Video-Based Computer-Aided Laparoscopic Bleeding Management: A Space-Time Memory Neural Network with Positional Encoding and Adversarial Domain Adaptation
Abstract
One of the main challenges in laparoscopic procedures is handling intraoperative bleeding. We propose video-based Computer-aided Laparoscopic Bleeding Management (CALBM) for early detection and management of intraoperative bleeding. Our system performs the online video-based segmentation of bleeding sources and displays them to the surgeon. It hinges on an improved space-time memory network, which we train from real and semi-synthetic data, using adversarial domain adaptation. Our system improves the IoU and F-Score from 69.97% to 73.40% and 50.23% to 58.09% in comparison to the baseline space-time memory network. It is far better than the prior CALBM systems based on still images, which we reimplemented with DeepLabV3+, reaching an IoU and F-Score of 65.86% and 43.19%. The improvement is also supported by user evaluation.
Cite
Text
Rabbani et al. "Video-Based Computer-Aided Laparoscopic Bleeding Management: A Space-Time Memory Neural Network with Positional Encoding and Adversarial Domain Adaptation." Medical Imaging with Deep Learning, 2023.Markdown
[Rabbani et al. "Video-Based Computer-Aided Laparoscopic Bleeding Management: A Space-Time Memory Neural Network with Positional Encoding and Adversarial Domain Adaptation." Medical Imaging with Deep Learning, 2023.](https://mlanthology.org/midl/2023/rabbani2023midl-videobased/)BibTeX
@inproceedings{rabbani2023midl-videobased,
title = {{Video-Based Computer-Aided Laparoscopic Bleeding Management: A Space-Time Memory Neural Network with Positional Encoding and Adversarial Domain Adaptation}},
author = {Rabbani, Navid and Seve, Callyane and Bourdel, Nicolas and Bartoli, Adrien},
booktitle = {Medical Imaging with Deep Learning},
year = {2023},
pages = {961-974},
volume = {172},
url = {https://mlanthology.org/midl/2023/rabbani2023midl-videobased/}
}