Atlas based 3D liver segmentation using adaptive thresholding and superpixel approaches
Negar Farzaneh, Samuel Habbo-Gavin, S. M. Reza Soroushmehr, Hirenkumar Patel, David Paul Fessell, Kevin R. Ward, Kayvan Najarian
Abstract
Traumas and illnesses can cause injury in internal organs. The liver, being the largest abdominal organ, is most likely to be injured by trauma. Currently CT scans are analyzed by radiologists to see if there is any injuries in organs; however, due to the large amounts of data and its complexity in terms of noise, intensity variations in different images and so on, visual inspection would be time consuming and prone of error. Therefore, an automated approach would be beneficial. In this paper we propose a fully automated Bayesian based method for 3D segmentation of the liver. Experimental results show that the proposed method can achieve high performance with Dice and Jaccard similarity coefficients of 93:5% and 87:9% respectively.
BibTeX
@inproceedings{icassp2017_atlasbased3dlive,
title = {Atlas based 3D liver segmentation using adaptive thresholding and superpixel approaches},
author = {Negar Farzaneh and Samuel Habbo-Gavin and S. M. Reza Soroushmehr and Hirenkumar Patel and David Paul Fessell and Kevin R. Ward and Kayvan Najarian},
booktitle = {ICASSP 2017},
year = {2017}
}