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Proceedings Paper

Multi-atlas-based tissue identification in the lower leg using pQCT
Author(s): Sokratis Makrogiannis; Azubuike Okorie; Taposh Biswas; Luigi Ferrucci
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Paper Abstract

Accurate and reproducible tissue identification techniques are essential for understanding structural and functional changes that either occur naturally with aging, or because of chronic disease, or in response to intervention therapies. These image analysis techniques are frequently utilized for characterization of changes in bone architecture to assess fracture risk, and for the assessment of loss of muscle mass and strength defined as sarcopenia. Peripheral quantitative computed tomography (pQCT) is widely employed for tissue identification and analysis. Advantages of pQCT scanners are compactness, portability, and low radiation dose. However, these characteristics imply limitations in spatial resolution and SNR. Therefore, there is still a need for segmentation methods that address image quality limitations and artifacts such as patient motion. In this paper, we introduce multi-atlas segmentation (MAS) techniques to identify soft and hard tissues in pQCT scans of the proximal tibia (~ 66% of tibial length) and to address the above factors that limit delineation accuracy. To calculate the deformation fields, we employed multi-grid free-form deformation (FFD) models with B-splines and a symmetric extension of the log-domain diffeomorphic demons (SDD). We then applied majority voting and Simultaneous Truth And Performance Level Estimation (STAPLE) for label fusion. We compared the results of our MAS methodology for each deformable registration model and each label fusion method, using Dice similarity coefficient scores (DSC). The results show that our technique utilizing SDD with STAPLE produces very good accuracy (DSC mean of 0.868) over all tissues, even for scans with considerable quality degradations caused by motion artifacts.

Paper Details

Date Published: 10 March 2020
PDF: 6 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113133D (10 March 2020); doi: 10.1117/12.2550010
Show Author Affiliations
Sokratis Makrogiannis, Delaware State Univ. (United States)
Azubuike Okorie, Delaware State Univ. (United States)
Taposh Biswas, Delaware State Univ. (United States)
Luigi Ferrucci, National Institutes of Health (United States)

Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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