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

10 Dec 2021, 17:02 (modified: 22 Jun 2022, 18:47)MIDL 2022Readers: Everyone
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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