Automatic renal segmentation for MR urography using 3D-GrabCut and random forests.

ABSTRACT:

Purpose

To introduce and evaluate a fully automated renal segmentation technique for glomerular filtration rate (GFR) assessment in children.

Methods

An image segmentation method based on iterative graph cuts (GrabCut) was modified to work on time-resolved 3D dynamic contrast-enhanced MRI data sets. A random forest classifier was trained to further segment the renal tissue into cortex, medulla, and the collecting system. The algorithm was tested on 26 subjects and the segmentation results were compared to the manually drawn segmentation maps using the F1-score metric. A two-compartment model was used to estimate the GFR of each subject using both automatically and manually generated segmentation maps.

Results

Segmentation maps generated automatically showed high simil

SUBMITTER: Yoruk U 

PROVIDER: S-EPMC5745323 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature