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Healthc Technol Lett. 2018 Oct 19;5(5):208-214. doi: 10.1049/htl.2018.5071. eCollection 2018 Oct.

Fast and accurate vision-based stereo reconstruction and motion estimation for image-guided liver surgery.

Author information

1
Department of Electrical Engineering and Computer Science, York University, Toronto, ON, Canada.
2
Hepatopancreatobiliary Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
3
Department of Surgery, The Hospital for Sick Children, Toronto, ON, Canada.
4
Department of Surgery, University of Toronto, Toronto, ON, Canada.

Abstract

Image-guided liver surgery aims to enhance the precision of resection and ablation by providing fast localisation of tumours and adjacent complex vasculature to improve oncologic outcome. This Letter presents a novel end-to-end solution for fast stereo reconstruction and motion estimation that demonstrates high accuracy with phantom and clinical data. The authors' computationally efficient coarse-to-fine (CTF) stereo approach facilitates liver imaging by accounting for low texture regions, enabling precise three-dimensional (3D) boundary recovery through the use of adaptive windows and utilising a robust 3D motion estimator to reject spurious data. To the best of their knowledge, theirs is the only adaptive CTF matching approach to reconstruction and motion estimation that registers time series of reconstructions to a single key frame for registration to a volumetric computed tomography scan. The system is evaluated empirically in controlled laboratory experiments with a liver phantom and motorised stages for precise quantitative evaluation. Additional evaluation is provided through testing with patient data during liver resection.

KEYWORDS:

ablation; accurate vision-based stereo reconstruction; adaptive CTF matching approach; adaptive windows; adjacent complex vasculature; cancer; coarse-to-fine stereo approach; computerised tomography; end-to-end solution; fast vision-based stereo reconstruction; image matching; image reconstruction; image registration; image texture; image-guided liver surgery; liver; liver imaging; liver phantom; liver resection; low texture regions; medical image processing; motion estimation; oncologic outcome; phantoms; precise quantitative evaluation; robust 3D motion estimator; stereo image processing; surgery; three-dimensional boundary recovery; time series; tumours; volumetric computed tomography scan

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