title: Graph-based IVUS segmentation with efficient computer-aided refinement.
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 ( 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.
authors: Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA. shanhui-sun@uiowa.edu
authors: Shanhui
authors: S
authors: Sun
authors: Milan
authors: M
authors: Sonka
authors: Reinhard R
authors: RR
authors: Beichel
mesh_terms: D000465:Algorithms; D003331:Coronary Vessels / Q000000981:diagnostic imaging; D016208:Databases, Factual; D004562:Electrocardiography; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D018084:Ultrasonography, Interventional / Q000379:methods*
references: IEEE Trans Med Imaging. 2000 Oct;19(10):996-1011
references: 11131497
references: IEEE Trans Med Imaging. 2006 May;25(5):590-601
references: 16689263
references: Med Image Comput Comput Assist Interv. 2009;12(Pt 2):885-92
references: 20426195
references: IEEE Trans Pattern Anal Mach Intell. 2006 Jan;28(1):119-34
references: 16402624
references: Int J Card Imaging. 1999 Dec;15(6):513-22
references: 10768745
references: Int J Cardiovasc Imaging. 2002 Aug;18(4):235-48
references: 12123316
references: IEEE Trans Pattern Anal Mach Intell. 2004 Sep;26(9):1124-37
references: 15742889
references: IEEE Trans Med Imaging. 1995;14(4):719-32
references: 18215876
references: Med Phys. 2010 Jul;37(7):3633-47
references: 20831071
references: IEEE Trans Med Imaging. 1998 Dec;17(6):889-99
references: 10048846
references: Med Image Anal. 2006 Aug;10(4):615-31
references: 16644262
references: Med Image Comput Comput Assist Interv. 2009;12(Pt 2):827-35
references: 20426188
references: IEEE Trans Med Imaging. 2010 Dec;29(12):2023-37
references: 20643602
references: IEEE Trans Med Imaging. 2013 Mar;32(3):493-510
references: 23070299
references: IEEE Trans Inf Technol Biomed. 2008 May;12(3):335-47
references: 18693501