A Multiatlas Segmentation Using Graph Cuts with Applications to Liver Segmentation in CT Scans

Carlos Platero,M Carmen Tobar

doi:10.1155/2014/182909

Abstract

An atlas-based segmentation approach is presented that combines low-level operations, an affine probabilistic atlas, and a multiatlas-based segmentation. The proposed combination provides highly accurate segmentation due to registrations and atlas selections based on the regions of interest (ROIs) and coarse segmentations. Our approach shares the following common elements between the probabilistic atlas and multiatlas segmentation: (a) the spatial normalisation and (b) the segmentation method, which is based on minimising a discrete energy function using graph cuts. The method is evaluated for the segmentation of the liver in computed tomography (CT) images. Low-level operations define a ROI around the liver from an abdominal CT. We generate a probabilistic atlas using an affine registration based on geometry moments from manually labelled data. Next, a coarse segmentation of the liver is obtained from the probabilistic atlas with low computational effort. Then, a multiatlas segmentation approach improves the accuracy of the segmentation. Both the atlas selections and the nonrigid registrations of the multiatlas approach use a binary mask defined by coarse segmentation. We experimentally demonstrate that this approach performs better than atlas selections and nonrigid registrations in the entire ROI. The segmentation results are comparable to those obtained by human experts and to other recently published results.

Highlights

Segmentation of 3D computed tomography (CT) images of the liver is generally the first step in computer-assisted diagnosis and surgery systems for liver diseases
An atlas-based segmentation framework is proposed that combines low-level operations and a fast probabilistic atlas with multiatlas segmentation
The proposed combination provides high accuracy in segmentation due to registrations and atlas selection based on regions of interest (ROIs) and coarse segmentations

Summary

Introduction

Segmentation of 3D CT images of the liver is generally the first step in computer-assisted diagnosis and surgery systems for liver diseases. The first group is based on grey-level intensities, such as thresholding, clustering, or region growing Their major drawback is the adjacent organ separations (e.g., stomach, kidney, and heart), which may have intensities similar to that of the liver. Regiongrowing approaches leak into surrounding tissue and require subsequent manual corrections [3, 4] For such images, the intensity alone is insufficient for obtaining a robust segmentation. The statistical shape model is frequently used for liver segmentation because of its ability to constrain the segmentation to match shapes observed in a training database [5, 6] In many cases, these approaches do not make full use of the appearance. The level set-based variational approaches allow the incorporation of prior shapes into edge-based and regionbased models [7, 8]

Methods

Results

Conclusion