Classification approach for automatic laparoscopic video database organization

Andru Putra Twinanda, Jacques Marescaux, Michel de Mathelin, Nicolas Padoy

Abstract: One of the advantages of minimally invasive surgery (MIS) is that the underlying digitization provides invaluable information regarding the execution of procedures in various patient-specific conditions. However, such information can only be obtained conveniently if the laparoscopic video database comes with semantic annotations, which are typically provided manually by experts. Considering the growing popularity of MIS, manual annotation becomes a laborious and costly task. In this paper, we tackle the problem of laparoscopic video classification, which consists of automatically identifying the type of abdominal surgery performed in a video. In addition to performing classifications on the full recordings of the procedures, we also carry out sub-video and video clip classifications. These classifications are carried out to investigate how many frames from a video are needed to get a good classification performance and which parts of the procedures contain more discriminative features.