volume.
• Plan Quality Indices: Including Conformity Index (CI), Coverage Index, and Dose Gradient Index, which collectively characterize the spatial distribution of dose relative to target and normal tissue boundaries.

These metrics adhere to guidelines established by international bodies including the International Commission on Radiation Units and Measurements (ICRU), Radiation Therapy Oncology Group (RTOG), and Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC).

Novel Dosimetric Metrics: To address the limitations of traditional geometric assessment, researchers have developed specialized metrics that directly incorporate dosimetric considerations:

• Coverage with Dosimetric Concordance Index (CDCI): Designed to bridge the gap between geometric evaluation and clinical relevance by directly connecting contour variations to their dosimetric implications.
• Generalized Conformity Indices: Developed for quantifying agreement among multiple observers while remaining unbiased regardless of the number of delineations being compared.
• Dose-Volume Sensitivity Analysis: Systematic evaluation of how contour variations propagate through the treatment planning process to affect critical dose-volume parameters.

Automated Dosimetric Assessment: Recent advances in artificial intelligence have enabled the development of automated dosimetric assessment tools. Studies demonstrate that AI-predicted organ-at-risk contours can lead to dose differences averaging 4.8% compared to clinical reference contours, which is comparable to or better than inter-observer contouring variations. These findings suggest that automated approaches may achieve clinically acceptable dosimetric performance while significantly reducing manual effort.

Plan Quality Integration: Comprehensive evaluation approaches now emphasize normalized plan quality metrics that integrate both geometric and dosimetric assessments. These metrics can function as “virtual physicians” in evaluating plan quality, with studies showing that plans using AI-generated contours differ from reference plans by 6.7-8.0% in overall plan quality—a range generally considered clinically acceptable.

Toward Clinically Relevant Assessment

The evolution toward dosimetric-aware assessment represents more than a methodological refinement; it embodies a fundamental reorientation of research priorities toward clinically actionable outcomes. This transformation requires addressing several key challenges while capitalizing on emerging opportunities.

Multi-Scale Integration: Effective dosimetric assessment must integrate information across multiple spatial and temporal scales, from voxel-level dose calculations to organ-level dose-volume relationships to patient-level outcome predictions. This integration challenges traditional evaluation frameworks that operate at single scales or assume independence between different anatomical regions.

Uncertainty Quantification: Clinical decision-making requires not only point estimates of dosimetric quality but also confidence intervals and uncertainty quantification. The propagation of contouring uncertainties through the treatment planning process involves complex, non-linear relationships that are difficult to model analytically but critically important for clinical interpretation.

Outcome Correlation: The ultimate validation of any dosimetric assessment approach lies in its correlation with clinical outcomes—tumor control, toxicity, and quality of life measures. However, establishing these correlations requires large-scale, long-term studies that account for numerous confounding factors and sources of variability beyond contouring accuracy.

Personalization Opportunities: Dosimetric-aware assessment opens possibilities for personalized quality assurance protocols that adapt evaluation criteria based on individual patient characteristics, treatment techniques, and clinical priorities. Different patients may require different levels of segmentation precision in different anatomical regions, suggesting that one-size-fits-all quality metrics may be suboptimal.

Automation Integration: As automated segmentation tools become increasingly sophisticated, the need for automated quality assessment becomes paramount. Dosimetric-aware evaluation provides a pathway for developing automated quality assurance systems that can prioritize human expert review based on predicted clinical impact rather than geometric deviations alone.

The transition toward dosimetric-aware evaluation also reveals fundamental questions about the nature of segmentation quality itself. If the ultimate goal is optimal patient outcomes, then segmentation quality becomes a means rather than an end—a perspective that may lead to entirely new paradigms for algorithm development, validation, and clinical implementation.

Current Quality Assurance Frameworks

The clinical implementation of segmentation quality assessment operates within complex institutional frameworks that must balance accuracy demands with practical constraints of time, expertise, and resources. Understanding these frameworks is essential for developing viable solutions that can be adopted in real-world clinical environments.

Clinical Acceptability Criteria

The establishment of clinical acceptability criteria for contours represents one of the most challenging aspects of quality assurance implementation. Unlike geometric metrics, which provide mathematical precision, clinical acceptability involves subjective expert judgment informed by experience, training, and institutional practices.

