Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy

Abstract

Objective: Surgical data science is evolving into a research field that aims to observe everything occurring within and around the treatment process to provide situation-aware data-driven assistance. In the context of endoscopic video analysis, the accurate classification of organs in the field of view of the camera proffers a technical challenge. Herein, we propose a new approach to anatomical structure classification and image tagging that features an intrinsic measure of confidence to estimate its own performance with high reliability and which can be applied to both RGB and multispectral imaging (MI) data. Methods: Organ recognition is performed using a superpixel classification strategy based on textural and reflectance information. Classification confidence is estimated by analyzing the dispersion of class probabilities. Assessment of the proposed technology is performed through a comprehensive in vivo study with seven pigs. Results: When applied to image tagging, mean accuracy in our experiments increased from 65% (RGB) and 80% (MI) to 90% (RGB) and 96% (MI) with the confidence measure. Conclusion: Results showed that the confidence measure had a significant influence on the classification accuracy, and MI data are better suited for anatomical structure labeling than RGB data. Significance: This work significantly enhances the state of art in automatic labeling of endoscopic videos by introducing the use of the confidence metric, and by being the first study to use MI data for in vivo laparoscopic tissue classification. The data of our experiments will be released as the first in vivo MI dataset upon publication of this paper.

7 pages, 6 images, 2 tables

References in corpus (6)

• Scikit-learn: Machine Learning in Python
• What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
• Loopy Belief Propagation for Approximate Inference: An Empirical Study
• Weight Uncertainty in Neural Networks
• Surgical Data Science: Enabling Next-Generation Surgery
• Implicit Weight Uncertainty in Neural Networks

Cited by in corpus (8)

• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
• Robust deep learning-based semantic organ segmentation in hyperspectral images
• Preterm infants' pose estimation with spatio-temporal features
• Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networks
• Action Recognition in Video Recordings from Gynecologic Laparoscopy
• Machine learning-based analysis of hyperspectral images for automated sepsis diagnosis
• OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations
• Label tree semantic losses for rich multi-class medical image segmentation