Unsupervised Anomaly Detection of Forceps Force by Localizing the Region of Interest
The lack of haptic feedback in robotic surgical systems can lead to unintended tissue damage as a result of excessive mechanical force. To address this issue, many studies in vision-based force sensing have focused on supervised learning approaches, which require labeled force data for training. How...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11097314/ |
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| Summary: | The lack of haptic feedback in robotic surgical systems can lead to unintended tissue damage as a result of excessive mechanical force. To address this issue, many studies in vision-based force sensing have focused on supervised learning approaches, which require labeled force data for training. However, obtaining force labels in real surgical scenarios remains challenging. In this study, we propose an alternative approach by formulating the force feedback problem as an image-based anomaly detection task. By leveraging unsupervised learning, we overcome the limitations posed by the scarcity of labeled abnormal force data and present a novel abnormal force anomaly detection method. Our method employs a bidirectional generative adversarial network (BiGAN) and integrates a region-of-interest (ROI) guided module to enhance attention on the contact area between surgical instruments and tissue. The model is trained to distinguish between normal and abnormal force samples based on deformation features in an organ. Extensive experiments on an ex vivo porcine spleen manipulation dataset demonstrate the efficacy of the proposed ROI-guided BiGAN in accurately detecting abnormal forces. The ROI-guided BiGAN exhibits an area under the curve of 0.7963 and a recall rate of 0.9174 when applied to the test data. The proposed framework is expected to be useful as a practical tool for abnormal forceps force detection and to serve as a useful alarm in robot-assisted surgery. |
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| ISSN: | 2169-3536 |