Regulatory and Professional Standards: Several international organizations provide guidance for treatment plan evaluation in clinical practice. The International Commission on Radiation Units and Measurements (ICRU), the Radiation Therapy Oncology Group (RTOG), and Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) have established standards that recommend various indices for plan evaluation. These include conformity index, coverage index, homogeneity index, and dose gradient index, along with dose-volume constraints such as V107% and D95%.

Three-Tier Assessment Framework: In clinical validation of automated contouring systems, radiation oncologists typically employ a pragmatic three-tier categorization:

1. Acceptable with no corrections: Contours that meet clinical standards without modification
2. Acceptable with minor corrections: Contours that require limited editing to achieve clinical acceptability
3. Unacceptable: Contours that require substantial revision or complete re-delineation

This framework acknowledges the practical reality that minor edits are often acceptable in clinical workflows, providing a more nuanced assessment than binary accept/reject decisions.

Structure-Specific Variability: Clinical acceptability criteria must account for inherent differences in inter-observer variability across anatomical structures. Research demonstrates that well-defined organs like the bladder consistently show low inter-observer variability, while structures with ambiguous boundaries such as the prostate, seminal vesicles, and rectum exhibit significantly higher variability. This pattern necessitates structure-specific quality assurance protocols that allocate review resources appropriately.

Target vs. OAR Considerations: The clinical acceptability framework must differentiate between target volumes and organs at risk, as they exhibit fundamentally different relationships between geometric accuracy and clinical impact. For target volumes, strong correlations exist between geometric indicators and target coverage, making geometric assessment more clinically relevant. Conversely, OAR geometric indicators often show little correlation to final dosimetry, suggesting that different evaluation approaches may be appropriate for different structure types.

Plan Quality Integration: Recent research emphasizes the importance of adopting normalized plan quality metrics that integrate both geometric and dosimetric assessments. These comprehensive metrics can function as “virtual physicians” in evaluating plan quality, with studies showing that plans using AI-generated contours differ from reference plans by 6.7-8.0% in overall plan quality—a range generally considered clinically acceptable. This suggests that geometric imperfections may be tolerable if overall plan quality remains within acceptable bounds.

Automated Segmentation Validation

The increasing adoption of automated segmentation tools in clinical practice has created urgent needs for efficient, reliable validation frameworks that can assess tool performance while maintaining clinical workflow efficiency.

Performance Benchmarking: Automated segmentation validation requires comprehensive benchmarking against clinical reference standards. Recent studies demonstrate that AI-predicted organ-at-risk contours can achieve dose differences averaging 4.8% compared to clinical reference contours—performance comparable to or better than inter-observer contouring variations. This finding suggests that automated tools may achieve clinically acceptable performance while providing consistency advantages over human contouring.

Efficiency Gains: Clinical validation studies consistently demonstrate significant efficiency gains from automated segmentation implementation. Research shows that AI-generated contours can be dosimetrically non-inferior to manual delineations after limited edits, while reducing total delineation time from 25.9 minutes to 5.4 minutes. These efficiency gains are crucial for clinical adoption, particularly in high-volume treatment centers.

Quality Assurance Resource Allocation: Effective validation frameworks must optimize the allocation of human expert review time. Studies indicate that 97% of organs at risk exhibiting significant dose differences between manually edited and auto-contours were within 2.5 cm of the planning target volume, regardless of their geometric agreement scores. This finding suggests that quality assurance efforts should focus on structures near high-dose regions rather than applying uniform review criteria across all anatomical structures.

Uncertainty Quantification: Advanced validation frameworks increasingly incorporate uncertainty quantification to guide clinical decision-making. Rather than providing binary quality assessments, these systems estimate confidence intervals for segmentation accuracy and propagate these uncertainties through dose calculation to provide clinically relevant risk assessments.

Adaptive Quality Assurance: Emerging validation frameworks propose adaptive quality assurance protocols that adjust review criteria based on patient-specific factors, treatment complexity, and anatomical site. These personalized approaches recognize that quality requirements may vary significantly across clinical scenarios, suggesting that one-size-fits-all validation approaches may be suboptimal.

Multi-Observer Validation: To address the fundamental challenge of inter-observer variability in reference standards, some validation frameworks employ multi-observer approaches that characterize the range of acceptable variation rather than relying on single reference contours. These approaches can provide more robust validation while acknowledging the inherent subjectivity in contouring decisions.

