3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models.

Fahmi Khalifa,Ahmed Soliman,Adel Elmaghraby,Georgy Gimel’Farb,Ayman El-Baz

doi:10.1155/2017/9818506

Abstract

Kidney segmentation is an essential step in developing any noninvasive computer-assisted diagnostic system for renal function assessment. This paper introduces an automated framework for 3D kidney segmentation from dynamic computed tomography (CT) images that integrates discriminative features from the current and prior CT appearances into a random forest classification approach. To account for CT images' inhomogeneities, we employ discriminate features that are extracted from a higher-order spatial model and an adaptive shape model in addition to the first-order CT appearance. To model the interactions between CT data voxels, we employed a higher-order spatial model, which adds the triple and quad clique families to the traditional pairwise clique family. The kidney shape prior model is built using a set of training CT data and is updated during segmentation using not only region labels but also voxels' appearances in neighboring spatial voxel locations. Our framework performance has been evaluated on in vivo dynamic CT data collected from 20 subjects and comprises multiple 3D scans acquired before and after contrast medium administration. Quantitative evaluation between manually and automatically segmented kidney contours using Dice similarity, percentage volume differences, and 95th-percentile bidirectional Hausdorff distances confirms the high accuracy of our approach.

Highlights

Kidney segmentation from dynamic contrast-enhanced computed tomography (CT) is of immense importance for any computer-assisted diagnosis of renal function assessment, pathological tissue localization, radiotherapy planning, and so forth [1]
Most of the energy-based methods use regional and boundary information that may not exist in some images and may not achieve globally optimal results. To account for these limitations, we developed a 3D kidney segmentation framework that integrates, in addition to the current CT appearance features, higher-order appearance models and adaptive shape model features into a random forests (RF) classification model [33]
It is worth mentioning that, in addition to our methodology presentation in [33], this paper provides (i) a more comprehensive review of the related literature work on the abdominal CT images segmentation (Section 3); (ii) detailed description of the metrics that are used for segmentation evaluation of our and compared techniques (Section 3); and (iii) expansion of the experimental results by adding an essential metric that is used to evaluate the robustness of segmentation techniques, namely, the receiver operating characteristics (ROC) (Section 4)

Summary

Introduction

Kidney segmentation from dynamic contrast-enhanced computed tomography (CT) is of immense importance for any computer-assisted diagnosis of renal function assessment, pathological tissue localization, radiotherapy planning, and so forth [1]. A stochastic level set-based framework by Khalifa et al [16, 17] integrated probabilistic kidney shapes and image signals priors into Markov random field (MRF) for abdominal 3D CT kidney segmentation. Chu et al [29] presented an automated MAPbased multiorgan segmentation method that incorporated image-space division and multiscale weighting scheme Their framework is based on a spatially divided probabilistic atlases and the segmentation is performed using a graph cut method. Most of the energy-based methods (e.g., graph-cut) use regional and boundary information that may not exist in some (e.g., precontrast) images and may not achieve globally optimal results To account for these limitations, we developed a 3D kidney segmentation framework that integrates, in addition to the current CT appearance features, higher-order appearance models and adaptive shape model features into a random forests (RF) classification model [33]. It is worth mentioning that, in addition to our methodology presentation in [33], this paper provides (i) a more comprehensive review of the related literature work on the abdominal CT images segmentation (Section 3); (ii) detailed description of the metrics that are used for segmentation evaluation of our and compared techniques (Section 3); and (iii) expansion of the experimental results by adding an essential metric that is used to evaluate the robustness of segmentation techniques, namely, the receiver operating characteristics (ROC) (Section 4)

Background

Segmentation Evaluation Metrics

Experimental Results

Conclusions