Towards Robust Algorithms for Surgical Phase Recognition via Digital Twin-based Scene Representation

• URL: http://arxiv.org/abs/2410.20026v1
• Date: Sat, 26 Oct 2024 00:49:06 GMT
• Title: Towards Robust Algorithms for Surgical Phase Recognition via Digital Twin-based Scene Representation
• Authors: Hao Ding, Yuqian Zhang, Hongchao Shu, Xu Lian, Ji Woong Kim, Axel Krieger, Mathias Unberath,
• Abstract summary: End-to-end trained neural networks that predict surgical phase directly from videos have shown excellent performance on benchmarks. Our goal is to improve model robustness to variations in the surgical videos by leveraging the digital twin (DT) paradigm. This approach takes advantage of the recent vision foundation models that ensure reliable low-level scene understanding.
• Score: 14.108636146958007
• License:
• Abstract: Purpose: Surgical phase recognition (SPR) is an integral component of surgical data science, enabling high-level surgical analysis. End-to-end trained neural networks that predict surgical phase directly from videos have shown excellent performance on benchmarks. However, these models struggle with robustness due to non-causal associations in the training set, resulting in poor generalizability. Our goal is to improve model robustness to variations in the surgical videos by leveraging the digital twin (DT) paradigm -- an intermediary layer to separate high-level analysis (SPR) from low-level processing (geometric understanding). This approach takes advantage of the recent vision foundation models that ensure reliable low-level scene understanding to craft DT-based scene representations that support various high-level tasks. Methods: We present a DT-based framework for SPR from videos. The framework employs vision foundation models to extract representations. We embed the representation in place of raw video inputs in the state-of-the-art Surgformer model. The framework is trained on the Cholec80 dataset and evaluated on out-of-distribution (OOD) and corrupted test samples. Results: Contrary to the vulnerability of the baseline model, our framework demonstrates strong robustness on both OOD and corrupted samples, with a video-level accuracy of 51.1 on the challenging CRCD dataset, 96.0 on an internal robotics training dataset, and 64.4 on a highly corrupted Cholec80 test set. Conclusion: Our findings lend support to the thesis that DT-based scene representations are effective in enhancing model robustness. Future work will seek to improve the feature informativeness, automate feature extraction, and incorporate interpretability for a more comprehensive framework.

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