A Novel Unsupervised Approach for Minimally-invasive Video Segmentation

Temporal segmentation of laparoscopic video is the first step toward identifying anomalies and interrupts, recognizing actions, annotating video and assessing the surgeons' learning curve. In this paper, a novel approach for temporal segmentation of minimally-invasive videos (MIVS) is proposed. Illumination variation, shadowing, dynamic backgrounds and tissue respiratory motion make it challenging to extract information from laparoscopic videos. These challenges if not properly addressed could increase the errors of data extraction modules. Therefore, in MIVS, several data sets are extracted from laparoscopic videos using different methods to alleviate error effects of data extraction modules on MIVS performance. Each extracted data set is segmented temporally with Genetic Algorithm (GA) after outlier removal. Three different cost functions are examined as objective function of GA. The correlation coefficient is calculated between objective values of the solutions visited by GA and their corresponding performance measures. Performance measures include detection rate, recognition rate and accuracy. Cost functions having negative correlations with all mentioned performance measures are selected. Finally, a multi-objective GA is executed on the data sets to optimize the selected cost functions. MIVS is tested on laparoscopic videos of varicocele and ureteropelvic junction obstruction surgeries collected from hasheminejad kidney center. Experimental results demonstrate that MIVS outperforms the state-of-the-art methods in terms of accuracy, detection rate and recognition rate.