Open AccessFast and accurate vision‐based stereo reconstruction and motion estimation for image‐guided liver surgery
Author(s) -
Speers Andrew D.,
Ma Burton,
Jarnagin William R.,
Himidan Sharifa,
Simpson Amber L.,
Wildes Richard P.
Publication year - 2018
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2018.5071
Subject(s) - computer vision , computer science , artificial intelligence , imaging phantom , motion estimation , estimator , iterative reconstruction , radiology , mathematics , medicine , statistics
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.