Towards abdominal 3-D scene rendering from laparoscopy surgical videos using NeRFs
(2024)
MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I.
In Lecture notes in computer science
14348.
p.83-93
- Author
- Khoa Tuan Nguyen (UGent) , Francesca Tozzi (UGent) , Niki Rashidian (UGent) , Wouter Willaert (UGent) , Joris Vankerschaver (UGent) and Wesley De Neve (UGent)
- Organization
- Abstract
- Given that a conventional laparoscope only provides a two-dimensional (2-D) view, the detection and diagnosis of medical ailments can be challenging. To overcome the visual constraints associated with laparoscopy, the use of laparoscopic images and videos to reconstruct the three-dimensional (3-D) anatomical structure of the abdomen has proven to be a promising approach. Neural Radiance Fields (NeRFs) have recently gained attention thanks to their ability to generate photo-realistic images from a 3-D static scene, thus facilitating a more comprehensive exploration of the abdomen through the synthesis of new views. This distinguishes NeRFs from alternative methods such as Simultaneous Localization and Mapping (SLAM) and depth estimation. In this paper, we present a comprehensive examination of NeRFs in the context of laparoscopy surgical videos, with the goal of rendering abdominal scenes in 3-D. Although our experimental results are promising, the proposed approach encounters substantial challenges, which require further exploration in future research.
- Keywords
- RECONSTRUCTION, 3-D reconstruction, Laparoscopy, Neural Rendering, View Synthesis
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HKYHYMJJGX2MGJV02D666BP5
- MLA
- Nguyen, Khoa Tuan, et al. “Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos Using NeRFs.” MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I, vol. 14348, Springer, 2024, pp. 83–93, doi:10.1007/978-3-031-45673-2_9.
- APA
- Nguyen, K. T., Tozzi, F., Rashidian, N., Willaert, W., Vankerschaver, J., & De Neve, W. (2024). Towards abdominal 3-D scene rendering from laparoscopy surgical videos using NeRFs. MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I, 14348, 83–93. https://doi.org/10.1007/978-3-031-45673-2_9
- Chicago author-date
- Nguyen, Khoa Tuan, Francesca Tozzi, Niki Rashidian, Wouter Willaert, Joris Vankerschaver, and Wesley De Neve. 2024. “Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos Using NeRFs.” In MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I, 14348:83–93. Springer. https://doi.org/10.1007/978-3-031-45673-2_9.
- Chicago author-date (all authors)
- Nguyen, Khoa Tuan, Francesca Tozzi, Niki Rashidian, Wouter Willaert, Joris Vankerschaver, and Wesley De Neve. 2024. “Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos Using NeRFs.” In MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I, 14348:83–93. Springer. doi:10.1007/978-3-031-45673-2_9.
- Vancouver
- 1.Nguyen KT, Tozzi F, Rashidian N, Willaert W, Vankerschaver J, De Neve W. Towards abdominal 3-D scene rendering from laparoscopy surgical videos using NeRFs. In: MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I. Springer; 2024. p. 83–93.
- IEEE
- [1]K. T. Nguyen, F. Tozzi, N. Rashidian, W. Willaert, J. Vankerschaver, and W. De Neve, “Towards abdominal 3-D scene rendering from laparoscopy surgical videos using NeRFs,” in MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I, Vancouver, CANADA, 2024, vol. 14348, pp. 83–93.
@inproceedings{01HKYHYMJJGX2MGJV02D666BP5,
abstract = {{Given that a conventional laparoscope only provides a two-dimensional (2-D) view, the detection and diagnosis of medical ailments can be challenging. To overcome the visual constraints associated with laparoscopy, the use of laparoscopic images and videos to reconstruct the three-dimensional (3-D) anatomical structure of the abdomen has proven to be a promising approach. Neural Radiance Fields (NeRFs) have recently gained attention thanks to their ability to generate photo-realistic images from a 3-D static scene, thus facilitating a more comprehensive exploration of the abdomen through the synthesis of new views. This distinguishes NeRFs from alternative methods such as Simultaneous Localization and Mapping (SLAM) and depth estimation. In this paper, we present a comprehensive examination of NeRFs in the context of laparoscopy surgical videos, with the goal of rendering abdominal scenes in 3-D. Although our experimental results are promising, the proposed approach encounters substantial challenges, which require further exploration in future research.}},
author = {{Nguyen, Khoa Tuan and Tozzi, Francesca and Rashidian, Niki and Willaert, Wouter and Vankerschaver, Joris and De Neve, Wesley}},
booktitle = {{MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I}},
isbn = {{9783031456725}},
issn = {{0302-9743}},
keywords = {{RECONSTRUCTION,3-D reconstruction,Laparoscopy,Neural Rendering,View Synthesis}},
language = {{eng}},
location = {{Vancouver, CANADA}},
pages = {{83--93}},
publisher = {{Springer}},
title = {{Towards abdominal 3-D scene rendering from laparoscopy surgical videos using NeRFs}},
url = {{http://doi.org/10.1007/978-3-031-45673-2_9}},
volume = {{14348}},
year = {{2024}},
}
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