2024-01-23-labrunie24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Automatic 3D/2D Deformable Registration in Minimally Invasive Liver Resection using a Mesh Recovery Network	We propose the patient-specific Liver Mesh Recovery (LMR) framework, to automatically achieve Augmented Reality (AR) guidance by registering a preoperative 3D model in Minimally Invasive Liver Resection (MILR). Existing methods solve registration in MILR by pose estimation followed with numerical optimisation and suffer from a prohibitive intraoperative runtime. The proposed LMR is inspired by the recent Human Mesh Recovery (HMR) framework and forms the first learning-based method to solve registration in MILR. In contrast to existing methods, the computation load in LMR occurs preoperatively, at training time. We construct a patient-specific deformation model and generate patient-specific training data reproducing the typical defects of the automatically detected registration primitives. Experimental results show that LMR’s registration accuracy is on par with optimisation-based methods, whilst running in real-time intraoperatively.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	labrunie24a	0	Automatic 3D/2D Deformable Registration in Minimally Invasive Liver Resection using a Mesh Recovery Network	1104	1123	1104-1123	1104	false	Labrunie, Mathieu and Pizarro, Daniel and Tilmant, Christophe and Bartoli, Adrien	given family Mathieu Labrunie given family Daniel Pizarro given family Christophe Tilmant given family Adrien Bartoli	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/labrunie24a/labrunie24a.pdf