Paper Info
Title
Automatic classification of focal liver lesions based on clinical DCE-MR and T2-weighted images: A feasibility study
Abstract
Focal liver lesion classification is an important part of diagnostics. In clinical practice, T2-weighted (T2W) and dynamic contrast enhanced (DCE) MR images are used to determine the type of lesion. For automatic liver lesion classification only T2W images are exploited. In this feasibility study, a multi-modal approach for automatic lesion classification of five lesion classes (adenoma, cyst, haemangioma, HCC, and metastasis) is studied. Features are derived from four sets: (A) non-corrected, and (B) motion corrected DCE-MRI, (C) T2W images, and (D) B+C combined, originating from 43 patients. An extremely randomized forest is used as classifier. The results show that motion corrected DCE-MRI features are a valuable addition to the T2W features, and improve the accuracy in discriminating benign and malignant lesions, as well as the classification of the five lesion classes. The multimodal approach shows promising results for an automatic liver lesion classification.
Year
DOI
Venue
2018
10.1109/ISBI.2018.8363565
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Keywords
Field
DocType
Liver,classification,DCE-MRI
Metastasis,Liver lesion,Lesion,Pattern recognition,Computer science,Clinical Practice,Multimodal therapy,Adenoma,Artificial intelligence,Radiology,Cyst
Conference
ISSN
ISBN
Citations 
1945-7928
978-1-5386-3637-4
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Mariëlle J. A. Jansen101.01
Hugo J. Kuijf2467.89
Josien P. W. Pluim32439178.67