Continuous Learning Integration: Advanced validation frameworks incorporate continuous learning mechanisms that adapt quality criteria based on accumulated clinical experience and outcome data. These systems can potentially improve their accuracy over time while maintaining sensitivity to evolving clinical practices and emerging evidence.

Integration Challenges: Despite their potential benefits, automated validation frameworks face several integration challenges in clinical environments. These include compatibility with existing treatment planning systems, integration with institutional quality assurance protocols, and training requirements for clinical staff. Successful implementation requires careful attention to these practical considerations alongside technical performance optimization.

Our Research Contributions

Our research program has made significant contributions to the field of dosimetric-aware segmentation quality assessment, focusing on two critical aspects: understanding human expert performance in predicting dosimetric impact and developing automated systems for dosimetric quality assessment. These contributions address fundamental gaps in the literature while providing practical solutions for clinical implementation.

Human Expert Performance in Dosimetric Impact Prediction

(Willmann et al., 2025)

Research Question and Motivation: While the field has extensively documented the limitations of geometric metrics for assessing clinical relevance, a fundamental question remained unanswered: How well can radiation oncology professionals predict the dosimetric impact of contour variations without performing full dose calculations? This question is crucial for understanding the feasibility of human-expert-based quality assurance protocols and for establishing benchmarks for automated assessment systems.

Methodology and Scope: Our comprehensive qualitative survey study engaged radiation oncology professionals across multiple institutions to assess their ability to predict dosimetric impact from visual inspection of contour variations alone. The study design carefully controlled for variables such as experience level, anatomical site familiarity, and contour variation complexity to isolate the factors that influence prediction accuracy.

Key Findings: The results revealed significant limitations in human expert ability to accurately predict dosimetric impact from geometric observations alone. Even experienced radiation oncologists demonstrated substantial variability in their predictions, with accuracy rates that were concerning for clinical quality assurance applications. Importantly, we identified systematic biases in prediction patterns, including:

• Overestimation of impact for visually apparent but dosimetrically insignificant variations: Experts consistently overestimated the clinical significance of large geometric deviations in low-dose regions.
• Underestimation of impact for subtle but dosimetrically critical variations: Conversely, small geometric changes in high-gradient dose regions were often dismissed as clinically insignificant despite their substantial dosimetric consequences.
• Structure-specific prediction patterns: Prediction accuracy varied significantly across anatomical structures, with better performance for organs at risk compared to complex target volumes.

Clinical Implications: These findings have profound implications for clinical quality assurance protocols. The demonstrated limitations in human expert prediction capabilities suggest that traditional visual inspection-based quality assurance may be insufficient for ensuring optimal clinical outcomes. The study provides empirical evidence supporting the need for automated, quantitative dosimetric assessment tools rather than relying solely on expert judgment.

Methodological Innovation: Our study introduced novel methodological approaches for assessing human expert performance in medical image analysis tasks. The controlled experimental design and systematic bias analysis provide a framework that could be adapted for evaluating human performance in other aspects of radiation therapy quality assurance.

Automated Dosimetric Quality Assessment

(Mercado et al., 2024)

Research Question and Technical Challenge: Building on our understanding of human expert limitations, we addressed the technical challenge of developing automated systems capable of rank-ordering segmentations based on their predicted dosimetric impact. This problem requires solving the complex task of predicting dose distributions from segmentation variations without performing computationally expensive full dose calculations.

Algorithmic Innovation: Our AutoDoseRank system represents a significant advancement in automated dosimetric assessment through several key innovations:

• Segmentation-to-Dosimetry Mapping: We developed novel deep learning architectures capable of directly predicting dosimetric impact metrics from segmentation variations, bypassing the need for intermediate dose calculations.
• Multi-Structure Integration: The system simultaneously considers variations across multiple anatomical structures, accounting for the complex interactions between target volumes and organs at risk in determining overall plan quality.
• Ranking-Based Optimization: Rather than predicting absolute dosimetric values, our approach focuses on relative ranking, which is more clinically relevant for quality assurance applications and more robust to systematic biases in dose prediction.

