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On the use of coupled shape priors for segmentation of magnetic resonance images of the knee.

ABSTRACT: Active contour techniques have been widely employed for medical image segmentation. Significant effort has been focused on the use of training data to build prior statistical models applicable specifically to problems where the objects of interest are embedded in cluttered background. Usually, the training data consist of whole shapes of certain organs or structures obtained manually by clinical experts. The resulting prior models enforce segmentation accuracy uniformly over the entire structure or structures to be identified. In this paper, we consider a new coupled prior shape model which is demonstrated to provide high accuracy, specifically in the region of the interest where precision is most needed for the application of the segmentation of the femur and tibia in magnetic resonance (MR) images. Experimental results for the segmentation of MR images of human knees demonstrate that the combination of the new coupled prior shape and a directional edge force provides the improved segmentation performance. Moreover, the new approach allows for equivalent accurate identification of bone marrow lesions, a promising biomarker related to osteoarthritis, to the current state of the art but requires significantly less manual interaction.

SUBMITTER: Pang J

PROVIDER: S-EPMC4439111 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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On the use of coupled shape priors for segmentation of magnetic resonance images of the knee.

Pang Jincheng J Driban Jeffrey B JB McAlindon Timothy E TE Tamez-Peña José G JG Fripp Jurgen J Miller Eric L EL

IEEE journal of biomedical and health informatics 20140630 3

Active contour techniques have been widely employed for medical image segmentation. Significant effort has been focused on the use of training data to build prior statistical models applicable specifically to problems where the objects of interest are embedded in cluttered background. Usually, the training data consist of whole shapes of certain organs or structures obtained manually by clinical experts. The resulting prior models enforce segmentation accuracy uniformly over the entire structure ...[more]

PMID: 25014973

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Project description:Transcranial brain stimulation (TBS) has been established as a method for modulating and mapping the function of the human brain, and as a potential treatment tool in several brain disorders. Typically, the stimulation is applied using a one-size-fits-all approach with predetermined locations for the electrodes, in electric stimulation (TES), or the coil, in magnetic stimulation (TMS), which disregards anatomical variability between individuals. However, the induced electric field distribution in the head largely depends on anatomical features implying the need for individually tailored stimulation protocols for focal dosing. This requires detailed models of the individual head anatomy, combined with electric field simulations, to find an optimal stimulation protocol for a given cortical target. Considering the anatomical and functional complexity of different brain disorders and pathologies, it is crucial to account for the anatomical variability in order to translate TBS from a research tool into a viable option for treatment. In this article we present a new method, called CHARM, for automated segmentation of fifteen different head tissues from magnetic resonance (MR) scans. The new method compares favorably to two freely available software tools on a five-tissue segmentation task, while obtaining reasonable segmentation accuracy over all fifteen tissues. The method automatically adapts to variability in the input scans and can thus be directly applied to clinical or research scans acquired with different scanners, sequences or settings. We show that an increase in automated segmentation accuracy results in a lower relative error in electric field simulations when compared to anatomical head models constructed from reference segmentations. However, also the improved segmentations and, by implication, the electric field simulations are affected by systematic artifacts in the input MR scans. As long as the artifacts are unaccounted for, this can lead to local simulation differences up to 30% of the peak field strength on reference simulations. Finally, we exemplarily demonstrate the effect of including all fifteen tissue classes in the field simulations against the standard approach of using only five tissue classes and show that for specific stimulation configurations the local differences can reach 10% of the peak field strength.

Project description:BackgroundCartilage morphometry based on magnetic resonance images (MRIs) is an emerging outcome measure for clinical trials among patients with knee osteoarthritis (KOA). However, current methods for cartilage morphometry take many hours per knee and require extensive training on the use of the associated software. In this study we tested the feasibility, reliability, and construct validity of a novel osteoarthritis cartilage damage quantification method (Cartilage Damage Index [CDI]) that utilizes informative locations on knee MRIs.MethodsWe selected 102 knee MRIs from the Osteoarthritis Initiative that represented a range of KOA structural severity (Kellgren Lawrence [KL] Grade 0 - 4). We tested the intra- and inter-tester reliability of the CDI and compared the CDI scores against different measures of severity (radiographic joint space narrowing [JSN] grade, KL score, joint space width [JSW]) and static knee alignment, both cross-sectionally and longitudinally.ResultsDetermination of the CDI took on average14.4 minutes (s.d. 2.1) per knee pair (baseline and follow-up of one knee). Repeatability was good (intra- and inter-tester reliability: intraclass correlation coefficient >0.86). The mean CDI scores related to all four measures of osteoarthritis severity (JSN grade, KL score, JSW, and knee alignment; all p values < 0.05). Baseline JSN grade and knee alignment also predicted subsequent 24-month longitudinal change in the CDI (p trends <0.05). During 24 months, knees with worsening in JSN or KL grade (i.e. progressors) had greater change in CDI score.ConclusionsThe CDI is a novel knee cartilage quantification method that is rapid, reliable, and has construct validity for assessment of medial tibiofemoral osteoarthritis structural severity and its progression. It has the potential to addresses the barriers inherent to studies requiring assessment of cartilage damage on large numbers of knees, and as a biomarker for knee osteoarthritis progression.

Dataset Information

On the use of coupled shape priors for segmentation of magnetic resonance images of the knee.

Publications

On the use of coupled shape priors for segmentation of magnetic resonance images of the knee.

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