bibtext: @InProceedings{ Feh_Intraoperative_MICCAI2024,
                   author = { Fehrentz, Maximilian and Azampour, Mohammad Farid and Dorent, Reuben and Rasheed, Hassan and Galvin, Colin and Golby, Alexandra and Wells III, William M. and Frisken, Sarah and Navab, Nassir and Haouchine, Nazim },
                   title = { { Intraoperative Registration by Cross-Modal Inverse Neural Rendering } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: We present in this paper a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. This disentanglement is achieved by controlling a Neural Radiance Field’s appearance with a multi-style hypernetwork. Once trained, the implicit neural representation serves as a differentiable rendering engine, which can be used to estimate the surgical camera pose by minimizing the dissimilarity between its rendered images and the target intraoperative image. We tested our method on retrospective patients’ data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
title: Intraoperative Registration by Cross-Modal Inverse Neural Rendering
authors: Fehrentz, Maximilian Azampour, Mohammad Farid Dorent, Reuben Rasheed, Hassan Galvin, Colin Golby, Alexandra Wells III, William M. Frisken, Sarah Navab, Nassir Haouchine, Nazim