Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning View

• URL: http://arxiv.org/abs/2408.14789v3
• Date: Thu, 7 Nov 2024 02:43:03 GMT
• Title: Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning View
• Authors: Mingyu Sheng, Jianan Fan, Dongnan Liu, Ron Kikinis, Weidong Cai,
• Abstract summary: We propose an unsupervised method by reframing the video frame segmentation as a graph partitioning problem. A self-supervised pre-trained model is firstly leveraged as a feature extractor to capture high-level semantic features. On the "deep" eigenvectors, a surgical video frame is meaningfully segmented into different modules like tools and tissues, providing distinguishable semantic information.
• Score: 7.594796294925481
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surgical instrument segmentation (SIS) on endoscopic images stands as a long-standing and essential task in the context of computer-assisted interventions for boosting minimally invasive surgery. Given the recent surge of deep learning methodologies and their data-hungry nature, training a neural predictive model based on massive expert-curated annotations has been dominating and served as an off-the-shelf approach in the field, which could, however, impose prohibitive burden to clinicians for preparing fine-grained pixel-wise labels corresponding to the collected surgical video frames. In this work, we propose an unsupervised method by reframing the video frame segmentation as a graph partitioning problem and regarding image pixels as graph nodes, which is significantly different from the previous efforts. A self-supervised pre-trained model is firstly leveraged as a feature extractor to capture high-level semantic features. Then, Laplacian matrixs are computed from the features and are eigendecomposed for graph partitioning. On the "deep" eigenvectors, a surgical video frame is meaningfully segmented into different modules such as tools and tissues, providing distinguishable semantic information like locations, classes, and relations. The segmentation problem can then be naturally tackled by applying clustering or threshold on the eigenvectors. Extensive experiments are conducted on various datasets (e.g., EndoVis2017, EndoVis2018, UCL, etc.) for different clinical endpoints. Across all the challenging scenarios, our method demonstrates outstanding performance and robustness higher than unsupervised state-of-the-art (SOTA) methods. The code is released at https://github.com/MingyuShengSMY/GraphClusteringSIS.git.

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