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International Journal of Computer Assisted Radiology and Surgery

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07.08.2017 | Original Article

Spine labeling in MRI via regularized distribution matching

verfasst von: Seyed-Parsa Hojjat, Ismail Ayed, Gregory J. Garvin, Kumaradevan Punithakumar

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 11/2017

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Abstract

Purpose

This study investigates an efficient (nearly real-time) two-stage spine labeling algorithm that removes the need for an external training while being applicable to different types of MRI data and acquisition protocols.

Methods

Based solely on the image being labeled (i.e., we do not use training data), the first stage aims at detecting potential vertebra candidates following the optimization of a functional containing two terms: (i) a distribution-matching term that encodes contextual information about the vertebrae via a density model learned from a very simple user input, which amounts to a point (mouse click) on a predefined vertebra; and (ii) a regularization constraint, which penalizes isolated candidates in the solution. The second stage removes false positives and identifies all vertebrae and discs by optimizing a geometric constraint, which embeds generic anatomical information on the interconnections between neighboring structures. Based on generic knowledge, our geometric constraint does not require external training.

Results

We performed quantitative evaluations of the algorithm over a data set of 90 mid-sagittal MRI images of the lumbar spine acquired from 45 different subjects. To assess the flexibility of the algorithm, we used both T1- and T2-weighted images for each subject. A total of 990 structures were automatically detected/labeled and compared to ground-truth annotations by an expert. On the T2-weighted data, we obtained an accuracy of 91.6% for the vertebrae and 89.2% for the discs. On the T1-weighted data, we obtained an accuracy of 90.7% for the vertebrae and 88.1% for the discs.

Conclusion

Our algorithm removes the need for external training while being applicable to different types of MRI data and acquisition protocols. Based on the current testing data, a subject-specific model density and generic anatomical information, our method can achieve competitive performances when applied to T1- and T2-weighted MRI images.

Typically, spine MRI studies contain more than 100 images.

Our meta-analysis of accuracy is not a direct comparison between the methods because the used data sets are different, and might have various levels of difficulties. For instance, the CT data set in [29] contains several unusual appearances (e.g., abnormal spine curvature), and undergo large variations in the field of view, image noise and resolutions.

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Titel: Spine labeling in MRI via regularized distribution matching
verfasst von: Seyed-Parsa Hojjat
Ismail Ayed
Gregory J. Garvin
Kumaradevan Punithakumar
Publikationsdatum: 07.08.2017
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 11/2017
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-017-1651-0

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Spine labeling in MRI via regularized distribution matching

Abstract

Purpose

Methods

Results

Conclusion

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Abstract

Purpose

Methods

Results

Conclusion

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