Video-based Computer-aided Laparoscopic Bleeding Management: a Space-time Memory Neural Network with Positional Encoding and Adversarial Domain Adaptation

Navid Rabbani, Callyane Seve, Nicolas Bourdel, Adrien Bartoli

Keywords: Bleeding Segmentation, Computer Assisted Intervention, Laparoscopic Surgery, Video Semantic Segmentation, 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.

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Paper Type: both

Primary Subject Area: Segmentation

Secondary Subject Area: Application: Endoscopy

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Code And Data: http://igt.ip.uca.fr/%7Eab/code_and_datasets/datasets/bleeding_segmentation_v1p0.zip