TY - GEN
T1 - XSynthMorph
T2 - 11th International Workshop on Biomedical Image Registration, WBIR 2024, held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024
AU - Cafaro, Alexandre
AU - Leroy, Amaury
AU - Beldjoudi, Guillaume
AU - Maury, Pauline
AU - Robert, Charlotte
AU - Deutsch, Eric
AU - Grégoire, Vincent
AU - Lepetit, Vincent
AU - Paragios, Nikos
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - We introduce a novel unsupervised approach to recovering and registering a 3D volume from only two planar projections that exploits a previously-captured 3D volume of the patient. Such pre-capturing volume is readily available in many important medical procedures and previous methods already used such a volume. Earlier methods that work by deforming this volume to match the projections can fail when the number of projections is very low as the alignment becomes underconstrained. We show how to use a generative model of the volume structures to constrain the deformation and obtain a correct estimate. Moreover, our method is independant of the number, calibration and geometry of projections and could be adapted to new configurations without retraining. We evaluate our approach on a challenging dataset and show it outperforms state-of-the-art methods. As a result, our method could be used in treatment scenarios such as surgery and radiotherapy while drastically reducing patient radiation exposure.
AB - We introduce a novel unsupervised approach to recovering and registering a 3D volume from only two planar projections that exploits a previously-captured 3D volume of the patient. Such pre-capturing volume is readily available in many important medical procedures and previous methods already used such a volume. Earlier methods that work by deforming this volume to match the projections can fail when the number of projections is very low as the alignment becomes underconstrained. We show how to use a generative model of the volume structures to constrain the deformation and obtain a correct estimate. Moreover, our method is independant of the number, calibration and geometry of projections and could be adapted to new configurations without retraining. We evaluate our approach on a challenging dataset and show it outperforms state-of-the-art methods. As a result, our method could be used in treatment scenarios such as surgery and radiotherapy while drastically reducing patient radiation exposure.
UR - http://www.scopus.com/inward/record.url?scp=85206894859&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73480-9_2
DO - 10.1007/978-3-031-73480-9_2
M3 - Conference contribution
AN - SCOPUS:85206894859
SN - 9783031734793
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 19
EP - 33
BT - Biomedical Image Registration - 11th International Workshop, WBIR 2024, Held in Conjunction with MICCAI 2024, Proceedings
A2 - Modat, Marc
A2 - Špiclin, Žiga
A2 - Hering, Alessa
A2 - Simpson, Ivor
A2 - Bastiaansen, Wietske
A2 - Mok, Tony C. W.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 October 2024 through 6 October 2024
ER -