Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT.

Journal Article (Journal Article)

The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four abdominal organs from 4D CT data using graph cuts. Contrast-enhanced CT scans were obtained at two phases: non-contrast and portal venous. Intra-patient data were spatially normalized by non-linear registration. Then 4D convolution using population training information of contrast-enhanced liver, spleen and kidneys was applied to multiphase data to initialize the 4D graph and adapt to patient-specific data. CT enhancement information and constraints on shape, from Parzen windows, and location, from a probabilistic atlas, were input into a new formulation of a 4D graph. Comparative results demonstrate the effects of appearance, enhancement, shape and location on organ segmentation. All four abdominal organs were segmented robustly and accurately with volume overlaps over 93.6% and average surface distances below 1.1mm.

Full Text

Duke Authors

Cited Authors

  • Linguraru, MG; Pura, JA; Pamulapati, V; Summers, RM

Published Date

  • May 2012

Published In

Volume / Issue

  • 16 / 4

Start / End Page

  • 904 - 914

PubMed ID

  • 22377657

Pubmed Central ID

  • PMC3322299

Electronic International Standard Serial Number (EISSN)

  • 1361-8423

International Standard Serial Number (ISSN)

  • 1361-8415

Digital Object Identifier (DOI)

  • 10.1016/


  • eng