Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos using NeRFs
This addresses the visual constraints in laparoscopy for medical diagnosis, though it is incremental as it applies an existing method to a new domain.
The paper tackles the problem of reconstructing 3-D abdominal scenes from 2-D laparoscopy surgical videos using Neural Radiance Fields (NeRFs), with promising experimental results but substantial challenges identified.
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.