Paper Info
Title
Liver segmental anatomy and analysis from vessel and tumor segmentation via optimized graph cuts
Abstract
The segmentation and classification of the major intra-hepatic blood vessels along with the segmentation and analysis of hepatic tumors are critical for patient specific models of the diseased liver. Additionally, the accurate identification of liver anatomical segments can assist in the clinical assessment of the risks and benefits of hepatic interventions. We propose a novel 4D graph-based method to segment hepatic vasculature and tumors. The algorithm uses multi-phase CT images to model the differential enhancement of the liver structures and Hessian-based shape likelihoods to avoid the common pitfalls of graph cuts with undersegmentation and intensity heterogeneity. A hybrid classification step based on post-order walks of a graph identifies the right, middle and left hepatic, and portal veins. Veins are tracked using the graph representation and planes fitted to the vessel segments. The method allows the detection of all hepatic tumors and identification of the liver segments with 87.8% accuracy.
Year
DOI
Venue
2011
10.1007/978-3-642-28557-8_24
Abdominal imaging
Keywords
Field
DocType
graph representation,hepatic tumor,liver structure,liver anatomical segment,tumor segmentation,liver segmental anatomy,left hepatic,optimized graph cut,segment hepatic vasculature,graph cut,diseased liver,hepatic intervention,liver segment,vein
Cut,Anatomy,Computer science,Vein,Hessian matrix,Tumor segmentation,Artificial intelligence,Hepatic vasculature,Computer vision,Graph,Pattern recognition,Segmentation,Radiology,Graph (abstract data type)
Conference
Citations 
PageRank 
References 
3
0.41
13
Authors
4
Name
Order
Citations
PageRank
Vivek Pamulapati1814.16
Aradhana Venkatesan230.41
Bradford J Wood314231.69
Marius George Linguraru436248.94