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 photorealistic 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.
@article{arxiv.2310.11645,
title = {Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs},
author = {Khoa Tuan Nguyen and Francesca Tozzi and Nikdokht Rashidian and Wouter Willaert and Joris Vankerschaver and Wesley De Neve},
journal= {arXiv preprint arXiv:2310.11645},
year = {2023}
}
Comments
The Version of Record of this contribution is published in MLMI 2023 Part I, and is available online at https://doi.org/10.1007/978-3-031-45673-2_9