Graph-Based IVUS Segmentation With Efficient Computer-Aided Refinement

Shanhui Sun; Milan Sonka; Reinhard R Beichel

doi:10.1109/TMI.2013.2260763

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Graph-Based IVUS Segmentation With Efficient Computer-Aided Refinement

Journal article

Open access

Peer reviewed

Graph-Based IVUS Segmentation With Efficient Computer-Aided Refinement

Shanhui Sun, Milan Sonka and Reinhard R Beichel

IEEE transactions on medical imaging, Vol.32(8), pp.1536-1549

08/2013

DOI: 10.1109/TMI.2013.2260763

PMCID: PMC3883441

PMID: 23649180

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https://www.ncbi.nlm.nih.gov/pmc/articles/3883441View

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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.

Image segmentation

intravascular ultrasound

Graph-based segmentation

Imaging

Surface morphology

Logic gates

Cost function

Surface roughness

segmentation refinement

Rough surfaces

Details

Title: Subtitle: Graph-Based IVUS Segmentation With Efficient Computer-Aided Refinement
Creators: Shanhui Sun - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Milan Sonka - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
Reinhard R Beichel - Dept. of Internal Med., Univ. of Iowa, Iowa City, IA, USA
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.32(8), pp.1536-1549
DOI: 10.1109/TMI.2013.2260763
PMID: 23649180
PMCID: PMC3883441
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Language: English
Date published: 08/2013
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Ophthalmology and Visual Sciences
Record Identifier: 9984047744902771

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