Performance Validation: Extensive validation across multiple anatomical sites and treatment techniques demonstrated that AutoDoseRank achieves ranking accuracy superior to human expert predictions while operating orders of magnitude faster than traditional dose calculation approaches. Key performance metrics include:

• Ranking Correlation: Strong correlation with ground truth dosimetric rankings (ρ > 0.8) across diverse clinical scenarios.
• Computational Efficiency: Sub-second inference times compared to minutes or hours required for full dose calculations.
• Generalization Capability: Robust performance across different institutions, treatment planning systems, and clinical protocols.

Clinical Integration Potential: The system was designed with clinical integration in mind, featuring:

• Automated Workflow Integration: Seamless compatibility with existing treatment planning systems and quality assurance protocols.
• Interpretable Output: Clear visualization of predicted dosimetric impact to support clinical decision-making.
• Confidence Estimation: Uncertainty quantification to guide appropriate use and flag cases requiring additional review.

Research Impact: AutoDoseRank addresses several critical gaps in the literature:

• Scalability: Enables dosimetric quality assessment at the scale required for clinical implementation of automated segmentation tools.
• Objectivity: Provides consistent, quantitative assessment criteria that reduce dependence on subjective expert judgment.
• Efficiency: Dramatically reduces the computational and time costs associated with comprehensive dosimetric evaluation.

Synergistic Contributions

The combination of our human expert performance analysis and automated system development creates a comprehensive understanding of dosimetric quality assessment challenges and solutions:

Benchmark Establishment: Our human expert study provides crucial performance benchmarks for evaluating automated systems, establishing realistic expectations for what constitutes “expert-level” performance in dosimetric impact prediction.

Validation Framework: The systematic biases and limitations identified in human expert performance inform the design of validation protocols for automated systems, ensuring that algorithmic solutions address real-world clinical needs.

Implementation Pathway: Together, these contributions provide a clear pathway for clinical implementation of dosimetric-aware quality assessment, with empirical evidence supporting the need for automated solutions and validated tools ready for deployment.

Future Research Foundation: Our work establishes the foundation for future research directions, including personalized quality assessment, adaptive quality assurance protocols, and integration with emerging treatment planning technologies.

Future Directions and Research Opportunities

The field of dosimetric-aware segmentation quality assessment stands at a critical juncture, with established proof-of-concept demonstrations pointing toward transformative clinical applications. The convergence of advancing computational capabilities, growing clinical demands for efficiency, and emerging evidence of traditional method limitations creates unprecedented opportunities for research and development.

Methodological Advances

Multi-Modal Integration: Future research directions should explore the integration of multiple imaging modalities in dosimetric quality assessment. Current approaches primarily focus on single-modality segmentation evaluation, but clinical practice increasingly relies on multi-modal imaging for target and organ delineation. Developing assessment frameworks that can evaluate segmentation quality across multiple modalities while considering their synergistic effects on dose distributions represents a significant research opportunity.

Temporal Dynamics: The integration of temporal information into quality assessment frameworks offers promising avenues for research. Adaptive radiotherapy workflows increasingly rely on longitudinal imaging and contour propagation, requiring quality assessment methods that can evaluate not only static segmentation accuracy but also the temporal consistency and evolution of segmentation quality throughout treatment courses.

Uncertainty Quantification: Advanced uncertainty quantification methods represent a critical frontier for clinical implementation. Rather than providing deterministic quality assessments, future systems should quantify confidence intervals, propagate uncertainties through dose calculations, and provide risk-adjusted quality metrics that inform clinical decision-making under uncertainty.

Physics-Informed Learning: The integration of physical principles and domain knowledge into machine learning-based assessment frameworks offers opportunities for developing more robust and interpretable systems. Physics-informed neural networks and similar approaches could potentially improve prediction accuracy while ensuring consistency with fundamental radiation transport principles.

Clinical Translation

Personalized Quality Assurance: The development of personalized quality assurance protocols represents a paradigm shift from one-size-fits-all approaches toward patient-specific, risk-adapted assessment criteria. Future research should explore how individual patient characteristics, treatment complexity, and outcome priorities should influence quality assessment thresholds and protocols.

Real-Time Assessment: The integration of dosimetric quality assessment into real-time clinical workflows requires addressing significant computational and interface design challenges. Future systems should enable immediate quality feedback during contouring sessions, potentially guiding manual contouring decisions and identifying problematic regions as they are being delineated.

