A Learning-Driven Framework with Spatial Optimization For Surgical Suture Thread Reconstruction and Autonomous Grasping Under Multiple Topologies and Environmental Noises Article Swipe
Bo Lu
,
Wei Chen
,
Yueming Jin
,
Dandan Zhang
,
Qi Dou
,
Henry K. Chu
,
Pheng‐Ann Heng
,
Yunhui Liu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.00920
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2007.00920
Surgical knot tying is one of the most fundamental and important procedures in surgery, and a high-quality knot can significantly benefit the postoperative recovery of the patient. However, a longtime operation may easily cause fatigue to surgeons, especially during the tedious wound closure task. In this paper, we present a vision-based method to automate the suture thread grasping, which is a sub-task in surgical knot tying and an intermediate step between the stitching and looping manipulations. To achieve this goal, the acquisition of a suture's three-dimensional (3D) information is critical. Towards this objective, we adopt a transfer-learning strategy first to fine-tune a pre-trained model by learning the information from large legacy surgical data and images obtained by the on-site equipment. Thus, a robust suture segmentation can be achieved regardless of inherent environment noises. We further leverage a searching strategy with termination policies for a suture's sequence inference based on the analysis of multiple topologies. Exact results of the pixel-level sequence along a suture can be obtained, and they can be further applied for a 3D shape reconstruction using our optimized shortest path approach. The grasping point considering the suturing criterion can be ultimately acquired. Experiments regarding the suture 2D segmentation and ordering sequence inference under environmental noises were extensively evaluated. Results related to the automated grasping operation were demonstrated by simulations in V-REP and by robot experiments using Universal Robot (UR) together with the da Vinci Research Kit (dVRK) adopting our learning-driven framework.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2007.00920
- https://arxiv.org/pdf/2007.00920
- OA Status
- green
- Cited By
- 1
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3040156066
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3040156066Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2007.00920Digital Object Identifier
- Title
-
A Learning-Driven Framework with Spatial Optimization For Surgical Suture Thread Reconstruction and Autonomous Grasping Under Multiple Topologies and Environmental NoisesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-02Full publication date if available
- Authors
-
Bo Lu, Wei Chen, Yueming Jin, Dandan Zhang, Qi Dou, Henry K. Chu, Pheng‐Ann Heng, Yunhui LiuList of authors in order
- Landing page
-
https://arxiv.org/abs/2007.00920Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2007.00920Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2007.00920Direct OA link when available
- Concepts
-
Image stitching, Tying, Artificial intelligence, Thread (computing), Computer vision, Computer science, Knot tying, Knot (papermaking), Leverage (statistics), Robot, Inference, Segmentation, Motion planning, Robotic surgery, Surgery, Engineering, Medicine, Chemical engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1Per-year citation counts (last 5 years)
- References (count)
-
33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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