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In vivo semi-automatic segmentation of multicontrast cardiovascular magnetic resonance for prospective cohort studies on plaque tissue composition: initial experience

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Abstract

Automatic in vivo segmentation of multicontrast (multisequence) carotid magnetic resonance for plaque composition has been proposed as a substitute for manual review to save time and reduce inter-reader variability in large-scale or multicenter studies. Using serial images from a prospective longitudinal study, we sought to compare a semi-automatic approach versus expert human reading in analyzing carotid atherosclerosis progression. Baseline and 6-month follow-up multicontrast carotid images from 59 asymptomatic subjects with 16–79 % carotid stenosis were reviewed by both trained radiologists with 2–4 years of specialized experience in carotid plaque characterization with MRI and a previously reported automatic atherosclerotic plaque segmentation algorithm, referred to as morphology-enhanced probabilistic plaque segmentation (MEPPS). Agreement on measurements from individual time points, as well as on compositional changes, was assessed using the intraclass correlation coefficient (ICC). There was good agreement between manual and MEPPS reviews on individual time points for calcification (CA) (area: ICC; 0.85–0.91; volume: ICC; 0.92–0.95) and lipid-rich necrotic core (LRNC) (area: ICC; 0.78–0.82; volume: ICC; 0.84–0.86). For compositional changes, agreement was good for CA volume change (ICC; 0.78) and moderate for LRNC volume change (ICC; 0.49). Factors associated with LRNC progression as detected by MEPPS review included intraplaque hemorrhage (positive association) and reduction in low-density lipoprotein cholesterol (negative association), which were consistent with previous findings from manual review. Automatic classifier for plaque composition produced results similar to expert manual review in a prospective serial MRI study of carotid atherosclerosis progression. Such automatic classification tools may be beneficial in large-scale multicenter studies by reducing image analysis time and avoiding bias between human reviewers.

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Acknowledgments

MRI scans were performed as part of a clinical trial funded by Kowa Research Institute.

Conflict of interest

DSH receives grant support from GE Healthcare. DX is a consultant for VPDiagnostics Inc, Seattle, WA. WSK is now affiliated with Presage Biosciences, Seattle, WA. TSH receives research grants from Philips Healthcare. CY receives grant support from Philips Healthcare and is a member of Radiology Medical Advisory Network of Philips Healthcare. The remaining authors report no conflicts of interest.

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    Authors and Affiliations

    1. Department of Radiology, University of Washington, 850 Republican St, Seattle, WA, 98109, USA

      Taku Yoneyama, Jie Sun, Daniel S. Hippe, Niranjan Balu, Dongxiang Xu, William S. Kerwin & Chun Yuan

    2. Department of Neurosurgery, Tokyo Women’s Medical University, 8-1, Kawadachou, Shinjukuku, Tokyo, 1628666, Japan

      Taku Yoneyama

    3. Department of Surgery, Harborview Medical Center, University of Washington, 325 9th Ave, Seattle, WA, 98104, USA

      Thomas S. Hatsukami

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    1. Taku Yoneyama
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    2. Jie Sun
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    Correspondence to Jie Sun or Chun Yuan.

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    Taku Yoneyama and Jie Sun have contributed equally to this work.

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    Yoneyama, T., Sun, J., Hippe, D.S. et al. In vivo semi-automatic segmentation of multicontrast cardiovascular magnetic resonance for prospective cohort studies on plaque tissue composition: initial experience. Int J Cardiovasc Imaging 32, 73–81 (2016). https://doi.org/10.1007/s10554-015-0704-0

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    • DOI: https://doi.org/10.1007/s10554-015-0704-0

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