A Low-Interaction Automatic 3D Liver Segmentation Method Using Computed Tomography for Selective Internal Radiation Therapy

Authors

Mohammed Goryawala, Department of Electrical and Computer Engineering, Florida International UniversityFollow
Seza Gulec, Herbert Wertheim College of Medicine, Florida International UniversityFollow
Ruchir Bhatt, Biomedical Engineering Department, Florida International University
Anthony J. McGoron, Department of Department of Biomedical Engineering, Florida International UniversityFollow
Malek Adjouadi, Department of Electrical and Computer Engineering, Florida International UniversityFollow

Date of this Version

7-3-2014

Document Type

Article

Abstract

This study introduces a novel liver segmentation approach for estimating anatomic liver volumes towards selective internal radiation treatment (SIRT). The algorithm requires minimal human interaction since the initialization process to segment the entire liver in 3D relied on a single computed tomography (CT) slice.The algorithm integrates a localized contouring algorithm with a modified k-means method. The modified k-means segments each slice into five distinct regions belonging to different structures. The liver region is further segmented using localized contouring.The novelty of the algorithm is in the design of the initialization masks for region contouring to minimize human intervention. Intensity based region growing together with novel volume of interest (VOI) based corrections is used to accomplish the single slice initialization. The performance of the algorithm is evaluated using 34 liver CT scans. Statistical experiments were performed to determine consistency of segmentation and to assess user dependency on the initialization process. Volume estimations are compared to the manual gold standard. Results show an average accuracy of 97.22% for volumetric calculation with an average Dice coefficient of 0.92. Statistical tests show that the algorithm is highly consistent (� = 0.55) and independent of user initialization (� = 0.20 and Fleiss’ Kappa = 0.77 ± 0.06).

Comments

Originally published in BioMed Research International.

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 License.

Recommended Citation

Mohammed Goryawala, Seza Gulec, Ruchir Bhatt, Anthony J. McGoron, and Malek Adjouadi, “A Low-Interaction Automatic 3D Liver Segmentation Method Using Computed Tomography for Selective Internal Radiation Therapy,” BioMed Research International, vol. 2014, Article ID 198015, 12 pages, 2014. doi:10.1155/2014/198015