28 March 2013 Assessment of methods to extract the mid-sagittal plane from brain MR images
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Abstract
Automatic detection of the mid-sagittal plane, separating both hemispheres of the brain, is useful in various applications. Several methods have been developed in the past years, applying different techniques to estimate the position of the mid-sagittal plane. These methods can be classified into three distinct classes: feature-based, global symmetry based, and local symmetry based methods. Feature-based methods use the shape or intensity of the interhemispheric fissure to extract the mid-sagittal plane. Global symmetry based methods reflect the entire image with respect to the sagittal axes and perform a rigid registration. Local symmetry based methods try to optimize a symmetry-measure in a small band covering the interhemispheric fissure. From each class, one leading method has been implemented. The methods have been evaluated on the same datasets to allow a fair comparison. Manual delineations were made by two experienced human observers. The results show that the examined methods perform similar to human observers. No significant differences were found between errors (defined as the angle and volume between planes) made by the methods and the inter-observer differences. Feature-based and local symmetry based methods have a low computation time of 1.8 and 0.5 seconds, respectively. The global symmetry based method has a higher computation time of 33.6 seconds, caused by the full 3D rigid registration. The largest errors, both by the methods and observers, are made in participants with cerebral atrophy. These participants have a widened interhemispheric fissure, allowing many plane orientations and positions to result in a valid division of the hemispheres.
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Hugo J. Kuijf, Hugo J. Kuijf, Alexander Leemans, Alexander Leemans, Max A. Viergever, Max A. Viergever, Koen L. Vincken, Koen L. Vincken, } "Assessment of methods to extract the mid-sagittal plane from brain MR images", Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 86731K (28 March 2013); doi: 10.1117/12.2006858; https://doi.org/10.1117/12.2006858
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