Automatic bleeding detection in laparoscopic surgery based on a faster region-based convolutional neural network.

ABSTRACT:

Background

Laparoscopic surgery has been in great demand over the past decades; it has also brought several obstacles, such as increasing difficulty in maintaining hemostasis, changes in surgical approach, and reduced field of vision. Locating the bleeding point can help surgeons to control bleeding quickly, however, to date, there have been no tools designed for automatic bleeding tracking in laparoscopic operations. Herein, we have proposed a spatiotemporal hybrid model based on a faster region-based convolutional neural network (RCNN) for bleeding point detection in laparoscopic surgery videos.

Methods

Laparoscopic videos performed at our hospital were retrieved and images containing bleeding events were extracted. Spatiotemporal features were extracted by using red-green

SUBMITTER: Hua S 

PROVIDER: S-EPMC9201197 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature