Tissue classification for laparoscopic image understanding based on multispectral texture analysis.

Intraoperative tissue classification is one of the prerequisites for providing context-aware visualization in computer-assisted minimally invasive surgeries. As many anatomical structures are difficult to differentiate in conventional RGB medical images, we propose a classification method based on multispectral image patches. In a comprehensive ex vivo study through statistical analysis, we show that (1) multispectral imaging data are superior to RGB data for organ tissue classification when used in conjunction with widely applied feature descriptors and (2) combining the tissue texture with the reflectance spectrum improves the classification performance. The classifier reaches an accuracy of 98.4% on our dataset. Multispectral tissue analysis could thus evolve as a key enabling technique in computer-assisted laparoscopy

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

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.Comment: 7 pages, 6 images, 2 table

Kontextsensitivität für den Operationssaal der Zukunft

The operating room of the future is a topic of high interest. In this thesis, which is among the first in the recently defined field of Surgical Data Science, three major topics for automated context awareness in the OR of the future will be examined: improved surgical workflow analysis, the newly developed event impact factors, and as application combining these and other concepts the unified surgical display.Der Operationssaal der Zukunft ist ein Forschungsfeld von großer Bedeutung. In dieser Dissertation, die eine der ersten im kürzlich definierten Bereich „Surgical Data Science“ ist, werden drei Themen für die automatisierte Kontextsensitivität im OP der Zukunft untersucht: verbesserte chirurgische Worflowanalyse, die neuentwickelten „Event Impact Factors“ und als Anwendungsfall, der diese Konzepte mit anderen kombiniert, das vereinheitlichte chirurgische Display

Kontextsensitivität für den Operationssaal der Zukunft

The operating room of the future is a topic of high interest. In this thesis, which is among the first in the recently defined field of Surgical Data Science, three major topics for automated context awareness in the OR of the future will be examined: improved surgical workflow analysis, the newly developed event impact factors, and as application combining these and other concepts the unified surgical display.Der Operationssaal der Zukunft ist ein Forschungsfeld von großer Bedeutung. In dieser Dissertation, die eine der ersten im kürzlich definierten Bereich „Surgical Data Science“ ist, werden drei Themen für die automatisierte Kontextsensitivität im OP der Zukunft untersucht: verbesserte chirurgische Worflowanalyse, die neuentwickelten „Event Impact Factors“ und als Anwendungsfall, der diese Konzepte mit anderen kombiniert, das vereinheitlichte chirurgische Display