A Novel Unsupervised Approach for Minimally-Invasive Video Segmentation

Toktam Khatibi, Mohammad Mehdi Sepehri, Pejman Shadpour



Background: Laparoscopy or minimally invasive surgery is a surgical procedure in which laparoscope and other surgical instruments are inserted inside body via a few small incisions. Laparoscope is used to look inside the patient's body and records displayed images. Temporal segmentation of laparoscopic videos has many applications like detecting laparoscopic anomalies and interrupts. It is prerequisite of laparoscopic action recognition for tagging laparoscopic video clips, training to the surgeons and fast retrieval of tagged laparoscopic video clips. Temporal segmentation of videos is is done with the aim of generating homogeneous segments.


Methods: In this paper, a novel approach for minimally-invasive video segmentation (MIVS) is proposed. In MIVS, several data sets are extracted from laparoscopic videos for increasing the confidence and reducing error of estimation. Each extracted data set is segmented individually with Genetic Algorithm several times after outlier removal. Each time, a different cost function is used as objective function of GA. The correlation coefficient is measured between objective values of individuals of each GA execution and their associated performance measures including detection rate, recognition rate and accuracy. Cost functions having negative correlation with all mentioned performance measures are selected as cost function of the next step segmentation which segments several data sets simultaneously exploiting Multi-objective GA.

Results: MIVS is tested on laparoscopic videos of Varicocelle and UPJO surgeries collected from HASHEMINEZHAD Kidney Center. Experimental results show that MIVS can segment laparoscopic videos with accuracy of 94.89%. Conclusions: MIVS outperforms previous presented segmentation methods in segmenting minimally-invasive surgical videos.


Minimally invasive surgery; surgical instruments; video segmentation; multi-objective genetic algorithm (MOGA)

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