Graph-Based IVUS Segmentation With Efficient Computer-Aided Refinement
- 30 April 2013
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Medical Imaging
- Vol. 32 (8), 1536-1549
- https://doi.org/10.1109/tmi.2013.2260763
Abstract
A new graph-based approach for segmentation of luminal and external elastic lamina (EEL) surface of coronary vessels in gated 20 MHz intravascular ultrasound (IVUS) image sequences (volumes) is presented. The approach consists of a fully automated segmentation stage (“new automated” or NA) and a user-guided computer-aided refinement (“new refinement” or NR) stage. Both approaches are based on the LOGISMOS approach for simultaneous dual-surface graph-based segmentation. This combination allows the user to efficiently combine general information about IVUS image appearance and case-specific IVUS morphology and therefore deal with frequently occurring issues like calcified plaque-causing signal shadowing-and imaging artifacts. The automated segmentation stage starts with pre-segmenting the lumen to automatically define the lumen centerline, which is used to transform the segmentation task into a LOGISMOS-family graph optimization problem. Following the automated segmentation, the user can inspect the result and correct local or regional segmentation inaccuracies by (iteratively) providing approximate clues regarding the location of the desired surface locations. This expert information is utilized to modify the previously calculated cost functions, locally re-optimizing the underlying modified graph without a need to start the new optimization from scratch. Validation of our method was performed on 41 gated 20 MHz IVUS data sets for which an expert-defined independent standard was available. Resulting from the automated stage of the approach (NA), the mean and standard deviation of the root mean square area errors for the luminal and external elastic lamina surfaces were 1.12 ± 0.67 mm 2 and 2.35 ± 1.61 mm 2 , respectively. Following the refinement stage (NR), the root mean square area errors significantly decreased to 0.82 ± 0.44 mm 2 and 1.17 ± 0.65 mm 2 for the same surfaces, respectively (p <; 0.001 for both surfaces). The approach is delivering a previously unachievable speed of obtaining clinically relevant segmentations compared to the current approaches of automated segmentation followed by manual editing.Keywords
This publication has 19 references indexed in Scilit:
- Optimal Graph Search Based Segmentation of Airway Tree Double Surfaces Across BifurcationsIEEE Transactions on Medical Imaging, 2012
- Fast‐marching segmentation of three‐dimensional intravascular ultrasound images: A pre‐ and post‐intervention studyMedical Physics, 2010
- An Inverse Scattering Algorithm for the Segmentation of the Luminal Border on Intravascular Ultrasound DataLecture Notes in Computer Science, 2009
- Optimal Graph Search Segmentation Using Arc-Weighted Graph for Simultaneous Surface Detection of Bladder and ProstateLecture Notes in Computer Science, 2009
- The Total Variation Regularized $L^1$ Model for Multiscale DecompositionMultiscale Modeling & Simulation, 2007
- Plaque development, vessel curvature, and wall shear stress in coronary arteries assessed by X-ray angiography and intravascular ultrasoundMedical Image Analysis, 2006
- An experimental comparison of min-cut/max- flow algorithms for energy minimization in visionIeee Transactions On Pattern Analysis and Machine Intelligence, 2004
- Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound imagesIEEE Transactions on Medical Imaging, 2000
- Tissue characterization in intravascular ultrasound imagesIEEE Transactions on Medical Imaging, 1998
- Segmentation of intravascular ultrasound images: a knowledge-based approachIEEE Transactions on Medical Imaging, 1995