Surgical-tools detection based on Convolutional Neural Network in laparoscopic robot-assisted surgery.

Laparoscopic surgery, a type of minimally invasive surgery, is used in a variety of clinical surgeries because it has a faster recovery rate and causes less pain. However, in general, the robotic system used in laparoscopic surgery can cause damage to the surgical instruments, organs, or tissues during surgery due to a narrow field of view and operating space, and insufficient tactile feedback. This study proposes real-time models for the detection of surgical instruments during laparoscopic surgery by using a CNN(Convolutional Neural Network). A dataset included information of the 7 surgical tools is used for learning CNN. To track surgical instruments in real time, unified architecture of YOLO apply to the models. So as to evaluate performance of the suggested models, degree of recall and precision is calculated and compared. Finally, we achieve 72.26% mean average precision over our dataset.

Automatic detection of hemorrhage and surgical instrument in laparoscopic surgery image.

In this paper, we propose a new method for detecting hemorrhage areas and surgical instruments in robot-assisted laparoscopic surgery images. The proposed scheme utilizes CIELAB information to identify a region of interest (ROI) and segment it. Histogram equalization and Otsu's method are also adopted to compute the detection threshold. Detection is performed automatically and additional adjustment of parameters is not needed. Experiments to verify the proposed algorithm were conducted using actual robot-assisted laparoscopic surgery images. Using the proposed algorithm, the average time consumption was 0.37 s per frame for hemorrhage identification and 0.11 s for instrument detection. The sensitivities were also high enough for practical application.