13 March 2014 A novel non-registration based segmentation approach of 4D dynamic upper airway MR images: minimally interactive fuzzy connectedness
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Abstract
There are several disease conditions that lead to upper airway restrictive disorders. In the study of these conditions, it is important to take into account the dynamic nature of the upper airway. Currently, dynamic MRI is the modality of choice for studying these diseases. Unfortunately, the contrast resolution obtainable in the images poses many challenges for an effective segmentation of the upper airway structures. No viable methods have been developed to date to solve this problem. In this paper, we demonstrate the adaptation of the iterative relative fuzzy connectedness (IRFC) algorithm for this application as a potential practical tool. After preprocessing to correct for background image non-uniformities and the non-standardness of MRI intensities, seeds are specified for the airway and its crucial background tissue components in only the 3D image corresponding to the first time instance of the 4D volume. Subsequently the process runs without human interaction and completes segmenting the whole 4D volume in 10 sec. Our evaluations indicate that the segmentations are of very good quality achieving true positive and false positive volume fractions and boundary distance with respect to reference manual segmentations of about 93%, 0.1%, and 0.5 mm, respectively.
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Yubing Tong, Yubing Tong, Jayaram K. Udupa, Jayaram K. Udupa, Dewey Odhner, Dewey Odhner, Sanghun Sin, Sanghun Sin, Mark E. Wagshul, Mark E. Wagshul, Raanan Arens, Raanan Arens, } "A novel non-registration based segmentation approach of 4D dynamic upper airway MR images: minimally interactive fuzzy connectedness", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 90380Z (13 March 2014); doi: 10.1117/12.2044473; https://doi.org/10.1117/12.2044473
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