Name
Title | ||
|---|---|---|
FetNet: a recurrent convolutional network for occlusion identification in fetoscopic videos. |
Abstract | ||
|---|---|---|
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. We propose FetNet, a combined convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network architecture for the spatio-temporal identification of fetoscopic events. We adapt an existing CNN architecture for spatial feature extraction and integrated it with the LSTM network for end-to-end spatio-temporal inference. We introduce differential learning rates during the model training to effectively utilising the pre-trained CNN weights. This may support computer-assisted interventions (CAI) during fetoscopic laser photocoagulation. We perform quantitative evaluation of our method using 7 in vivo fetoscopic videos captured from different human TTTS cases. The total duration of these videos was 5551 s (138,780 frames). To test the robustness of the proposed approach, we perform 7-fold cross-validation where each video is treated as a hold-out or test set and training is performed using the remaining videos. FetNet achieved superior performance compared to the existing CNN-based methods and provided improved inference because of the spatio-temporal information modelling. Online testing of FetNet, using a Tesla V100-DGXS-32GB GPU, achieved a frame rate of 114 fps. These results show that our method could potentially provide a real-time solution for CAI and automating occlusion and photocoagulation identification during fetoscopic procedures. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11548-020-02169-0 | International Journal of Computer Assisted Radiology and Surgery |
Keywords | DocType | Volume |
Deep learning, Surgical vision, Twin-to-twin transfusion syndrome (TTTS), Fetoscopy, Video segmentation, Computer assisted interventions (CAI) | Journal | 15 |
Issue | ISSN | Citations |
5 | 1861-6410 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sophia Bano | 1 | 0 | 0.34 |
| Francisco Vasconcelos | 2 | 76 | 8.63 |
| Emmanuel Vander Poorten | 3 | 32 | 7.68 |
| Tom Vercauteren | 4 | 1956 | 108.68 |
| Sébastien Ourselin | 5 | 2499 | 237.61 |
| Jan Deprest | 6 | 123 | 20.45 |
| Danail Stoyanov | 7 | 792 | 81.36 |