3D kidney segmentation from abdominal diffusion MRI using an appearance-guided deformable boundary

PloS One
Mohamed ShehataAyman El-Baz

Abstract

A new technique for more accurate automatic segmentation of the kidney from its surrounding abdominal structures in diffusion-weighted magnetic resonance imaging (DW-MRI) is presented. This approach combines a new 3D probabilistic shape model of the kidney with a first-order appearance model and fourth-order spatial model of the diffusion-weighted signal intensity to guide the evolution of a 3D geometric deformable model. The probabilistic shape model was built from labeled training datasets to produce a spatially variant, independent random field of region labels. A Markov-Gibbs random field spatial model with up to fourth-order interactions was adequate to capture the inhomogeneity of renal tissues in the DW-MRI signal. A new analytical approach estimated the Gibbs potentials directly from the DW-MRI data to be segmented, in order that the segmentation procedure would be fully automatic. Finally, to better distinguish the kidney object from the surrounding tissues, marginal gray level distributions inside and outside of the deformable boundary were modeled with adaptive linear combinations of discrete Gaussians (first-order appearance model). The approach was tested on a cohort of 64 DW-MRI datasets with b-values ranging from...Continue Reading

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Citations

Dec 25, 2019·Scientific Reports·Joāo Dos Santos PeriquitoAndreas Pohlmann
Jun 23, 2020·Journal of Healthcare Engineering·Ines NjehAhmed BenHamida
Dec 3, 2020·Scientific Reports·Taro LangnerJoel Kullberg
Feb 1, 2021·Medical Image Analysis·Israa AlnazerChristine Fernandez-Maloigne

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BETA
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