Uncertainty-Aware Organ Classification for Surgical Data Science Applications in Laparoscopy
This work significantly enhances the state of the art in automatic labeling of endoscopic videos by introducing a confidence metric and being the first to use multispectral imaging data for in vivo laparoscopic tissue classification, though it is incremental as it builds on existing classification methods.
The paper tackled the problem of accurately classifying organs in endoscopic videos for surgical data science by proposing an uncertainty-aware approach with a confidence measure, resulting in increased mean accuracy from 65% (RGB) and 80% (MI) to 90% (RGB) and 96% (MI).
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.