Paper Info
Title
Computerized classification of liver disease in MRI using an artificial neural network
Abstract
We developed a software named "LiverANN" based on an artificial neural network (ANN) technique for distinguishing the pathologies of focal liver lesions in magnetic resonance (MR) imaging, which helps radiologists integrate the imaging findings with different pulse sequences and raise the diagnostic accuracy even with radiologists inexperienced in liver MR imaging. In each patient, regions of focal liver lesion on Tl-weighted, T2-weighted, and gadolinium-enhanced dynamic MIR images obtained in the hepatic arterial and equilibrium phases were placed by a radiologist (M.K.), then the program automatically calculated the brightness and homogeneity into numerical data within the selected areas as the input signals to the ANN. The outputs from the ANN were the 5 categories of focal hepatic diseases: liver cyst, cavernous hemangioma, dysplasia, hepatocellular carcinoma, and metastasis. Fifty cases were used for training the ANN, while 30 cases for testing the performance. The result showed that the LiverANN classified 5 types of focal liver lesions with sensitivity of 93%, which demonstrated the ability of ANN to fuse the complex relationships among the imaging findings with different sequences, and the ANN-based software may provide radiologists with referential opinion during the radiologic diagnostic procedure.
Year
DOI
Venue
2001
10.1117/12.431062
Proceedings of SPIE
Keywords
Field
DocType
NR imaging,focal liver disease,differentiation,artificial neural network,computer-aided diagnosis (CAD)
Hepatic Diseases,Liver disease,Dysplasia,Liver cancer,Hemangioma,Radiology,Cyst,Artificial neural network,Medicine,Magnetic resonance imaging
Conference
Volume
ISSN
Citations 
4322
0277-786X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xuejun Zhang17016.55
Masayuki Kanematsu29017.09
Hiroshi Fujita38619.92
Takeshi Hara463979.10
Hiroaki Hoshi510618.21