| Autor: |
Ryo Furukawa, Elvis Chen, Ryusuke Sagawa, Shiro Oka, Hiroshi Kawasaki |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Healthcare Technology Letters, Vol 11, Iss 2-3, Pp 196-205 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2053-3713 |
| DOI: |
10.1049/htl2.12083 |
| Popis: |
Abstract Accurate 3D shape measurement is crucial for surgical support and alignment in robotic surgery systems. Stereo cameras in laparoscopes offer a potential solution; however, their accuracy in stereo image matching diminishes when the target image has few textures. Although stereo matching with deep learning has gained significant attention, supervised learning requires a large dataset of images with depth annotations, which are scarce for laparoscopes. Thus, there is a strong demand to explore alternative methods for depth reconstruction or annotation for laparoscopes. Active stereo techniques are a promising approach for achieving 3D reconstruction without textures. In this study, a 3D shape reconstruction method is proposed using an ultra‐small patterned projector attached to a laparoscopic arm to address these issues. The pattern projector emits a structured light with a grid‐like pattern that features node‐wise modulation for positional encoding. To scan the target object, multiple images are taken while the projector is in motion, and the relative poses of the projector and a camera are auto‐calibrated using a differential rendering technique. In the experiment, the proposed method is evaluated by performing 3D reconstruction using images obtained from a surgical robot and comparing the results with a ground‐truth shape obtained from X‐ray CT. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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