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 50 to 1000 s/mm2. The performance of the presented approach was evaluated using leave-one-subject-out cross validation and compared against three other well-known segmentation methods applied to the same DW-MRI data using the following evaluation metrics: 1) the Dice similarity coefficient (DSC); 2) the 95-percentile modified Hausdorff distance (MHD); and 3) the percentage kidney volume difference (PKVD). High performance of the new approach was confirmed by the high DSC (0.95±0.01), low MHD (3.9±0.76) mm, and low PKVD (9.5±2.2)% relative to manual segmentation by an MR expert (a board certified radiologist).
Highlights
The prevalence of chronic kidney disease (CKD) in the U.S (2017) was about 15% of the adult population and 662,000 were of end-stage kidney disease (ESKD) [1]
The segmentation accuracy was evaluated by two volumetric measures, namely, the Dice similarity coefficient (DSC) [52] and percentage kidney volume difference (PKVD), and one distance-based metric —the 95-percentile modified Hausdorff distance (MHD) [53], which characterize the spatial overlap and distribution of the surface to surface distances between the segmented and ground truth kidneys, respectively
This paper presented a segmentation technique is very promising to provide the guidance for the level-set geometric deformable model to accurately segment kidneys from the other abdomen structures and surrounding tissues using (3D + b-value) diffusionweighted magnetic resonance imaging (DW-MRI) data
Summary
The prevalence of chronic kidney disease (CKD) in the U.S (2017) was about 15% of the adult population and 662,000 were of end-stage kidney disease (ESKD) [1]. Clinicians are in a bad need of a fast, accurate, and reliable diagnostic tool to early detect AR for a higher chance of rescuing the transplanted kidney [3], [4] An imaging modality, such as diffusionweighted magnetic resonance imaging (DW-MRI) with the ability to provide both anatomical and functional information, expose patients to no radiation or contrast agent like other imaging modalities (e.g., computed topography (CT) and dynamic MRI), is critically needed to develop a computer-aided diagnostic (CAD) system for the early detection of AR posttransplantation [5]. DW-MRI allows mapping of water molecules’ diffusion process in biological tissues, in-vivo and non-invasively These water molecule diffusion patterns are quantified by apparent diffusion coefficients (ADCs) and reveal details about tissue (e.g., kidney) status, either normal or diseased. We will review some of these studies that used different segmentation methods (e.g., threshold, region growing and graph cuts, and evolving deformable boundary) and different imaging modalities (e.g., CT and dynamic MRI)
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