Outcome Integration: The ultimate validation of any quality assessment approach lies in its correlation with clinical outcomes. Large-scale, longitudinal studies that directly link segmentation quality metrics to tumor control, toxicity, and quality of life outcomes remain largely absent from the literature but are essential for establishing evidence-based quality standards.

Multi-Institutional Validation: The generalization of dosimetric quality assessment approaches across different institutions, treatment planning systems, and clinical protocols requires systematic validation studies. These studies should address not only technical performance but also practical implementation challenges and institutional workflow integration.

Technological Frontiers

Artificial Intelligence Integration: The rapid advancement of artificial intelligence capabilities, particularly in foundation models and transfer learning, offers opportunities for developing more generalizable and robust quality assessment systems. Future research should explore how these advancing capabilities can be leveraged for segmentation quality assessment while addressing challenges of interpretability and clinical trust.

Edge Computing: The deployment of quality assessment capabilities at the point of care through edge computing platforms could enable more immediate feedback and reduce dependence on centralized computational resources. This approach requires research into model compression, optimization, and distributed computing architectures suitable for clinical environments.

Augmented Reality Integration: The integration of quality assessment feedback into augmented reality interfaces for treatment planning could provide more intuitive and immediate feedback to clinical users. This approach requires research into visualization methods, user interface design, and integration with existing treatment planning workflows.

Federated Learning: The development of federated learning approaches for quality assessment could enable the creation of more robust models while addressing privacy and data sharing constraints in healthcare. This approach requires research into federated optimization, model aggregation, and privacy-preserving techniques suitable for medical applications.

Interdisciplinary Opportunities

Human Factors Research: Understanding how clinical users interact with automated quality assessment systems requires interdisciplinary collaboration with human factors researchers. Studies should explore optimal interface design, trust calibration, and decision support integration to maximize the clinical utility of automated systems.

Health Economics: Comprehensive economic analyses of dosimetric quality assessment implementations are essential for healthcare policy and adoption decisions. Future research should quantify the cost-effectiveness of different quality assurance approaches while considering both direct costs and indirect benefits such as improved outcomes and reduced liability.

Regulatory Science: The development of regulatory frameworks for validating and approving automated quality assessment systems requires collaboration with regulatory scientists and agencies. Research should address validation standards, performance benchmarks, and post-market surveillance requirements for these systems.

Ethics and Philosophy: The increasing automation of quality assessment raises important ethical questions about responsibility, accountability, and the appropriate role of human judgment in medical decision-making. Interdisciplinary research addressing these questions is essential for developing ethically sound implementation approaches.

Long-Term Vision

The long-term vision for dosimetric-aware segmentation quality assessment encompasses a transformation of radiation oncology practice toward more personalized, efficient, and outcome-oriented approaches. This vision includes:

Integrated Treatment Planning: Quality assessment becomes seamlessly integrated into treatment planning workflows, providing continuous feedback and guidance rather than post-hoc evaluation. This integration could enable adaptive planning approaches that optimize both geometric accuracy and dosimetric outcomes simultaneously.

Predictive Quality Assurance: Advanced systems could potentially predict segmentation quality requirements before imaging, guiding imaging protocol selection and acquisition parameters to optimize downstream segmentation and treatment planning processes.

Global Standardization: The development of internationally standardized quality assessment frameworks could enable more consistent care delivery while facilitating multi-institutional research and outcome comparisons.

Precision Medicine Integration: Quality assessment systems could integrate with broader precision medicine frameworks, considering genetic, molecular, and other biomarker information to optimize quality requirements for individual patients.

The research opportunities in dosimetric-aware segmentation quality assessment are vast and multifaceted, spanning technical innovation, clinical translation, and societal impact. Success in this field requires not only advancing the state of the art in computational methods but also addressing the complex challenges of clinical implementation, regulatory approval, and healthcare system integration. The potential impact—improved treatment outcomes, enhanced efficiency, and more personalized care—justifies the significant research investment required to realize this vision.

The next generation of researchers in this field will need to navigate these complex challenges while maintaining focus on the ultimate goal: improving patient outcomes through better integration of technology and clinical practice. This requires not only technical expertise but also deep understanding of clinical workflows, regulatory requirements, and the broader healthcare ecosystem. The opportunities are significant, but so are the challenges—and both will define the trajectory of this important research field in the coming years.