Summary of Advancing 6-dof Instrument Pose Estimation in Variable X-ray Imaging Geometries, by Christiaan G.a. Viviers et al.
Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging Geometries
by Christiaan G.A. Viviers, Lena Filatova, Maurice Termeer, Peter H.N. de With, Fons van der Sommen
First submitted to arxiv on: 19 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a general-purpose approach for 6-DoF pose estimation of surgical instruments during minimally invasive surgeries. The existing deep learning methods require custom approaches for each object and laborious setup and training environments, which can be time-consuming and lack real-time computation. To address this issue, the authors introduce a novel YOLOv5-6D pose architecture that achieves accurate and fast object pose estimation. The proposed model is tested on public benchmarks and shows competitive results while being significantly faster at 42 FPS on GPU. Additionally, the method generalizes across varying X-ray acquisition geometry and semantic image complexity to enable accurate pose estimation over different domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to accurately estimate the position of surgical instruments during surgeries. The current methods require a lot of work and are not very fast. To solve this problem, the authors have developed a new model that can quickly and accurately find the position of objects in X-ray images. This model is tested on many different types of data and shows good results. It also works well with different types of X-ray machines and image details. |
Keywords
» Artificial intelligence » Deep learning » Pose estimation
