FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos.

ABSTRACT:

Purpose

Fetoscopic laser photocoagulation is a minimally invasive surgery for the treatment of twin-to-twin transfusion syndrome (TTTS). By using a lens/fibre-optic scope, inserted into the amniotic cavity, the abnormal placental vascular anastomoses are identified and ablated to regulate blood flow to both fetuses. Limited field-of-view, occlusions due to fetus presence and low visibility make it difficult to identify all vascular anastomoses. Automatic computer-assisted techniques may provide better understanding of the anatomical structure during surgery for risk-free laser photocoagulation and may facilitate in improving mosaics from fetoscopic videos.

Methods

We propose FetNet, a combined convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neu

SUBMITTER: Bano S 

PROVIDER: S-EPMC7261278 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature