Literature DB >> 24012215

Standardized evaluation methodology and reference database for evaluating IVUS image segmentation.

Simone Balocco1, Carlo Gatta2, Francesco Ciompi3, Andreas Wahle4, Petia Radeva3, Stephane Carlier5, Gozde Unal6, Elias Sanidas7, Josepa Mauri8, Xavier Carillo8, Tomas Kovarnik9, Ching-Wei Wang10, Hsiang-Chou Chen10, Themis P Exarchos11, Dimitrios I Fotiadis11, François Destrempes12, Guy Cloutier13, Oriol Pujol14, Marina Alberti3, E Gerardo Mendizabal-Ruiz15, Mariano Rivera16, Timur Aksoy6, Richard W Downe4, Ioannis A Kakadiaris15.   

Abstract

This paper describes an evaluation framework that allows a standardized and quantitative comparison of IVUS lumen and media segmentation algorithms. This framework has been introduced at the MICCAI 2011 Computing and Visualization for (Intra)Vascular Imaging (CVII) workshop, comparing the results of eight teams that participated. We describe the available data-base comprising of multi-center, multi-vendor and multi-frequency IVUS datasets, their acquisition, the creation of the reference standard and the evaluation measures. The approaches address segmentation of the lumen, the media, or both borders; semi- or fully-automatic operation; and 2-D vs. 3-D methodology. Three performance measures for quantitative analysis have been proposed. The results of the evaluation indicate that segmentation of the vessel lumen and media is possible with an accuracy that is comparable to manual annotation when semi-automatic methods are used, as well as encouraging results can be obtained also in case of fully-automatic segmentation. The analysis performed in this paper also highlights the challenges in IVUS segmentation that remains to be solved.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Algorithm comparison; Evaluation framework; IVUS (intravascular ultrasound); Image segmentation

Mesh:

Year:  2013        PMID: 24012215     DOI: 10.1016/j.compmedimag.2013.07.001

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  10 in total

1.  Assessment of image features for vessel wall segmentation in intravascular ultrasound images.

Authors:  Lucas Lo Vercio; José Ignacio Orlando; Mariana Del Fresno; Ignacio Larrabide
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-01-25       Impact factor: 2.924

2.  CycleGAN for style transfer in X-ray angiography.

Authors:  Oleksandra Tmenova; Rémi Martin; Luc Duong
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-07-08       Impact factor: 2.924

3.  A Domain Enriched Deep Learning Approach to Classify Atherosclerosis using Intravascular Ultrasound Imaging.

Authors:  Max L Olender; Lambros S Athanasiou; Lampros K Michalis; Dimitris I Fotiadis; Elazer R Edelman
Journal:  IEEE J Sel Top Signal Process       Date:  2020-06-15       Impact factor: 6.856

4.  Simultaneous Registration of Location and Orientation in Intravascular Ultrasound Pullbacks Pairs Via 3D Graph-Based Optimization.

Authors:  Ling Zhang; Andreas Wahle; Zhi Chen; Li Zhang; Richard W Downe; Tomas Kovarnik; Milan Sonka
Journal:  IEEE Trans Med Imaging       Date:  2015-06-11       Impact factor: 10.048

5.  Cascaded learning in intravascular ultrasound: coronary stent delineation in manual pullbacks.

Authors:  Tobias Wissel; Katharina A Riedl; Klaus Schaefers; Hannes Nickisch; Fabian J Brunner; Nikolas D Schnellbaecher; Stefan Blankenberg; Moritz Seiffert; Michael Grass
Journal:  J Med Imaging (Bellingham)       Date:  2022-03-28

6.  Fluorescence lifetime imaging and intravascular ultrasound: co-registration study using ex vivo human coronaries.

Authors:  Dimitris Gorpas; Hussain Fatakdawala; Julien Bec; Dinglong Ma; Diego R Yankelevich; Jinyi Qi; Laura Marcu
Journal:  IEEE Trans Med Imaging       Date:  2014-08-21       Impact factor: 10.048

7.  Optimal surface segmentation with convex priors in irregularly sampled space.

Authors:  Abhay Shah; Michael D Abámoff; Xiaodong Wu
Journal:  Med Image Anal       Date:  2019-02-08       Impact factor: 8.545

8.  Assessment of Inter-Expert Variability and of an Automated Segmentation Method of 40 and 60 MHz IVUS Images of Coronary Arteries.

Authors:  François Destrempes; Marie-Hélène Roy Cardinal; Yoshifumi Saijo; Gérard Finet; Jean-Claude Tardif; Guy Cloutier
Journal:  PLoS One       Date:  2017-01-20       Impact factor: 3.240

9.  SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing.

Authors:  Lennart Bargsten; Alexander Schlaefer
Journal:  Int J Comput Assist Radiol Surg       Date:  2020-06-18       Impact factor: 2.924

Review 10.  Advanced Ultrasound and Photoacoustic Imaging in Cardiology.

Authors:  Min Wu; Navchetan Awasthi; Nastaran Mohammadian Rad; Josien P W Pluim; Richard G P Lopata
Journal:  Sensors (Basel)       Date:  2021-11-28       Impact factor: 3.576

  10 in total

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