IEEE Trans Biomed Eng. 2018 Nov;65(11):2649-2659. doi: 10.1109/TBME.2018.2813015. Epub 2018 Mar 9.
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.
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.
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.
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.
This paper 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.
外科数据科学正在发展成为一个研究领域，旨在观察治疗过程中发生的一切，提供情境感知的数据驱动辅助。在 endoscopic video analysis 中，对相机视野内器官的准确分类是一个技术挑战。在此，我们提出了一种新的解剖结构分类和图像标记方法，具有内在的置信度度量，可以用高可靠性估计自身性能，并且可以应用于 RGB 和多光谱成像（MI）数据。
使用基于纹理和反射信息的超像素分类策略进行器官识别。通过分析类概率的分散来估计分类置信度。通过对七头猪的全面体内研究来评估所提出的技术。
当应用于图像标记时，实验中的平均准确率从 RGB 的 65%和 MI 的 80%分别提高到了 90%和 96%。
结果表明，置信度度量对分类准确率有显著影响，MI 数据比 RGB 数据更适合解剖结构标记。
本文通过引入置信度度量，将 MI 数据首次应用于体内腹腔镜组织分类，显著提高了自动内窥镜视频标记的现状。我们实验的数据将在本文发表时作为第一个体内 MI 数据集发布。
