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Reliability of deep learning models for anatomical landmark detection: The role of inter-rater variability

Soorena Salari, Hassan Rivaz, Yiming Xiao

TL;DR

This research conducted a thorough study of different annotation-fusion strategies to preserve inter-rater variability in DL models for anatomical landmark detection, aiming to boost the performance and reliability of the resulting algorithms.

Abstract

Automated detection of anatomical landmarks plays a crucial role in many diagnostic and surgical applications. Progresses in deep learning (DL) methods have resulted in significant performance enhancement in tasks related to anatomical landmark detection. While current research focuses on accurately localizing these landmarks in medical scans, the importance of inter-rater annotation variability in building DL models is often overlooked. Understanding how inter-rater variability impacts the performance and reliability of the resulting DL algorithms, which are crucial for clinical deployment, can inform the improvement of training data construction and boost DL models' outcomes. In this paper, we conducted a thorough study of different annotation-fusion strategies to preserve inter-rater variability in DL models for anatomical landmark detection, aiming to boost the performance and reliability of the resulting algorithms. Additionally, we explored the characteristics and reliability of four metrics, including a novel Weighted Coordinate Variance metric to quantify landmark detection uncertainty/inter-rater variability. Our research highlights the crucial connection between inter-rater variability, DL-models performances, and uncertainty, revealing how different approaches for multi-rater landmark annotation fusion can influence these factors.

Reliability of deep learning models for anatomical landmark detection: The role of inter-rater variability

TL;DR

This research conducted a thorough study of different annotation-fusion strategies to preserve inter-rater variability in DL models for anatomical landmark detection, aiming to boost the performance and reliability of the resulting algorithms.

Abstract

Automated detection of anatomical landmarks plays a crucial role in many diagnostic and surgical applications. Progresses in deep learning (DL) methods have resulted in significant performance enhancement in tasks related to anatomical landmark detection. While current research focuses on accurately localizing these landmarks in medical scans, the importance of inter-rater annotation variability in building DL models is often overlooked. Understanding how inter-rater variability impacts the performance and reliability of the resulting DL algorithms, which are crucial for clinical deployment, can inform the improvement of training data construction and boost DL models' outcomes. In this paper, we conducted a thorough study of different annotation-fusion strategies to preserve inter-rater variability in DL models for anatomical landmark detection, aiming to boost the performance and reliability of the resulting algorithms. Additionally, we explored the characteristics and reliability of four metrics, including a novel Weighted Coordinate Variance metric to quantify landmark detection uncertainty/inter-rater variability. Our research highlights the crucial connection between inter-rater variability, DL-models performances, and uncertainty, revealing how different approaches for multi-rater landmark annotation fusion can influence these factors.

Paper Structure

This paper contains 14 sections, 5 equations, 1 figure, 3 tables.

Table of Contents

  1. INTRODUCTION
  2. Methods and Materials
  3. Dataset and Preprocessing
  4. Inter-rater Variability
  5. Integrating Multi-Rater Annotations and Network Architecture
  6. Uncertainty Quantification
  7. Implementation and Network Training
  8. Experimental Setup and Results
  9. Landmark Detection Accuracy
  10. Uncertainty and Inter-Rater Variability Quantification
  11. Reliability of Different Uncertainty Metrics
  12. Discussion
  13. Conclusions
  14. Acknowledgment

Figures (1)

  • Figure 1: (a) Definition for five target anatomical landmarks. (b) Landmark annotations from 11 raters on an exemplary scan (c) An example for Gaussian-based inter-rater variability modeling (adaptable for uncertainty modeling).